customer performance measurement

Transcrição

UNIVERSITY OF FRIBOURG SWITZERLAND
UNIVERSITÄT FREIBURG SCHWEIZ
DEPARTMENT OF INFORMATICS
SEMINAR FOR MARKETING AND COMMUNICATION
DEPARTEMENT FÜR INFORMATIK
SEMINAR FÜR MARKETING & UNTERNEHMENSKOMMUNIKATION
Master Thesis
Presented to the Faculty of Economics,
University of Fribourg, Switzerland
In Partial Fulfilment of the Requirements for the Degree of
Master of Arts in Management
CUSTOMER PERFORMANCE
MEASUREMENT
Analysis of the Benefit of a Fuzzy Classification
Approach in Customer Relationship Management
Author:
Darius Zumstein
Address:
Route du Champ-des-Fontaines 24
1700 Fribourg (Switzerland)
E-Mail:
dzumstein(at)gmx.ch
Homepage:
www.dzumstein.ch
Mobile:
+41 (0)78 870 95 55
Place of origin: Burgdorf/Seeberg BE
Matriculation #: 00-201-327
1st Reader:
2nd Reader:
Prof. Dr. Maurizio Vanetti
Prof. Dr. Andreas Meier
Tutor:
Nicolas Werro
Date:
Fribourg, 7th of March 2007
Abstract
Customers are the most valuable asset of a company. As a result, customers have to be classified, analysed, evaluated, segmented and managed according to their value for the company
using appropriate tools and methods of Customer Relationship Management (CRM).
This master thesis proposes fuzzy classification as a multidimensional data analysis and management method suitable for realising these CRM processes and for establishing profitable
customer relationships. In contrast to other data mining and statistical methods, fuzzy classification and fCQL (fuzzy Classification Query Language) allow the classification of customers
into more than one class at the same time.
The application of the fuzzy classification approach to widely used management tools like the
SWOT, portfolio and ABC analysis and to scoring models enables a better and fairer classification, segmentation and management of customers. So far, these methods have mostly been
applied uncritically with sharp classes, although sharp segmentation can obviously be very
arbitrary, imprecise, unfair and discriminatory and may have negative effects.
The application of the fuzzy portfolio analysis within the scope of performance measurement is
especially suited to classifying, analysing, evaluating and improving important monetary customer performance indicators, like turnover, contribution margins, profit and customer equity,
and non-monetary indicators, such as customer value, satisfaction, loyalty and retention.
Surprisingly, little research has been done on Customer Performance Measurement (CPM)
and customer performance indicators despite the increasing theoretical and practical importance of CRM. This work discusses a holistic customer performance measurement framework
with 170+ Customer Performance Indicators (CPIs) and relevant Key Customer Performance
Indicators (KCPIs). To avoid misclassifications, to improve the quality of customer evaluations
and to exploit customer potential, it is suggested to classify all indicators fuzzily.
Customer performance indicators for revenue and profitability, and customer investment, relationship, recommendation, information and cooperation indicators allow to segment customers
precisely, to optimise fuzzy classified customer portfolios, to drive the proposed CRM success
chain and to define customer strategies in order to increase corporate profits and growth.
Key words:
Fuzzy classification, fuzzy Classification Query Language (fCQL), Customer Relationship
Management (CRM), analytical CRM (aCRM), customer performance measurement,
customer performance indicators, fuzzy customer segmentation, management tools,
fuzzy portfolio analysis, fuzzy credit rating.
-I-
Kurzfassung
Kunden sind die wertvollste Ressource eines Unternehmens. Deshalb müssen Kunden durch
geeignete Instrumente des Customer Relationship Managements (CRM) entsprechend ihrem
Wert für das Unternehmen analysiert, klassifiziert, beurteilt, segmentiert und behandelt werden. Diese Arbeit schlägt unscharfe Klassifikation als eine Analyse- und Managementmethode
vor, um solche CRM-Prozesse umzusetzen und profitable Kundenbeziehungen aufzubauen.
Die unscharfe Klassifikation und fCQL (fuzzy Classification Query Language) verbinden fuzzy
logic mit relationalen Datenbanken und erlauben im Gegensatz zu anderen Data Mining und
statistischen Methoden, dass Kunden mehreren Klassen gleichzeitig angehören können.
Wird der Ansatz der unscharfen Klassifikation auf weit verbreitete Managementinstrumente
wie etwa auf die Portfolio-, SWOT-, ABC-Analyse oder Scoring-Modelle angewendet, können
Kunden besser und fair klassifiziert, segmentiert und gehandhabt werden. Bis anhin wurden
diese Methoden der Kundensegmentierung unkritisch mit trennscharfen Klassen durchgeführt,
obwohl scharfe Segmentierung offensichtlich sehr willkürlich, ungenau und diskriminierend
sein kann, und womöglich negative Auswirkungen nach sich zieht.
Unscharfe Klassifikationen und unscharfe Portfolioanalysen können gerade im Rahmen des
Performance Measurement nutzbringend eingesetzt werden, um monetäre Kundenkennzahlen (z.B. Umsätze, Deckungsbeiträge, Gewinne, Kundenwert) und nicht-monetäre Kennzahlen
(Kundennutzen, -zufriedenheit, -loyalität oder -bindung) zu beurteilen und zu verbessern.
Trotz der grossen theoretischen und praktischen Bedeutung des CRMs gibt es erstaunlicherweise wenig Literatur zu Kundenkennzahlen und zu Kundenkennzahlensystemen. Deshalb
diskutiert diese Arbeit ein Customer Performance Measurement (CPM) Framework mit 170+
Customer Performance Indicators (CPIs) und zentralen Key Customer Performance Indicators
(KCPIs), und empfiehlt diese unscharf zu klassifizieren and zu bewerten. Kennzahlen über die
Kundenprofitabilität, -investitionen und die Kundenbeziehung, sowie über das Weiterempfehlungs-, Informations- und Kooperationsverhalten von Kunden, erlauben den Verantwortlichen,
unscharf klassifizierte Kundenportfolios zu optimieren, sowie an den wichtigen und richtigen
Stellen der vorgeschlagenen CRM-Erfolgskette die Hebel anzusetzen und Kundenstrategien
umzusetzen, um damit dem Unternehmen zu höherem Gewinn und Wachstum zu verhelfen.
Stichworte:
Unscharfe Klassifikation, fuzzy Classification Query Language (fCQL), Customer Relationship
Management (CRM), analytisches CRM, Kundenkennzahlensystem, Kundenkennzahlen,
unscharfe Kundensegmentierung, Management Tools, unscharfe Portfolioanalyse,
unscharfe Kreditwürdigkeitsprüfung.
- II -
Contents
Abstract .................................................................................................................................... I
Contents ................................................................................................................................ III
List of Figures.........................................................................................................................V
List of Tables .....................................................................................................................VIII
List of Abbreviations........................................................................................................... IX
Acknowledgement ............................................................................................................... XI
CHAPTER 1: INTRODUCTION .............................................................................. 1
1.1
1.2
1.3
1.4
Motivation ............................................................................................................... 2
Problem Statement ................................................................................................. 3
Objectives ............................................................................................................... 4
Outline of the Thesis............................................................................................... 5
CHAPTER 2: FUZZY CLASSIFICATION .................................................................7
2.1
The Approach of Fuzzy Classification .................................................................... 8
2.1.1
2.1.2
2.1.3
2.1.4
2.1.5
2.2
Classification as a Database Schema Extension...................................................8
Fuzzy Classification with Linguistic Variables ......................................................10
Aggregation Operator...........................................................................................11
Multidimensional Fuzzy Classification..................................................................13
Dynamic Fuzzy Classification...............................................................................16
Fuzzy Classification Query Language (fCQL) ...................................................... 17
2.2.1
2.2.2
2.2.3
2.2.4
Introduction...........................................................................................................17
Fuzzy Classification Query Examples..................................................................17
Architecture of the fCQL Toolkit ...........................................................................18
Advantages of Fuzzy Classification and fCQL .....................................................20
CHAPTER 3: FUZZY CLASSIFICATION MANAGEMENT TOOLS ........................... 22
3.1
Potential Business Applications for Fuzzy Classification...................................... 23
3.1.1
3.1.2
3.2
Fuzzy Portfolio Analysis........................................................................................ 25
3.2.1
3.2.2
3.2.3
3.3
Definition ..............................................................................................................31
Sharp Classification and Disadvantages..............................................................31
Fuzzy Classification and Advantages ..................................................................32
Fuzzy ABC Analysis ............................................................................................. 35
3.4.1
3.4.2
3.4.3
3.5
Definition ..............................................................................................................25
Fuzzy SWOT Analysis .......................................................................................... 31
3.3.1
3.3.2
3.3.3
3.4
Overview ..............................................................................................................23
Existing Literature on Marketing and Fuzzy Classification...................................24
Definition ..............................................................................................................35
Fuzzy Scoring Methods ........................................................................................ 39
3.5.1
3.5.2
3.5.3
Definition ..............................................................................................................39
- III -
Contents
CHAPTER 4: ANALYTICAL CUSTOMER RELATIONSHIP MANAGEMENT ............. 44
4.1
Customer Relationship Management (CRM)........................................................ 45
4.1.1
4.1.2
4.1.3
4.1.4
4.1.5
4.2
Customer Performance Measurement.................................................................. 54
4.2.1
4.2.2
4.3
Overview ..............................................................................................................45
The Development to the Customer Oriented Company.......................................46
CRM and Customer Management .......................................................................47
Definition of CRM .................................................................................................49
Objectives and Key Points of CRM ......................................................................51
Definitions.............................................................................................................54
Processes of Customer Performance Measurement ...........................................55
Customer Performance Indicators ........................................................................ 57
4.3.1
4.3.2
4.3.3
Definitions.............................................................................................................57
Categories of Customer Performance Indicators.................................................58
Customer Performance Indicators in Business Practice......................................61
CHAPTER 5: FUZZY CUSTOMER SEGMENTATION ............................................. 62
5.1
Fuzzy Customer Segmentation with Important Indicators .................................... 63
5.1.1
5.1.2
5.1.3
5.1.4
5.1.5
5.1.6
5.1.7
5.1.8
5.1.9
5.1.10
5.1.11
5.1.12
5.1.13
5.1.14
5.1.15
5.1.16
5.2
Definitions.............................................................................................................63
Fuzzy Clustering...................................................................................................64
Methods of Customer Segmentation....................................................................65
Selected Indicators for Fuzzy Customer Segmentation.......................................69
Customer Orientation ...........................................................................................70
Customer Value....................................................................................................71
Customer Satisfaction ..........................................................................................72
Customer Loyalty .................................................................................................73
Customer Retention .............................................................................................75
Repurchases ........................................................................................................78
Add-on Selling ......................................................................................................78
Share of Wallet.....................................................................................................79
Turnover ...............................................................................................................81
Contribution Margins ............................................................................................82
Profitability............................................................................................................84
Customer Equity and Customer Lifetime Value (CLV).........................................86
Fuzzy Market Segmentation ................................................................................. 91
CHAPTER 6: FUZZY CREDIT RATING ................................................................ 94
6.1
Methods of Sharp Credit Rating ........................................................................... 95
6.1.1
6.1.2
6.1.3
6.1.4
6.2
Definitions.............................................................................................................95
Subjective Expertise.............................................................................................95
Statistical Methods ...............................................................................................96
Disadvantages of Sharp Credit Rating.................................................................98
Methods of Fuzzy Credit Rating ........................................................................... 99
6.2.1
6.2.2
6.2.3
Existing Literature on Fuzzy Credit Rating...........................................................99
Fuzzy Credit Rating with fCQL...........................................................................101
Other Applications for Fuzzy Classification in Banking......................................106
CHAPTER 7: CONCLUSION ............................................................................. 107
7.1
7.2
7.3
Summary ............................................................................................................ 108
Critical Remarks ................................................................................................. 115
Outlook ............................................................................................................... 117
References and Further Reading ..................................................................................... 118
Appendix............................................................................................................................. 133
Statement............................................................................................................................. 144
- IV -
List of Figures
Figure 1: Application of Fuzzy Classification to Popular Management Tools .......................... 2
Figure 2: Theoretical Classification of the Master Thesis ........................................................ 5
Figure 3: Structure of the Master Thesis ................................................................................. 6
Figure 4: Structure of Chapter 2: Fuzzy Classification ............................................................ 8
Figure 5: Classification Space defined by Customer Attractiveness & Competitive Position .. 9
Figure 6: Concept of Linguistic Variables .............................................................................. 10
Figure 7: Fuzzy Classification with Membership Functions ................................................... 11
Figure 8: t-Norms, t-Conorms and Averaging Operator......................................................... 12
Figure 9: Three-Dimensional Sharp (a) and Fuzzy (b) Classification .................................... 14
Figure 10: Example of Hierarchical Multidimensional Fuzzy Classification ............................. 15
Figure 11: Dynamic Fuzzy Classification and Implementation of a Trigger Mechanism.......... 16
Figure 12: Architecture of the fQCL Toolkit ............................................................................. 18
Figure 13: Screenshots of the fCQL Toolkit Query Panel........................................................ 19
Figure 14: Examples of Tasks and Methods of Data Mining ................................................... 21
Figure 15: Fuzzy Classification as a Promising Management Tool for Different Fields........... 23
Figure 16: The Boston Consulting Group Matrix (a) and Norm Strategies (b)......................... 26
Figure 17: Sharp (a) and Fuzzy (b) BCG Portfolio................................................................... 26
Figure 18: Sharp (a) and Fuzzy (b) Investments ..................................................................... 28
Figure 19: Balancing of Fuzzy Classified Portfolios................................................................. 29
Figure 20: Sharp (a) and Fuzzy Classified (b) McKinsey/General Electrics Portfolio .............. 30
Figure 21: Sharp (a) and Fuzzy (b) SWOT Matrix ................................................................... 31
Figure 22: Fuzzy Strength (a), Weakness (b), Opportunity (c) and Threat (d) Matrices.......... 32
Figure 23: Sharp (a) and Fuzzy (b) Risk Matrix ....................................................................... 34
Figure 24: Fuzzy ABC Analysis ............................................................................................... 36
Figure 25: Fuzzy ABC Analysis with Different Customer Performance Indicators................... 37
Figure 26: Combination of the Fuzzy Portfolio and ABC Analysis ........................................... 38
Figure 27: Sharp (a) and Fuzzy (b) RFM Method .................................................................... 41
Figure 28: Fuzzy RFM Incentives ............................................................................................ 42
Figure 29: Structure of Chapter 4 and 5 .................................................................................. 45
Figure 30: The Development to the Customer-Oriented Company ......................................... 46
Figure 31: Applications of Fuzzy Classification in the Domain of Customer Management...... 47
Figure 32: Fuzzy Classification and Individual Marketing ........................................................ 48
Figure 33: The Use of Fuzzy Classification in Typical Tasks of CRM ..................................... 49
-V-
List of Figures
Figure 34: CRM Application Architecture................................................................................. 50
Figure 35: Mobile Analytical Customer Relationship Management ......................................... 51
Figure 36: CRM Success Chain .............................................................................................. 52
Figure 37: Dimensions of Customer Performance Measurement............................................ 54
Figure 38: Processes of Customer Performance Measurement.............................................. 55
Figure 39: Measurement Dimensions of the CPIP ‘Customer Profit’ ....................................... 58
Figure 40: Measurement Dimensions of the CRI ‘Customer Loyalty’ ...................................... 59
Figure 41: Measurement Dimensions of the CReI ‘Number of Customer Recommendations’ 59
Figure 42: CRM Success Chain with 170+ Customer Performance Indicators ....................... 60
Figure 43: Empirical Results of Customer Performance Measurement in Companies............ 61
Figure 44: Fuzzy Methods of Cluster Analysis ........................................................................ 64
Figure 45: Sharp (a) and Fuzzy (b) Customer Segments ........................................................ 65
Figure 46: Methods of Customer Segmentation ...................................................................... 66
Figure 47: Information Dashboard of Relevant Customer Data............................................... 67
Figure 48: Context of Fuzzy Customer Segmentation............................................................. 68
Figure 49: Indicators of the CRM Success Chain for Fuzzy Customer Segmentation ............ 69
Figure 50: Driving the CRM Success Chain by Optimising Fuzzy Classified Portfolios .......... 69
Figure 51: Fuzzy Cost-Benefit Analysis (a) and Portfolio of Customer Orientation (b)............ 70
Figure 52: Examples of Fuzzy Classified Customer Satisfaction Portfolios............................. 72
Figure 53: Loyalty Ladder ........................................................................................................ 73
Figure 54: Examples of Fuzzy Classified Customer Loyalty Portfolios.................................... 74
Figure 55: Determinants of Customer Retention ..................................................................... 75
Figure 56: Controlling Level and Indicators of Customer Retention ........................................ 76
Figure 57: Fuzzy Classified Portfolios of Customer Retention Indicators ................................ 77
Figure 58: Examples of Fuzzy Classified Repurchase Portfolios ............................................ 78
Figure 59: Examples of Fuzzy Classified Add-on Selling Portfolios ........................................ 79
Figure 60: Crisp (a) and Fuzzy (b) Choice............................................................................... 80
Figure 61: Examples of Fuzzy Classified Share of Wallet Portfolios ....................................... 80
Figure 62: Examples of Fuzzy Classified Turnover Portfolios ................................................. 81
Figure 63: Customer Contribution Margin Accounting ............................................................. 82
Figure 64: Fuzzy Classified Customer Contribution Margins Portfolios................................... 83
Figure 65: Fuzzy Classification of Customer Profitability......................................................... 84
Figure 66: Customer Growth Strategies .................................................................................. 85
Figure 67: Examples of Fuzzy Classified Customer Equity Portfolios ..................................... 86
Figure 68: Fuzzy Classified Customer Satisfaction/Equity Portfolio ........................................ 87
Figure 69: Three-Dimensional Fuzzy Classification of Customer Equity ................................. 89
Figure 70: Fuzzy Classified Customer (a) and Prospect (b) Lifetime Value Portfolios ............ 89
- VI -
List of Figures
Figure 71: Sharp (a) and Fuzzy Classified (b) Customer Equity Pyramid ............................... 90
Figure 72: Sharp Market Segmentation................................................................................... 91
Figure 73: Basic Market-Preferences Patterns ........................................................................ 92
Figure 74: Fuzzy Market Segmentation of Income and Age.................................................... 92
Figure 75: Fuzzy Market Segments and Strategies................................................................. 93
Figure 76: Discriminant Function and Type I and II Errors ...................................................... 96
Figure 77: Architecture of a Neural Network for Credit Rating................................................. 97
Figure 78: Discriminant Functions in Discriminant Analysis and ANN..................................... 97
Figure 79: Hierarchy of Creditworthiness with Weights δ and Parameters γ........................... 99
Figure 80: Credit Rating Hierarchy with the Degree of Importance gi of each Criterion ........ 100
Figure 81: fCQL as a Method of Artificial Intelligence............................................................ 101
Figure 82: Practice-Related Example of a Hierarchy of Creditworthiness ............................. 102
Figure 83: Examples of a Qualitative and a Quantitative Attribute of Fuzzy Credit Scoring .. 102
Figure 84: Hierarchical Fuzzy Classification of Creditworthiness .......................................... 103
Figure 85: Thee-Dimensional Sharp (a) and Fuzzy (b) Credit Rating.................................... 105
Figure 86: Promising Management Tools, Methods and Concepts for Fuzzy Classification . 109
Figure 87: Fuzzy Classified Customer Portfolio (a) and Fuzzy ABC Analysis (b) .................. 110
Figure 88: Tools and Indicators for Customer Performance Measurement ........................... 113
Figure 89: The Main Challenges of Marketing Controlling in Practice ................................... 116
- VII -
List of Tables
Table 1:
Research Questions and Objectives ........................................................................ 4
Table 2:
Selected Indicators for Customer Attractiveness and Competitive Position ............. 9
Table 3:
Absolute and Normalised Membership Degress of Customer Smith...................... 12
Table 4:
Membership Degress of the Customers ................................................................. 14
Table 5:
Basic Scheme of SQL and fCQL ............................................................................ 17
Table 6:
Criteria for Assessing Industry Attractiveness and Competitive Strength............... 30
Table 7:
Sharp ABC Analysis ............................................................................................... 35
Table 8:
Fuzzy ABC Analysis ............................................................................................... 36
Table 9:
Example of the RFM Method with Sharp Classes .................................................. 39
Table 10: RFM Method: Definition of Classes and Scores ..................................................... 40
Table 11: Sharp RFM Scoring of Customers.......................................................................... 41
Table 12: Fuzzy RFM Scoring of Customers.......................................................................... 42
Table 13: Mass vs. One-to-One Marketing and Applications for Fuzzy Classification ........... 48
Table 14: Drivers of Customer Value and Satisfaction ........................................................... 71
Table 15: Determinants and Indicators of Customer Equity ................................................... 88
Table 16: Interest Rates for Different Loan Categories ........................................................ 105
Table 17: Sharp Classification of the Loan Applicants ......................................................... 105
Table 18: Results of Research Question (RQ) 1 .................................................................. 109
Table 19: Results of Research Question 2........................................................................... 111
- VIII -
List of Abbreviations
#
aCRM
AI
ANN
BCG
BE
BP
BPR
BSC
C
CAS
CCI
CCO
CII
CInfI
CIM
CLV
CP
CPI
CPM
CPMS
CPIP
CR
CRA
CRC
CReI
CRI
CRM
CRO
Cu.
DB
DBMS
DWH(S)
e
EDGE
EDI
Ed(s).
EGPRS
ERP
fc
FCM
fCMT
Number
analytical Customer Relationship Management
Artificial Intelligence
Artificial Neural Networks
Boston Consulting Group
Balance Error
Balanced Portfolio
Business Process Re-Engineering
Balanced Scorecard
Class
Computer Aided Selling
Customer Cooperation Indicator
Chief Customer Officer
Customer Investment Indicator
Customer Information Indicator
Computer Integrated Manufacturing
Customer Lifetime Value
Customer Performance
Customer Performance Indicator
Customer Performance Measurement
Customer Performance Measurement System
Customer Performance Indicator for Revenue and Profitability
Customer Relation
Customer Relationship Analytics
Customer Relationship Communication
Customer Recommendation Indicator
Customer Relationship Indicator
Customer Relationship Management
Customer Relationship Operations
Customer
Database
Database Management System
Data Warehouse (System)
electronic
Enhanced Data rates for → GSM Evolution
Electronic Data Interface
Editor(s)
Enhanced GPRS (→ GPRS plus → EDGE)
Enterprise Ressource Planning
fuzzy classification
fuzzy-C-Means (algorithm)
fuzzy Classification Management Tools
- IX -
List of Abbreviations
fCQL
FMLE
GPRS
GSM
HSDPA
I
ICT
IM
IS
IT
KAM
KDD
KCPI
KPI
KSF
L
MD
MIS
MOA
No.
OLAP
p(p).
PDA
PM
PMS
R&D
RDBMS
RFM
ROC(I)
ROI
ROM(I)
ROQ
ROR
ROS
RQ
SCM
SFA
SME
SWOT
sc
SBF
SBU
SQL
TQM
UMTS
Vol.
WLAN
fuzzy Classification Query Language
Fuzzy-Maximum-Likelihood-Estimation (algorithm)
General Packet Radio Service
Global System for Mobile Communication
High Speed Downlink Packed Access
Indicator
Information and Communication Technology
Information Management
Information System
Information Technology
Key Account Management
Knowledge Discovery in Databases
Key Customer Performance Indicators
Key Performance Indicators
Key Success Factor
Level
Membership Degrees
Management Information System
Market Opportunity Analysis
Number
On-Line Analytical Processing
page(s)
Personal Digital Assistant
Performance Measurement
Performance Measurement System
Research & Development
Relational Database Management System
Recency, Frequency, Monetary value
Return on Customer (Investment)
Return on Investment
Return on Marketing (Investment)
Return on Quality
Return on Relationship
Return on Sales
Research Question
Supply Chain Management
Sales Force Automation
Small and Medium Enterprises
Strengths, Weaknesses, Opportunities, Threats
sharp classification
Strategic Business Field
Strategic Business Units
Structured Query Language
Total Quality Management
Universal Mobile Telecommunications System
Volume
Wireless Local Area Network
-X-
Acknowledgement
Firstly, I thank Nicolas Werro for asking me to write about fuzzy classification and for the excellent assistance. He gave me the opportunity for a very interesting and exciting trip into
new worlds.
In addition, I want to thank Prof. Dr. Andreas Meier and Prof. Dr. Maurizio Vanetti that this
interdisciplinary project could be realised.
I am particularly grateful to my parents, Gabriela, Beatus and Jürg, for supporting me in all
the years. They made possible, what was and is so important to me.
I am also thankful for all the interesting discussions and the good advice of Florian Schramm
and Martin Zöller, and for the corrections of Tau Kevin Musa.
This thesis is dedicated to Ela, who supported and loved me so much in the last three years
– the best ones of my life.
- XI -
Chapter 1
Introduction
-1-
Chapter 1: Introduction
1.1 Motivation
Since Zadeh first published the article “Fuzzy Sets” in the Journal “Information and Control” in
1965, much scientific research has been done in the field of fuzzy control over all the years.
In basic research, many publications have been written on fuzzy logic, fuzzy sets or on fuzzy
classification, on different mathematical definitions of the fuzzy classification approach, on its
implementations in information systems and on diverse applications in the field of engineering.
In fact, fuzzy logic and fuzzy classification is well known in many rather technical disciplines
like electronics or engineering, but also in mathematics, statistics, informatics and data mining.
In contrast, in marketing and business management fuzzy classification is still largely unknown and rarely used, in both theory and practice. This gap motivated to write this master
thesis about the potential and benefit of fuzzy classification in business activities.
Consequently, following initial questions raised, which will be discussed in this work in detail
and summarised in Chapter 7: there exists an approach of fuzzy classification and the fuzzy
Classification Query Language (fCQL). However,
‘where’ can fuzzy classification be used in business management?
‘How’ could fuzzy classification be used?
‘What for’ can fuzzy classification be used?
‘Why’ should fuzzy classification be used?
First of all, one management field, which seems to be promising for fuzzy classification, is
Customer Relationship Management (CRM).
Most researchers and managers have recognised that customers are the most valuable and
Average overall satisfaction with a tool
scarcest assets of a company. As a result, CRM has become very important in the last years.
4.2
4.1
4.0
3.9
3.8
3.7
Core competencies
Strategic alliances
Strategic planning
Growth strategies (customer acquisition)
Customer
Supply chain management
segmentation
Scenario and contingency planning
Benchmarking
TQM
Offshoring
CRM
Economical value-added analysis
Outsourcing
Six sigma
Mission and vision statement
Price optimization models Balanced scorecard
Business process reengineering
Activity-based
Change management
management
Open market innovation
Knowledge management
Mass customisation
Loyality management
3.6
Promising management tools and fields for fuzzy classification
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Usage of tool (in how many of the asked companies a management tool was used)
Scale of satisfaction: 1/2: extremely/somewhat dissatisfied; 3: neither dis-/ nor satisfied; 4/5: somewhat/extremely satisfied; Source data: [Bain & Company 2005, p.13]
Figure 1: Application of Fuzzy Classification to Popular Management Tools
-2-
An international study of [Bain & Company 2005], shown in Figure 1, confirms that CRM was
applied in 75% of all asked companies and is the second most important management tool in
business practice, behind strategic planning (79%) and before customer segmentation (72%)
and performance measurement (with the balanced scorecard: 57%). Obviously, most companies were somewhat or very satisfied with the tools. In addition, the management tools seem
to be promising for the fuzzy classification approach, as reasoned in the problem statement.
1.2 Problem Statement
This thesis will discuss several research questions and problems in the following domains,
‘where’ fuzzy classification can be used:
Fuzzy classification has not often been applied to CRM, although it seems to be suited to
improve CRM. As a result, a first research question has to be answered: ‘where’, in which
CRM fields and processes, ‘how’ and ‘why’ could fuzzy classification improve CRM?
To analyse and control marketing or CRM, management needs adequate methods, instrument or tools to evaluate customers. However, what are widely used management tools
and methods in business practice suited for fuzzy classification? The thesis will explain,
‘how’ and ‘why’ fuzzy classification can be applied to such management tools.
To manage customers according to their importance for the firm, CRM and marketing need
a measurement system to analyse and evaluate the performance of customers. Defining
another research question and creating a new term: what is the benefit of fuzzy classification in customer performance measurement? Although many authors of literature on
CRM, marketing, accounting and information management emphasise the importance of
measuring customer relationships and customer performance, there exist surprisingly few
reviews and little literature about the measurement of customer performance.
To measure customer performance, CRM requires indicators. However, what kind of indicators does CRM need? What are important customer performance indicators for customer
performance measurement? The thesis will work out a concept, ‘how’ customer performance indicators could be applied, and ‘why’ and ‘what for’ they are relevant.
Customer segmentation, an important task of analytical CRM and data mining, seems to
be particularly interesting for fuzzy classification (‘what for’), because it is evidently dangerous to label and classify customers sharply just as "good" (profitable) or "bad" (unprofitable).
However, this work argues, ‘how’ and ‘why’ fuzzy classification can be used for an exact,
fair and enhanced customer segmentation.
With a specific problem of customer segmentation, each loan officer of a bank is confronted:
when is a loan applicant creditworthy, and when is he not? Answering this last research
question, it will be shown, ‘how’ and ‘why’ fuzzy classification can be used for credit rating.
-3-
1.3 Objectives
This thesis tries to answer seven Research Questions (RQ) and their objectives, mentioned
in the problem statement and summarised in Table 1. Answering the research questions and
discussing all the points on the right hand side of Table 1, new theoretical insights should be
gained about the benefit of the application of the fuzzy classification approach in customer
relationship management. The main aim of this master thesis is to show the possibilities for an
improved evaluation and management of customers using fuzzy classification.
Table 1: Research Questions and Objectives
#
Research Questions (RQ): Objectives: analysis, discussion and evaluation of the …
1
What are potential fields
applications of fuzzy classification:
and topics for business
overview of different promising marketing fields and concepts for
applications of fuzzy classification
applications for fuzzy
classification in marketing? overview of different potential fuzzy management tools and methods
… possible spectrum of marketing concepts and management tools for the
2
3
4
5
6
7
discussion of existing literature on marketing and fuzzy classification
… application of the fuzzy classification approach to different management
What are potential
tools and instruments of analysis and control:
management tools
definition and advantages of fuzzy portfolio analysis
definition and advantages of fuzzy SWOT analysis
and methods for fuzzy
definition and advantages of fuzzy ABC analysis
classification?
definition and advantages of fuzzy scoring methods and RFM method
… application of fuzzy classification to fields, tasks, processes and instruWhat are potential fields,
ments of marketing and Customer Relationship Management (CRM):
processes and instruments discussion of promising marketing concepts for fuzzy classification,
promising fields for fuzzy classification in customer management/CRM
for fuzzy classification in
definition, processes, architecture, objectives and key points of CRM
CRM?
definition of a small CRM success chain
… application of the fuzzy classification approach to customer performance
What are the benefits
measurement and management:
of fuzzy classification in
definition of the term customer performance
definition of the term customer performance measurement
customer performance
characteristics and processes of customer performance measurement
measurement?
customer performance measurement in business practice
… customer performance indicators and key customer performance
What are important
indicators for holistic customer performance measurement:
customer performance
definition of the term customer performance indicator
collection, discussion and categorisation of a comprehensive number of
indicators for customer
performance measurement? customer performance indicators
mapping of customer performance indicators to a big CRM success chain
… fuzzy segmentation of customers into fuzzy customer segments using
important customer performance indicators:
How can customers be
definition of the term fuzzy customer segmentation
methods and context of fuzzy customer evaluation and segmentation
segmented fuzzily?
fuzzy customer segmentation with 12 Key Customer Performance Indicators (KCPIs) of the CRM success chain using fuzzy portfolio analysis
… fuzzy credit rating approach for the evaluation of the creditworthiness of
private loan applicants:
What are the benefits of
discussion and disadvantages of methods of sharp credit rating
the fuzzy classification
discussion and advantages of methods of fuzzy credit rating
approach in credit rating? example of a hierarchical fuzzy classification for credit scoring
calculation of personalised interest rates using fuzzy classification
The following outline shows, how the thesis is structured and organised in order to work out a
clear concept by discussing the seven research questions.
-4-
1.4 Outline of the Thesis
The research questions indicate that the objects of research of this work are intersections of
different fields in information management: the fuzzy classification approach with the fuzzy
Classification Query Language (fCQL) is actually a topic of computer science and information
technology. However, this thesis discusses different business applications for fuzzy classification and is therefore an approach of business management. The main issues of the thesis,
customer performance measurement and customer segmentation (research questions 4 to 6),
belong both to marketing and managerial accounting. In addition, research question 7, the
task of credit rating, can be assigned to finance (compare Figure 2).
Marketing
Fuzzy
classification
Objects of
research
Customer Relationship Management (CRM)
Customer performance measurement
Customer segmentation
Credit rating
Accounting
Finance
Information
management
Figure 2: Theoretical Classification of the Master Thesis
The master thesis has the following structure:
The introducing Chapter 2 will summarise the essential ideas, logic, concepts and the
model of fuzzy classification and the fuzzy classification Query Language (fCQL).
Chapter 3 discusses different potential fields for fuzzy classification (RQ 1) and different
fuzzy classification management tools (RQ 2): fuzzy portfolio analysis, fuzzy SWOT
analysis, fuzzy ABC analysis and fuzzy scoring methods.
Chapter 4 deals with the applications of fuzzy classification within analytical CRM (RQ 3),
with the conception of customer performance measurement (RQ 4), and with customer
performance indicators (RQ 5).
In Chapter 5, the developed concept of customer performance measurement and different
important indicators are applied to fuzzy customer segmentation (RQ 6).
Methods of sharp and fuzzy credit rating (RQ 7) are discussed in Chapter 6.
In Chapter 7, conclusion, all findings are summarised. Critical remarks and an outlook on
further research questions about business applications for fuzzy classification will round off
the thesis.
-5-
Figure 3 shows the structure of the thesis, including the seven Research Questions (RQ).
1. Introduction
1.1 Motivation
1.2 Problem Statement
1.3 Objectives
1.4 Outline
2. Fuzzy Classification
2.1 The Approach of Fuzzy Classification
2.2 Fuzzy Classification Query Language (fCQL)
3. Fuzzy Classification Management Tools
3.3 Fuzzy SWOT Analysis
RQ 2
3.4 Fuzzy ABC Analysis
3.5 Fuzzy Scoring Methods
RQ 1
Fuzzy classification
and advantages
3.2 Fuzzy Portfolio Analysis
Sharp classification
and disadvantages
3.1 Potential Business Applications for Fuzzy Classification
4. Analytical Customer Relationship Management
4.1 Customer Relationship Management (CRM)
RQ 3
4.2 Customer Performance Measurement
RQ 4
4.3 Customer Performance Indicators
RQ 5
5. Fuzzy Customer Segmentation
5.1 Fuzzy Customer Segmentation with Important Indicators
RQ 6
5.2 Fuzzy Market Segmentation
6. Fuzzy Credit Rating
6.1 Methods of Sharp Credit Rating
6.2 Methods of Fuzzy Credit Rating
7. Conclusion
Figure 3: Structure of the Master Thesis
-6-
RQ 7
Chapter 2
Fuzzy Classification
-7-
Chapter 2: Fuzzy Classification
2.1
The Approach of Fuzzy Classification
2.1.1 Classification as a Database Schema Extension
This technical chapter recapitulates the main findings and research on fuzzy classification of
the Information System Research Group at the Department of Informatics at the University
of Fribourg (Switzerland). This chapter is based on the research of [Schindler 1998a, Meier et
al. 2001, 2003, 2005, Werro 2005, Werro et al. 2005a/b, 2006].
However, the concepts are explained using a new example of fuzzy classification, a customer
attractiveness/competitive position portfolio. Figure 4 shows the structure of the chapter.
Aggregation Operator
Multidimensional Fuzzy Classification
Dynamic Fuzzy Classification
Fuzzy Classification Query Examples
Architecture of the fCQL Toolkit
Advantages of Fuzzy Classification and fCQL
Linguistic variables, verbal terms,
membership degree, continuous and
discrete member functions;
Aggregation Operator, compensatory,
t-norms, t-conorms
Multidimensional fuzzy classification,
hierarchical fuzzy classification,
decomposition principle;
Fuzzy classification over time,
monitoring, trigger mechanism;
fCQL, relational database,
fuzzy queries;
SQL, fCQL/SQL basic scheme,
fCQL syntax;
fCQL toolkit, fCQL tool’s architecture,
fCQL interpreter;
Improved classification, reduction of
complexity, extraction of hidden data,
no migration, easy to use, etc.
[Zimmermann 1992], [Zimmermann and Zysno 1980]
Schindler 1998a]
[Meier et al. 2005a/b]
[Werro et al. 2005]
[ Werro et al. 2006]
Fuzzy Classification with Linguistic Variables
Sharp classification and
fuzzy classification;
[Chen 1998]
[Shenoi 1995]
[Meier 2003a]
Sharp vs. Fuzzy classification
References
Attribute, domain, context, database
scheme, equivalence class;
Classification as a Database Schema Extension
Fuzzy Classification Query Language (fCQL)
Section 2.2 fCQL
Keywords
New example: customer attractiveness/competitive position portfolio
Section 2.1: Approach of Fuzzy Classification
Subsections
Figure 4: Structure of Chapter 2: Fuzzy Classification
To define classes in the relational database schema, a context model proposed by [Chen
1998] was extended [Meier et al. 2001]: To every attribute Aj, defined by a domain D(Aj), a
context K(Aj) is added. A context K(Aj) is a partition of D(Aj) into equivalence classes. A relational database schema with contexts R(A, K) is then the set A = (A1,…, An) of attributes with
associated contexts K = (K1(Aj),…,Kn(An)) [Shenoi 1995].
-8-
In this chapter, the following example of CRM with two attributes is considered: customers
who buy the company’s products can be evaluated by ‘customer attractiveness’ (attribute A1),
which is measured by an index, and ‘competitive position’ (A2). ‘Customer attractiveness’ and
‘competitive position’ can be specified by one or several indicators (combined and weighted by
a scoring model or decomposed by a hierarchical classification) shown in Table 2. For explanations, details and more customer indicators see Appendix 4 (pages 136ff).
Table 2: Selected Indicators for Customer Attractiveness and Competitive Position
Customer attractiveness (A1)
Competitive position (A2)
Customer turnover or sales (I 31-I 34)
Customer contribution margins I – IV (I 70-I 74)
Customer gross/net profit (I 75,I 76), profitability (I 78)
Customer equity (I 79), Customer Lifetime Value (I 80)
Growth of turnover (I 35) or profit (I 77); potential (I 83)
Probability of repurchases (I 57), price sensitivity (I 60)
Customer’s product mix at the company/competitor (I 58)
Punctuality of payment (I 63), creditworthiness (I 68)
Number of recommendations (I 152), cooperation (I 171)
Customer penetration (I 38)
Share of wallet (I 39-I 41)
Market share of customer (I 42)
Cross- and up-selling (I 50- I 54)
Duration of customer relationship (I 147)
Satisfaction (I 126), commitment (I 131)
Customer loyalty (I 134), retention (I 137)
Image of company (I 115), products (I 116)
Competitive advantages
The pertinent contexts K(Aj) of the two qualifying attributes can be defined as follows:
Customer attractiveness (A1): The attribute domain D(A1) of the index ‘customer attractiveness’ is defined by [0, 100] and is divided into the two equivalence classes [0, 49] for
‘unpromising’ customer attractiveness and [50, 100] for ‘promising’ attractiveness.
Competitive position (A2): The domain D(A2) is {very bad, bad, insufficient, sufficient,
good, excellent} and has two equivalence classes: {very bad, bad, insufficient} for a ‘weak’
competitive position, and {sufficient, good, excellent} for a ‘strong’ one.
The definition of the equivalence classes of the two attributes ‘customer attractiveness’ and
‘competitive position’ determines a two-dimensional classification space shown in Figure 5.
The four resulting classes C1 to C4 could be characterised as ‘star customers’ (C1) with the
strategy to maintain, as ‘development customers’ (C2) in which has to be invested, ‘absorption
customers’ (C3) are to skim, and in ‘renunciation customers’ (C4) a firm should not invest.
Customer attractiveness
100
C2)
50
49
C1)
Development customers
(to invest)
Star customers
(to maintain)
C4)
C3)
Renunciation customers
Absorption customers
(to skim)
(not to invest)
0
very bad
bad
insufficient
sufficient
good
excellent
Competitive
position
Figure 5: Classification Space defined by Customer Attractiveness and Competitive Position
-9-
This portfolio, originally described by [Link and Hildebrand 1993] and discussed by [Homburg
and Krohmer 2006], is adapted from the market growth/market share portfolio of the Boston
Consulting Group (with the classes C1: ‘stars’, C2: ‘question marks’, C3: ‘cash cows’ and C4:
‘poor dogs’) and from the industry attractiveness/competitive strength portfolio of McKinsey
and General Electrics (see Section 3.2 Fuzzy Portfolio Analysis).
2.1.2 Fuzzy Classification with Linguistic Variables
In this section, the model will be extended by defining a fuzzy classification database scheme
in order to determine the customer’s membership degrees to the different classes.
To derive fuzzy classes from sharp contexts, the attributes are considered as linguistic variables, and verbal terms are assigned to each equivalence class [Zimmermann 1992]. With the
help of linguistic variables, that means words or word combinations, the equivalence classes
of the attributes can be described more intuitively. In the example, the linguistic variable ‘customer attractiveness’ is described by the terms ‘unpromising’ and ‘promising’. The linguistic
variable, ‘competitive position’, is divided into the terms ‘weak’ and ‘strong’ (see Figure 6).
unpromising
0
promising
49 50
[0,..., 49]
Competitive position
100
[50,..., 100]
Equivalence class Equivalence class
weak
very bad
Linguistic variable
(attribute)
strong
bad insufficient sufficient
good
Term
excellent
[very bad, bad, insufficient] [sufficient, good, excellent]
Domain
Context
Equivalence class Equivalence class
Figure 6: Concept of Linguistic Variables
Every term of the linguistic variable represents a fuzzy set. Each fuzzy set is determined by a
membership function (μ) over the whole domain of the corresponding attribute. The attribute
‘customer attractiveness’ contains numerical values in the interval [0,100]. Consequently, the
membership functions μunpromising and μpromising are continuous functions. In contrast, the attribute
‘competitive position’ does not hold numerical values, but general terms. In this case, the
terms of the linguistic variable ‘weak’ and ‘strong’ are associated to discrete functions, i. e.
each term corresponds to a discrete value. Using the context model, linguistic variables and
membership functions, the classification space becomes fuzzy. This fuzzy partition has an
important outcome, it implies the disappearance of the classes’ sharp borders, that means
there are continuous transitions between the different classes [Werro et al. 2006].
- 10 -
μ unpromising
22.9% C2:
Development
customer
0
1
0.65 0.35
0
Sharply classified
customer turnover
of the last year
Fuzzily classified
customer turnover
of the last year
very bad
0
0.2
0.4
0.6
0.8
1
.
Smith
Brown
C3)
26.4% C3:
Absorption customer
Smith
Brown
C4)
Renunciation
customers
(not to invest)
. Ford
C3)
Absorption
customers
(to skim )
weak
strong
.
.
33.7% C4:
Renunciation customer
Ford
.
25.1% C3:
Absorption customer
C2)
Development
customers
(to invest)
D(Competitive position)
49 50
C4)
31.5% C1:
Star customer
C1) .
Star Miller
customers
(to maintain)
promising
Miller
24.5% C2:
Development customer
18.9%: C4
Renunciation customer
.
D(Customer attractiveness)
.
C1)
C2)
17% C1:
Star customer
100% C1:
Star customer
unpromising
100
μ promising
D(Customer attractiveness)
100% C4: Renunciation customer
bad
insufficient sufficient
good
μ weak
excellent
D(Competitive position)
μ strong
Figure 7: Fuzzy Classification with Membership Functions
In terms of colour: at the transitions of the classes, the four colours of the classes (in Figure 7),
light and dark green, and light and dark blue, become blurred, melt or flow together.
Classified fuzzily, a customer can belong to more than one class at the same time and his
membership degrees to the different classes can be calculated using an aggregation operator,
for instance the γ-operator discussed in the following subsection.
The sizes of the circles in Figure 7 are assumed to provide additional information about the
customer’s amount of turnover of the last year, since it is common in portfolio analysis to symbolise the importance of a business unit (here: of a customer) by the diameter of the circle.
However, the size of circle has nothing to do with the fuzzy classification of the customer itself.
2.1.3 Aggregation Operator
The membership degree M(Oi│Ck) of an object Oi (of a customer in the example) in class Ck
can be calculated by an aggregation over all terms of the linguistic variables that define the
class. Class C1 in the discussed example is described by the terms ‘promising’ and ‘strong’.
The membership grade of class C1 (star customers) is therefore a conjunction of the corresponding values of the membership functions μpromising and μstrong. Analogically, C2 is defined by
the membership functions μpromising and μweak, C3 (absorption customers) by μunpromising and
μstrong and C4 (renunciation customers) by μunpromising and μweak.
- 11 -
The used operator of the aggregation is the γ-operator, the “compensatory and”, which was
suggested and empirically tested by [Zimmermann and Zysno 1980, Zimmermann 1992]:
(1− γ)
⎛ m
⎞
μAi (x) = ⎜ ∏μi (x)⎟
⎝ i =1
⎠
γ
m
⎛
⎞
⎜1− ∏(1- μi (x))⎟ , x ∈ X, 0 ≤ γ ≤ 1
⎝ i =1
⎠
(γ-operator)
The γ-operator is composed by the algebraic product operator, a t-norm and its counterpart,
a t-conorm (compare Figure 8). The t-norms and t-conorms are non-compensatory operations, what means that there is no compensatory effect between the considered elements. In
contrast, the averaging operators have a compensation mechanism to reflect the reasoning
of humans, who intuitively weigh up elements (for details see [Werro 2007, p. 15]).
μ
A
Gamma = 0.5
μ
1
1
t-conorms
B
Gamma = 0.6
Gamma = 0
Gamma = 0.8
Gamma = 0.2
Gamma = 1
Gamma = 0.4
0.9
0.8
0.7
0.6
Averaging
operators
Mu
Averaging
operators
0.5
0.4
0.3
0.2
t-norms
M
o
0.1
x
0
Source: adapted from [Werro 2007, p. 15]
1
5
9
13
17
21
25
29
33
37
41
45
49
53
57
61
65
69
73
77
81
85
89
93
97 101
x
x
Figure 8: t-Norms, t-Conorms and Averaging Operator
In all examples of fuzzy classification in this thesis it is assumed that gamma is 0.5 (γ = 0.5;
(1− 0.5)
red curve in Figure 8), That means that the γ-operator is
⎛ m
⎞
μAi (x) = ⎜ ∏μi (x)⎟
⎝ i =1
⎠
m
⎛
⎞
⎜1 − ∏ (1- μi (x))⎟
i =1
⎝
⎠
0.5
.
The membership degrees of all classified objects Oi (customers) to each class Ck can be calculated be the γ-operator using the fCQL toolkit or the Microsoft® Excel file “GammaOperator”
in Appendix 1 (see p. 133). For customer Smith in Figure 7, for instance, the membership
degrees are calculated in Table 3. To receive the relative membership degrees Mnorm(Oi│Ck),
the absolute degrees M(Oi│Ck) have to be normalised, because the membership degree of
an object to a class depends on all other classes and therefore indirectly on the number of
classes k (see [Krishnapuram and Keller 1993, Schindler 1998a, p. 165]).
Table 3: Absolute and Normalised Membership Degress of Customer Smith
Corresponding membership
Class
functions of each class
(C)
Customer
attractiveness
Competitive
position
Membership degrees
of attributes
Customer
Competitive
attractiveness
position
C1
μpromising
μstrong
0.35
0.40
C2
μpromising
μweak
0.35
0.60
C3
μunpromising
μstrong
0.65
0.40
C4
μunpromising
μweak
0.65
0.60
Total
- 12 -
Absolute membership
degrees M(OSmith│Ck):
(1−0.5)
⎛m
⎞
μAi (x) = ⎜ ∏μi (x)⎟
⎝ i =1
⎠
0.5
m
⎛
⎞
⎜1− ∏(1- μi (x))⎟
⎝ i =1
⎠
(.35·.40).5 ·(1 - ((1-.35)·(1-.40))).5
= 0.37417 · 0.78102 = 0.29223
(.35·.60).5·(1 - ((1-.35)·(1-.60))).5
= 0.45826 · 0.86023 = 0.39421
(.65·.40).5·(1 - ((1-.65)·(1-.40))).5
= 0.50990 · 0.88882 = 0.45321
(.65·.60).5·(1 - ((1-.65)·(1-.60))).5
= 0.62450 · 0.92736 = 0.57914
1.71879
Normalised membership
degrees Mnorm(OSmith│Ck):
M(O i C k )
∑ (M(O C ))
k
i
k
n =1
0.29223 / 1.71879 = 0.17002
0.39421 / 1.71879 = 0.22935
0.45321 / 1.71879 = 0.26368
0.57914 / 1.71879 = 0.33695
1
Classified sharply (compare Figure 7), customer Smith belongs to one class only, 100% to
C4, and he is managed as a renunciation customer not to invest in, like customer Ford, who
has much lower values. This is arbitrary, not precise and not fair since Smith has nearly the
same values as Brown, who is classified in class C1 as a star customer to maintain. However,
Brown’s attractiveness and competitive position is not as high as the ‘real star customer’ Miller
ones and still should be improved. These two types of misclassifications are typical for sharp
classifications: elements with very similar values can be classified in total different classes,
and elements, which have very different values, may be classified in the same class. There
often exist high discrepancies between the classes and within a class.
With fuzzy classification, this does not happen and the misclassification problem is solved.
Classified fuzzily, Smith is no longer discriminated and belongs partly to all classes at same
time, as calculated in Table 3 and shown in Figure 7 (17% to C1, 22.9% to C2, 26.4% to C3
and 33.7% to C4). Brown also belongs simultaneously to four classes (31.5% to C1, 24.5% to
C2, 25.1% to C3, 18.9% to C4). With fuzzy classification both customer Smith and Brown are
not separated anymore and can be managed according to their real degree of ‘customer attractiveness’ and ‘competitive position’. However, Miller (C1) and Ford (C4) still belong to one
class only.
A class of the fuzzy classification can also be considered as a fuzzy customer segment. For
instance, all customers with absolute membership degrees between 0 and 1 to C1 ‘stars’ (in
Figure 7) can be considered as a fuzzy segment. Consequently, the fuzzy segment C1 consists of the Miller (with a absolute membership degree 100% to C1), Brown (54.4% to C1) and
Smith (29.2% to C1). From the management point of view, this raises important questions:
how much should be invested in Brown or Smith, who are positioned in the middle of the matrix and belong partially to different segments (i.e. classes) at the same time?
One answer could be that managers have to define customer strategies more specifically,
differentiated, and, most important, more individually and personally.
2.1.4 Multidimensional Fuzzy Classification
Fuzzy classification as a multidimensional analysis and classification method is not limited to
two dimensions. Several attributes or criteria can be considered at the same time. In Figure
9a, a third dimension, the future-oriented attribute ‘customer potential’, with the two terms ‘low
potential’ and ‘high potential’, is added to the discussed example. Classified sharply (Figure
9a), Brown and Miller belong to same class (C5), although their positions are very different.
Brown and Smith, however, are almost in the same position, but belong to different classes,
Brown to C5 and Smith to C3. Classified fuzzily (Figure 9b), the eight classes’ sharp borders
disappear and the three dimensional classification space becomes fuzzy.
- 13 -
b) Three-Dimensional Fuzzy Classification
a) Three-Dimensional Sharp Classification
C5) 100%
C1)
C2)
C3)
C4)
C5)
C6)
C7)
C8)
Customer
attractiveness
Smith
unpromising
high
potential
low
potential
weak
Miller
Competitive
position
strong
Brown
C5) 100% C5) 100%
Smith
Ford
C3) 100%
C4) 100%
C1)
C2)
C3)
C4)
C5)
C6)
C7)
C8)
Customer
Potential 0.56 0.44
0.57 0.43
μ unpromising
promising
μ promising
Customer
attractiveness
12.8%
9.6%
11.2%
8.3%
17.5%
13.4%
15.4%
11.7% Brown
14.9%
13.1%
17.3%
15.2%
9.7%
8.4%
11.5%
9.9%
Miller
Smith
Customer
Potential
μ high
potential
0.64
0.36
0.68
0.32
C3) 42.9%
C4) 57.1% Ford
1
0
0
1
0.36
μ low
potential
Competitive
position
0.44 0.38
0.64 0.56 0.62
μ weak
μ strong
Figure 9: Three-Dimensional Sharp (a) and Fuzzy (b) Classification
Brown and Smith belong now to eight different classes, with following absolute M(Oi│Ck) and
normalised Mnorm(Oi│Ck) membership degrees in Table 4. Calculation example for Smith to C1:
M(OSmith│C1) = (0.43·0.56·0.68)0.5 · ((1 - ((1-0.43)·(1-0.56)·(1-0.68)))0.5 = 0.40465·0.95903 = 0.38808
Mnorm(OSmith│C1) = 0.38808 / 2.60418 = 0.14902 = 14.9% (for details see Appendix 1).
Table 4: Membership Degress of the Customers
Class
C1
C2
C3
C4
C5
C6
C7
C8
functions to each class
μpromising
μpromising
μunpromising
μunpromising
μpromising
μpromising
μunpromising
μunpromising
μstrong
μweak
μstrong
μweak
μstrong
μweak
μstrong
μweak
μlow potential
μlow potential
μlow potential
μlow potential
μhigh potential
μhigh potential
μhigh potential
μhigh potential
Total
M(Oi│Ck)
Ford
0
0
0.6
0.8
0
0
0
0
1.4
Smith
Brown
0.38808
0.33987
0.45157
0.39674
0.25281
0.21772
0.29833
0.25906
2.60418
0.33409
0.25146
0.29126
0.21637
0.45698
0.35045
0.40151
0.30599
2.60811
Mnorm(Oi│Ck)
Miller
0
0
0
0
1
0
0
0
1
Ford
Smith
Brown
0
0
0.42857
0.57143
0
0
0
0
1
0.14902
0.13051
0.17340
0.15235
0.09708
0.08360
0.11456
0.09948
1
0.12810
0.09641
0.11167
0.08296
0.17521
0.13437
0.15395
0.11732
1
Miller
0
0
0
0
1
0
0
0
1
The example of the three-dimensional fuzzy classification in Figure 9b will now be extended to
a hierarchical fuzzy classification. The decomposition principle facilitates the definition
and the optimisation of the classification while maintaining a small number of classes with a
proper semantic even if many attributes are taken into account (see [Werro et al. 2006]).
The three attributes ‘customer attractiveness’, ‘competitive position’ and ‘customer potential’,
which define the ‘customer equity’ on the top level of the hierarchy, can be decomposed as
shown in Figure 10a. The attribute ‘customer attractiveness’, for instance, is defined by three
other attributes (in Figure 10c): ‘RFM score’, ‘profit’, and ‘payment history’. The attribute ‘RFM
score’ (described in detail in Section 3.5 and shown in Figure 10d) again, consists of the attributes ‘Recency’, ‘Frequency’ and ‘Monetary value’ of the customer’s repurchases.
- 14 -
a) Hierarchical Fuzzy Classification of Customers b) Fuzzy Classification of Customer Equity
Customer equity
Payment history
Loyalty
Duration or CR
Intention to switch
C1-1-6)
C1-1-2)
C1-1-8)
C1-1-7)
C1-1-3)
1
μ low
value
0
1
1
Frequency
0
μ rare
μ strong
μ weak
C1-6)
C1-2)
C1-8)
C1-4)
0
C1-5)
C1-1)
C1-7)
μ
C1-3)
good
μ
bad
Profit
0
1
μ frequent
d) Fuzzy Classification of RFM Score
Competitive
position
RFM
score
μ high
value
μ high
potential
C3)
0
0
1
C1-1-5)
μ long ago
C1-1-4)
1
Information
Cooperation
Recommendations
C1-1-1)
C7)
μ low
potential
Relationship
Retention
Punct. of payment
Method of payment
Outstanding bills
Recency
Down-selling pot.
Cross-selling pot.
Up-selling pot.
Products
Services
Relationship
C4)
μ high score
Turnover product
Turnover service
Transaction costs
μ recent
Add-on selling
Satisfaction with
C8)
μ low score
Profit
Growth of
Profits
Turnover
Cash flow
Customer
potential
Cu. penetration
Share of wallet
Cu. market share
C1)
Payment
history
Penetration
Recency
Frequency
Monetary value
C5)
C6)
C2)
μ unattractive
RFM score
μ attractive
Customer
potential
Competitive
position
Monetary
value
Customer
attractiveness
Customer
attractiveness
μ low profit
μ high profit
c) Fuzzy Classification of Customer Attractiveness
Figure 10: Example of Hierarchical Multidimensional Fuzzy Classification
In comparison to sharp classifications, for fuzzy classifications fewer terms (equivalence
classes) have to be defined in order to describe each attribute. The fuzzy classification of the
attribute ‘profit’, for instance, needs two terms only: ‘low profit’ and ‘high profit’. If turnover is
classified sharply, much more terms (equivalence classes) have to be defined: e.g. ‘very low’,
‘low’, ‘medium’, ‘high’ and ‘very high’ turnover. However, the lowest hierarchical level of the
fuzzy classification in Figure 10a still consists of 27 (3·9) attributes and 54 terms. To reduce
complexity and receive a clear semantic, all groups of three attributes at the bottom-level
(e.g. ‘Recency’, ‘Frequency’ and ‘Monetary value’) have to be aggregated (to the term ‘RFM
score’). The ‘RFM score’, in turn, ‘profits’ and ‘payment history’ express a higher semantic
(‘customer attractiveness’) on the next level. Depending on the information demand, customer
performance can be analysed on different levels of the hierarchy from a bottom-up or from a
top-down approach. The combination of this decomposition principle with a scoring model (see
Section 3.5) is a promising approach to develop a comprehensive customer evaluation model
based on any customer criteria, which are important for a company.
- 15 -
2.1.5 Dynamic Fuzzy Classification
Dynamically, fuzzy classification enables a precise monitoring of the positions and evolutions
of a customer, a customer segment or of all customers in a portfolio (see [Werro et al. 2006]).
By comparing different values over time, it can be controlled, if customer performance (in
Figure 11a) is increasing, steady or decreasing. This facilitates, on the one hand, a comparison of the target and actual business results of a single customer or segment, and, on the
other hand, to react quickly and to launch adequate counter measures, if the performance of a
customer, of a segment or of the whole portfolio is decreasing rapidly.
The identification of important leading-indicators of customer performance, such as the number of orders (I 17), order quote (I 19), repurchase intentions (I 55), cross-buying intentions (I
54), intention to recommend (I 151), customer (dis)satisfaction (I 126), or the intention to
switch (I 141), enables the implementation of a trigger mechanism (red-flag-function) which
warns at an early stage, if a good customer (Smith in Figure 11) shows a churning behaviour.
b)
Miller
2007(1) 2006(2) 2006(1)
2006(1)
μ unpromising
2006(2)
C4
2007(1)
Ford
X
2007(1)
2006(2)
2006(1)
C3
Trigger mechanism
for churn management
2006(1)
2006(2)
2007(1)
0
Turnover
in term (t)
0
1
Smith’s leading-indicators:
Intention to switch
satisfaction
Smith’s membership degrees
to the classes
C1, C2, C3, C4 1
Intention to repurchase/cross-buy
Number of orders
very low
1
Brown
Smith
Implementing a Trigger Mechanism
C1
maximum level
C2
very high
μ promising
minimum levels
a)
μ weak
0
2006(1)
μ strong
2006(2)
2007(1)
t
Figure 11: Dynamic Fuzzy Classification and Implementation of a Trigger Mechanism
In the fictive example of Figure 11b, Smith suddenly was very dissatisfied with a product or the
company itself, therefore his intention to switch highly increased and his intention to repurchase or cross-buy from the company decreased. Brown’s number of orders, and later his
turnover, also decreased, when he found an option or substitute elsewhere. As a result, his
membership degrees to the "good classes" (C1, C2, C3) steadily decreased from the first term
in 2006 to the first term in 2007, and the membership degree to C4 increased accordingly.
However, by modelling different leading- and lagging-indicators of customer performance, a
trigger mechanism warns, if the membership degrees to different classes fall below a minimum
(or exceed a maximum) level. To stop or weaken the negative trend, adequate CRM actions
can be launched in order to increase customer satisfaction, loyalty or retention again.
- 16 -
2.2
Fuzzy Classification Query Language (fCQL)
2.2.1 Introduction
The fuzzy Classification Query Language (fCQL) is a data analysis or a data mining tool,
which combines relational databases with fuzzy logic. This enables the use of numerical and
non-numerical values and allows the formulation of linguistic variables, which results in a more
human-oriented querying process. fCQL enables the formulation of unsharp queries on a linguistic level, so the user does not need to deal with a fuzzy SQL or with fuzzy predicates,
which could lead to varying semantics and different interpretations of the original data collection [Finnerty and Shenoi 1993]. As a result, the user can easily formulate classification queries as they are intuitive, i.e. the meaning of the queries is linguistically expressed.
2.2.2 Fuzzy Classification Query Examples
fCQL is an extension of the Structured Query Language (SQL), the international standard for
defining and query relational databases. Table 5 shows the comparison of the two languages.
Table 5: Basic Scheme of SQL and fCQL
SQL
Select
from
where
Attribute
Relation
Condition of Selection
fCQL
classify
from
with
Object
Relation
ClassificationCondition
Source: [Schindler 1998a]
As shown in Table 5, the select clause from the SQL schema is changed to classify, the
name of the object column to be fuzzily classified. The where clause turns to with, the
predicate for a classification. In the with clause, the user enters the predefined linguistic variables and their associated verbal terms. In combination with keywords, the user generates the
classification conditions.
After the definition of the fCQL syntax, following fuzzy classification query examples of the
discussed customer attractiveness/competitive position portfolio can be undertaken.
To query all customers of the customer portfolio, the fCQL query will be:
classify
from
customer
customer portfolio
In order to ask for new classes, a specific term of a linguistic variable can be queried.
For instance, the customer manager wants to know all unpromising customers of the customer
attractiveness/competitive position portfolio discussed in Subsection 2.1.2. He queries:
- 17 -
classify
from
with
customer
customer portfolio
customer attractiveness is unpromising
Now all membership degrees of the customer based on the function μunpromising are calculated.
If the user wants to know the promising and strong customers to invest, he queries:
classify
from
with
customer
customer portfolio
customer attractiveness is promising
and competitive position is strong
Now he receives the membership degrees of all customers with a membership degree based
on the functions μpromising and μstrong. The query returns the predefined class C1 with the semantic ‘star customers’. A simpler way to specify class C1 named ‘star customers’ is as follows:
classify
from
with
customer
customer portfolio
class is star customers
Particularly in complex databases, the utility of querying on linguistic variables becomes important. The ability of querying on linguistic variables can be considered as the slicing and
dicing operations on a fuzzy multidimensional classification space [Werro et al. 2005a].
2.2.3 Architecture of the fCQL Toolkit
In this subsection, the architecture and the implementation of the fuzzy Classification Query
Language (fCQL) will be resumed. By extending the relational database scheme, meta-tables
(n in Figure 12) are added to the Relational Database Management System (RDBMS; o).
p User or application
u Data architect
Case 2
Case 1
Case 3
v Graphical
interaction
fCQL
s queries
v Membership Function Editor
Definition and chart of discrete [a), b)]
or continuous, i.e. linear [c), d)] or sshaped [e), f)] membership functions.
a)
b)
1
fCQL
q toolkit
SQL
r queries
0
c)
Generated
t SQL
queries
d)
1
0
Server
e)
Raw data
n Meta-tables
f)
1
o Relational Database Management System (RDBMS)
0
Source: adapted from [Werro et al. 2006, p.3]
Figure 12: Architecture of the fQCL Toolkit
- 18 -
These meta-tables contain the definition of the linguistic variables and terms, the description of
the classes and all the meta-information regarding the membership functions. The architecture
of the fCQL toolkit illustrates the interactions between the user (p), the fCQL toolkit (q) and
the RDBMS. The fCQL toolkit is an additional layer above the relational database system
[Werro et al. 2005b]. This particularity makes fCQL independent from underlying database
systems based on SQL. Consequently, fCQL can operate with every RDBMS without migrating data and the user can always query the database also with standard SQL queries (r;
case 1 in Figure 12). In case 2, the user, or an application, can formulate fuzzy queries (s) to
the fCQL toolkit. The query panel of the fCQL toolkit allows analysing the data distribution
(1D), data space (2D) and the fuzzy classification (see screenshot in Figure 13).
Figure 13: Screenshots of the fCQL Toolkit Query Panel
The queries with fCQL are analysed and translated into corresponding SQL statements for the
RDBMS (t in Figure 12). In order to generate the fuzzy classification results, the toolkit accesses the raw data as well as the meta-data and computes the membership degrees of the
classified elements in the different classes. The classification results are then displayed to the
user or returned to the application. Before querying the fCQL toolkit, the data architect (u) has
to define the fuzzy classification (case 3). This primarily includes the definition and chart of
linear or s-shaped functions with the membership function editor (v).
In order to be easily accessible and platform independent, the fCQL toolkit has been implemented as a standalone Java application. Therefore, the user can install the toolkit on all
main platforms or operating systems available on the market (e.g. Windows, Mac, Linux, etc.).
- 19 -
2.2.4 Advantages of Fuzzy Classification and fCQL
Ce que l'ordinateur a de peu plaisant, c'est qu'il ne sait dire que oui ou
non, jamais un peut-être. (Brigitte Bardot, actrice française)
Das Unsympathische an Computern ist, dass sie nur ja oder nein sagen
können, aber nicht vielleicht. (Brigitte Bardot, französische Filmschauspielerin)
Saying this, Brigitte Bardot was definitively not aware of the idea of fuzzy classification discussed in this chapter: computers can say "perhaps" very well, they do not return a "yes" or a
"no" only. In fact, the main advantage of fuzzy classification and fCQL is that the membership
degree of a classified element to a class can be calculated in a range between 0 and 1.
In addition, a classified element is, in contrast to sharp classification, not limited to a single
class, but can be assigned to several classes at the same time. With fuzzy classification, the
transition between the different classes become fluent, that means continuous.
Further advantages of fuzzy classification and fCQL can be summarised so far: fCQL enables
the reduction of complexity without loss of information
a fair and non-discriminatory classification of customers according to their performance
the extraction of hidden data and a better description of the classified elements
to consider the potential as well as the possible weaknesses of each classified element
the use of numerical values, i.e. quantitative or monetary variables, and non-numerical
values, i.e. qualitative or non-monetary variables
queries on a linguistic level (that means the formulation of a word or word combinations),
an intuitive and human-oriented querying process and a clear semantic
the adoption of linguistic variables to the marketing terminology of every company and
therefore a facilitated cooperation between marketers and IT specialists
that the raw data of relational databases do not have to be migrated or modified
the multidimensional classification, evaluation and segmentation of objects, with any
number and any kind of attributes or criteria
to decompose complex classifications into a hierarchy of classifications
a dynamic classification, for instance the monitoring of classified elements over time and
the implementation of a trigger mechanism, which warns in case of a negative development
of the classified elements.
- 20 -
As it will be discussed in Chapter 4, fuzzy classification facilitates the personalisation, individualisation and differentiation of marketing and therefore an improved one-to-one marketing
and mass customisation.
In addition, fuzzy classification and the fuzzy Classification Query Language (fCQL) as a data
mining tool can be used for customer and market segmentation, e.g. for the identification of
market segments (compare Figure 14), for classification (e.g. the identification of customer
with high turnover) or for performance measurement, e.g. for the detection of Key Customer
Performance Indicators (KCPIs). Another objective of data mining and fuzzy classification is the
description and visualisation of data.
Figure 14 shows these examples of possible tasks for fuzzy classification, other statistical data
analysis methods and data mining techniques, which are not considered in this master thesis.
For details on data mining and data mining techniques see for instance: [Ester and Sander,
2000, Berry and Linoff 2000, Berson et al. 2000, Drozdenko and Drake 2002, Witten and
Frank 2005, Neckel and Knobloch 2005, Han and Kamber 2006, Larose 2006, Williams 2006].
Examples of problems
Tasks of data mining
Methods of data mining
Identification of profitable
customer or market segments
Segmentation
Identification of customers
with high turnover
Classification
Detection of Key Customer
Performance Indicators (KCPI)
Customer performance
measurement
Dependency analysis
Forecast of turnover X of
customer Y for next year
Prediction & estimation
Artificial neural
network
Identification and analysis
of purchase patterns
Market basket analysis
Association analysis
Presentation of results
to the management
Description & visualisation
Decision trees
Cluster analysis
Fuzzy classification & fCQL
Figure 14: Examples of Tasks and Methods of Data Mining
- 21 -
Chapter 3
Fuzzy Classification Management Tools
- 22 -
Chapter 3: Fuzzy Classification Management Tools
3.1
Potential Business Applications for Fuzzy Classification
3.1.1 Overview
The fuzzy classification approach and the fCQL toolkit are not just another concept or software
of information management. This data mining tool is a powerful instrument for management,
for the analysis and control of business processes and results.
If adequate data of marketing and accounting is available in an Information System (IS) or in a
Management Information System (MIS), fuzzy classification as a data analysis method can be
successfully used for Performance Measurement (PM). In the field of Customer Relationship
Management (CRM), fuzzy classification can therefore be used for Customer Performance
Measurement (CPM). Figure 15 gives a review of fuzzy Classification Management Tools
External
(market)
Internal
(company)
(fCMT) and the applications to different fields of marketing management.
Customer relationship management
¨Section 4.1
Customer performance measurement Fuzzy portfolio analysis
¨Section 4.2
¨Section 3.2
Fuzzy customer segmentation
Fuzzy SWOT analysis
¨Section 5.1
¨Section 3.3
Fuzzy credit rating
Fuzzy ABC analysis
¨Section 6.2
¨Section 3.4
Fuzzy market segmentation
¨Section 5.2
0.4 0.5
0.3
0.6
0.2
0.1
0
0.468
0.4 0.5
0.3
0.6
0.7
0.2
0.8
0.1
0.9
0
1
0.8
0.1
0.9
0
1
μ high
1
Management =
0.7
0.8
0.964
0.9
1
0
0
0
1
0.739
0.2
μ high
1
Satisfaction
Repurchases
Cross buying
Costs
Equity, CLV
Return on’s
Market analysis

Demand
Products
Structure

Development
Prices
Competition
performance indicators for
0.4 0.5
0.3
0.6
0.7
Value
Loyalty
Retention
Turnover
Profits
Profitability
Analysis Analysing and controlling of
Control
Indicator n
Indicator 2
Indicator 1
Performance
measurement
Fuzzy scoring methods
¨Section 3.5
Marketing and market research
Customer Analysis
Implementation
μ high
Production
Marketing
(e.g. products or customers)
Accounting or finance
(e.g. sales, profits, etc.)

Planning
Figure 15: Fuzzy Classification as a Promising Management Tool for Different Fields
Figure 15 shows that fuzzy classification can be applied, for example, to
management tools; e.g. as fuzzy portfolio analysis, as fuzzy SWOT analysis, as fuzzy ABC
analysis and as fuzzy scoring model (content of the Sections 3.2 to 3.5), and to
performance measurement to analyse and control any kind of performance indicators of
production, marketing, managerial accounting or finance. Section 4.2 focuses on the performance measurement of customers.
- 23 -
Fuzzy classification supports performance measurement, for instance, in the field of
Customer Relationship Management (CRM; discussed in Section 4.1); particularly the
task of customer analysis and customer segmentation (content of Section 5.1)
production and operations; to fuzzily categorise, analyse and control materials, products,
services and production processes and the utilisation of machines or employees.
supplier relationship management; to classify and evaluate different suppliers or products
segment marketing; to undertake fuzzy market segmentations, to fuzzily target market and
to fuzzily position products or companies in markets (content of Section 5.2)
credit rating; to fuzzily calculate the degrees of risk, creditworthiness or other data of bank
customers (content of Section 6.2).
3.1.2 Existing Literature on Marketing and Fuzzy Classification
Surprisingly, only a limited number of reviews, publications or books on fuzzy classification or
fuzzy system applications are available in literature on marketing and business management.
These cover, for instance: production and operations [Sárfi et al. 1996, Vasant et al. 2004],
web mining [Arotaritei and Mitra 2004] and portfolio selection [Inuiguchi and Ramik 2000].
Fuzzy systems have also been applied to credit rating [Levy et al. 1991, Romaniuk and Hall
1992, Weber 1996, Chen and Chiou 1999, Baetge and Heitmann 2000, Hoffmann et al. 2002,
Shin and Sohn 2004] and the modelling of fuzzy data in qualitative marketing research [Varki
et al. 2000]. In marketing, [Casabayo et al. 2004] used a fuzzy system to identify customers
who are most likely to defect to a different grocery retailer when a new retailer establishes
itself in the same area. As they state, the value added by fuzzy classification to customer relationship management is the ability to transform customer data into real useful knowledge
for taking strategic marketing decisions (see [Vogues and Pope 2006]).
[Hruschka 1986] proposed a segmentation of customers using fuzzy clustering methods. A
clusterwise regression model for simultaneous fuzzy market structuring was discussed by
[Wedel and Steenkamp 1991]. [Fisher et al. 1995] analysed fuzzily socio-economic attributes
of older consumers. Hsu’s Fuzzy Grouping Positioning Model [Hsu 2000] enables an understanding of the relationship between consumer consumption patterns and a company’s competitive situation and strategic positioning. However, most of the reviews are basic research
and usually do not deal with relational databases. If they do (see [Takahashi 1995, Biewer
1997, Bosc and Pivert 2000, Kacprzyk and Zadrozny 2000, Galindo et al. 2005]), they follow
another approach than fCQL does. In the authors’ point of view, practical applications of the
fuzzy classification approach both in literature and in business practice of marketing are still
insufficient. In view of that, the following chapters will point out further potential of fuzzy classification in the field of marketing, CRM and particularly in customer performance measurement.
- 24 -
3.2
Fuzzy Portfolio Analysis
3.2.1 Definition
The method of portfolio analysis is often used in the field of strategic management in order to
analyse and plan business strategies. According to [Kotler et al. 2005, p. 60], a business
portfolio is the collection of businesses, products or customers that make up the company.
The portfolio analysis is a tool by which management identifies and evaluates these various
businesses. The best business portfolio is the one that fits the company’s strengths to the opportunities in the environment (compare Section 3.3: Fuzzy SWOT Analysis). The company
first has to analyse its current business portfolio and decide which businesses should receive
more, less or no investment, and second to develop growth strategies for adding new businesses, or, in the case of a customer portfolio, to recruit and bind customers.
The most popular portfolio analysis is the market growth/market share matrix, which was
developed by the Boston Consulting Group (BCG). It has two axes: ‘real market growth’ and
‘relative market share’, which is determined in comparison with the position of the strongest
competitor. The axes define a matrix with four classes, which are usually labelled as ‘stars’,
‘question marks’, ‘cash cows’ and ‘poor dogs’. In the classes, Strategic Business Units (SBU)
or Strategic Business Fields (SBF), products, customers or other objects are positioned.
A Strategic Business Unit (SBU) is a unit of the company that has a separate mission and
objectives, and which can be planned independently from other businesses of the company.
[Grünig and Kühn 2005b, p. 133] define a SBU as a business which contributes critically to the
success, with its own independent market offer, but whose strategy must be adjusted to those
of other business units within the corporation because they operate in the same market and/or
share the same resources. The same authors define a Strategic Business Field (SBF) as a
business which contributes critically to the success and whose strategy can be planned independently because it has an independent market offer and it does not to any significant extent
share markets and/or resources with any other business in the corporation. According to [Kotler et al. 2005, p. 61], the four classes C1 to C4 in Figure 16a can be described as follows:
C1) Stars are high-growth, high-share businesses that require heavy investments to finance
their rapid growth. Eventually their growth will slow down and they turn into cash cows.
C2) Question marks are low-share business units in high-growth markets. They require cash
to hold the market share or become stars. Management has to think about the question
marks, which ones they should build into stars and which ones they should phase out.
C3) Cash cows are low-growth, high-share businesses, products or customers. These established and successful units needs less investment to hold their market share. Thus they
produce cash the company uses to invest in other SBUs.
- 25 -
C1)
Stars
C4)
C3)
Cash cows
Low
Poor dogs
Low
High
Real market growth
C2) Question marks
Improve dramatically reltive market share and then
follow strategy for stars .
Or:
▪ Follow strategy for dogs
C1) Stars
Preserve or increase
relative market share
Invest in resources
or marketing
Tolerate negative cash flow
C4) Poor dogs
Minimize investment
Continue while there is
positive free cash flow
Sell/liquidate businesses,
if cash flows are negative
C3) Cash cows
Preserve relative
market share
Invest defensively in
resources and marketing
Maximise cash flows
Relative market share
C2)
Question marks
b)
Life-cycle
High
a) Real market growth
Source (b): adapted from [Grünig and Kühn 2005b, p. 172]
Figure 16: The Boston Consulting Group Matrix (a) and Norm Strategies (b)
C4) Poor dogs are low-growth, low-share businesses, products or customers. They generate
enough cash to maintain them, but do not promise to be large sources of cash.
Figure 16 shows the BCG portfolio and the norm strategies, which are related to each class.
3.2.2 Sharp Classification and Disadvantages
Considered sharply, for example SBU 2 in Figure 17a, belongs only to one class, C1 (stars).
Following the norm strategy (of Figure 16b), the SBU 2 is may be managed in the same way
as the real star, SBU 1. This could be problematic since the performance (growth and market
share) of SBU 2 still can be improved. The same classification problem occurs to SBU 3,
which is sharply classified as a poor dog (C4), like weak SBU 4. However, this is not right,
because SBU 3 is nearly in the same position as SBU 2 and is rather a star then a dog. Even
if more classes are defined: sharp classification does not solve the problem that elements located near the borders of the classes are classified discriminatorily and imprecisely.
.
SBU 1
C4)
.
Poor dogs
SBU 3
.
SBU 2
C3)
Cash cows
SBU 4
Low
High
.
Real market growth
C2)
C1)
31% Question
marks
0.9 0.1
6.6% Poor dog
24.2%
Question mark
.
0.48 0.42
C4)
.
Sharply classified sales, turnover or profit of a product, customer
or SBU/SBF of the last year or average for recent years
.
.
SBU 1
48.4% Star
14%
Cash cow
20.4% Star
100%
Poor dog
SBU 4
00
0.44
0.56
1
μ low market share
Figure 17: Sharp (a) and Fuzzy (b) BCG Portfolio
- 26 -
SBU 2
C3)
25.6%
SBU 3 Cash cow
29.8% Poor dog
1
Fuzzy classified sales, turnover or profit
100% Star
0.3
0.7
μ high market share
C1)
Stars
High
C2)
Question marks
μ low market growth μ high market growth
b)
Real market growth
Low
a)
3.2.3 Fuzzy Classification and Advantages
With fuzzy classification, misclassifications can be eliminated. Viewed through a fuzzy lens
(Figure 17b), SBU 2 belongs now to all classes at the same time with following normalised
membership degrees: 48.4% to the class ‘stars’, 31% to ‘question marks’, 14% to ‘cash cows’
and 6.6% to the ‘poor dog’ class. SBU 3 goes also with all classes (20.4% ‘star’, 24.2% ‘question mark’, 25.6% ‘cash cow’, 29.8% ‘poor dog’) and can be separated from the real dog SBU
4 and managed accordingly. Considered sharply, there is no ‘question mark’ in the portfolio to
invest. Classified fuzzily, there is a certain percentage; with the fCQL toolkit, the membership
degrees of all the classified business units to the class C2 (‘question marks’) can be queried:
classify
from
with
SBU
portfolio
class is question marks
In this simple example with four SBU’s, the user will receive following absolute membership
degrees to C2: SBU 2: 50.1% and SBU 3: 41.9%.
The portfolio manager now wants to find out which businesses are performing well, i.e. all
membership degrees of all fuzzy classified SBUs to the class ‘stars’ (C1). He queries:
classify
from
with
SBU
portfolio
class is stars
By querying the following membership functions, he obtains the same results:
classify
from
with
SBU
portfolio
real market growth is high market growth and
relative market share is high market share
In this example the query results are also obvious (absolute degrees; SBU 1: 100%, SBU 2:
78.2%, SBU 3, 35.3%). However, in business practice, where portfolios of an enterprise may
consist of hundreds of businesses or products, the query process and the results are more
complicated. In this case, the potential and the benefit of the fCQL toolkit is much higher.
Applying fuzzy classification to strategic portfolios has several implications. Fuzzy classification allow portfolio managers to analyse and decide differentiated and more precisely about
actual and future strategies of each classified business unit. Since the membership degrees to
the different classes, e.g. all membership degrees of all fuzzy classified businesses to the
class C2, can be exactly calculated, the degree of investments in businesses can be undertaken proportionally to the share to a class. Figure 18 shows an example of such fuzzy investment. Since ‘question marks’ have to gain in market share, they require the highest capital spending proportion, for instance 60 percent of the investment expenditures. In addition,
‘stars’ (C1: medium investment of 30%) and ‘cash cows’ (C3: low investment 10%) need some
investments to hold the market share. There are no investments in ‘poor dogs’ (C4: 0%).
- 27 -
SBU 2
SBU 3
SHARPINV3 = 60%
High investment
(60%)
SBU 5
Low
C4)
SBU 1
Medium
investment (30%)
SHARPINV4 = 30%
SBU 4
C3)
No investment
Low investment
(0%)
SBU 7 (10%)
SHARPINV7
SBU 6
= 0% SHARPINV = 10%
6
SBU 8
SHARPINV8 = 0%
Low
FUZZINV2 = 48.1%
C1)
SHARPINV5 = 10%
High
Real market growth
C2)
SBU 2
SBU 3
FUZZINV3 = 60%
1
0
SBU 1
FUZZINV1 = 30%
26.1% medium investment
17.3% high investment
33.3% low investment
23.3% no investment
FUZZINV5 = 0.261·30
+ 0.173·60 + 0.333·10 +
0.233·0 = 21.5%
SBU 8
C1)
FUZZINV4 = 26.2%
SBU 4
SBU 5
C3)
SBU 6
SBU 7
FUZZINV7
= 4.7%
FUZZINV6 = 10%
FUZZINV8 = 0%
High
C2)
b)
SHARPINV1 = 30%
SHARPINV2 = 60%
μ low market growth μ high market growth
Real market growth
a)
0
SHARPINVi = Sharp investment in SBUi (i = 1,…,8)
1
FUZZINVi = Fuzzy investment in SBUi (i = 1,…,8)
μ low market share
μ high market share
Figure 18: Sharp (a) and Fuzzy (b) Investments
Classified sharply (Figure 18a), 60% are invested in both SBU 2 and SBU 3. However, in the
fuzzy classified portfolio (Figure 18b), the investment in SBU 2 amounts only 48.1%, not 60%
anymore. Sharply, SBU 5 in the middle of the portfolio belongs only to class C3) with 10% investment. Fuzzily, in SBU 5 is invested much more: 21.5% (for calculation see Appendix 1).
In contrast to sharp portfolio analysis, fuzzy one enables to calculate a theoretically most
efficient investment and an optimal allocation of limited resources. However, these values have to be considered as benchmarks for the share to be invested. The exact sum for the
investment depends on what is necessary for the development of the business or the SBU.
Considered dynamically, fuzzy portfolio analysis enables to monitor the development or investments in each business unit and its performance over time. In addition, fuzzy classification
contributes to the achievement of a main objective of portfolio analysis: a well-balanced collection of investments. Fuzzy portfolio analysis facilitates to balance mature cash producing
business units and future-oriented units, which require investments. This ensures, on the one
hand, that the company is investing in markets, which are highly attractive in the future, and on
the other hand, that the business units in mature markets must be self-financing and produce
free cash flow, which can be invested in other business units [Grant 2002, p. 410].
To balance a portfolio, management has first to define an ideal composition of the portfolio,
the optimal percentages of the normalised membership degrees of all classified business units
to each class. In Figure 19c), an optimally Balanced Portfolio (BP*k; symbol: ;) consists of
33% membership degrees of all business units to class C1 (BP*1 = ∑Mnorm(Oi│C1) = 0.33),
21% membership degrees of all business units to class C2 (BP*2 = ∑Mnorm(Oi│C2) = 0.21),
37% membership degrees of all business units to class C3 (BP*3 = ∑Mnorm(Oi│C3) = 0.37) and at most
09% membership degrees of all business units to class C4 (BP*4 = ∑Mnorm(Oi│C4) = 0.09).
In this case, the ‘cash cows’ and ‘stars’ generate enough free cash flows, which can be invested in ‘question marks’, i.e. in the future of the company.
- 28 -
If the portfolio is optimally balanced (BP*k), the Balance Error (BE) is 0. The higher the sum of
the absolute deviations (│absolute values│) between the membership degrees Mnorm(Oi│Ck)
and the degrees of an optimally balanced portfolio, the higher is the balance error.
⎛ n
⎞ ⎛ n
⎞ ⎛ n
⎞ ⎛ n
⎞
BE = ⎜ ∑Mnorm(Oi C1 ) − 0.33⎟ + ⎜ ∑Mnorm(Oi C2 ) − 0.21⎟ + ⎜ ∑Mnorm(Oi C3 ) − 0.37⎟ + ⎜ ∑Mnorm(Oi C4 ) − 0.09⎟
⎝ i =1
⎠ ⎝ i =1
⎠ ⎝ i =1
⎠ ⎝ i =1
⎠
Example of C4 in Figure 19b/d: │.25-.33│+│.22-.21│+│.14-.37│+│.39-.09│ = .08 + .01 + .23 + .3 = 0.65
In addition to the BE, basic requirements should be defined for an optimal portfolio, e. g. not
less ‘cash cows’ than 25% of the portfolio (∑Mnorm(Oi│C3) > 0.25), at least 10% ‘questions
marks’ (∑Mnorm(Oi│C1) > 0.1) or not more ‘poor dogs’ than 15% (∑Mnorm(Oi│C4) < 0.15).
a) Unbalanced Portfolios
b) Balance Indicator
Future cash flow
C3-4)
C3-3)
:
:
Optimally
balanced
portfolio
Unbalanced customer portfolios
Trigger mechanism (red-flag-function)
0.4 0.37
0.35 0.36
0.39
0.25
C4-3)
C4-4)
0.37
0.54
0.22
0.14
0.30
Share
0.1
C1)
actual
cash flow
Balanced
(diversified)
portfolio
C4)
C3)
Insufficient
actual & future
cash flow
Insufficient
future
potential
:
Real market growth
C1-1)
C1-2)
Stars: 33%
Question Marks:
21%
BP*1 = ∑Mnorm(Oi│C1) = 0.33
0.21
max.:
0.18
;
C2)
:
0.65
0.64
0.33
0.19
0.05
:Insufficient
Portfolio with both
insufficient actual
& future cash flow
Portfolio
with insufficient
future potential
Portfolio
with insufficient
actual cash flow
C4-1)
Actual cash flow
:
:
C4-2)
C2-3)
C3-1)
C2-4)
C3-2)
C2-1)
C2-2)
C1-4)
C1-3)
Poor Dogs: 9% Cash Cows: 37%
0.09
0
Real market growth
Real market growth
Balance
error (BE)
Market growth
CX.1 Stars CX.2 Question marks CX.3 Cash cows CX.4 Poor dogs
d) Balance Error and Implementation of a Trigger Mechanism c) Optimally Balanced Portfolio (BP*k)
Figure 19: Balancing of Fuzzy Classified Portfolios
If there are too little membership degrees of all classified businesses to class C2-3 (portfolio
with insufficient actual cash flow in Figure 19a), to class C3-2 (portfolio with insufficient future
potential) or to C4-2 and C4-3 (portfolio with both insufficient actual and future cash flow), the
portfolio is not balanced (symbol: :). In this case, the basic requirements are not fulfilled
and/or the level of the balance error exceeds the maximum level (max. of 0.30 in Figure 19d).
A trigger mechanism (red-flag-function) warns the portfolio manager who can launch counter
measures to augment actual cash flow or/and future cash flows, by investing in R&D or by
acquiring new businesses in order to balance and optimise the composition of the portfolio.
- 29 -
The fuzzy portfolio analysis approach is not only promising for the BCG portfolio, but can be
applied to all other existing portfolio analyses. For instance, the more complex McKinsey/
General Electrics portfolio holds the dimensions ‘industry attractiveness’ and ‘competitive
strength’, which can be defined by one or several criteria shown in Table 6.
Table 6: Criteria for Assessing Industry Attractiveness and Competitive Strength
W
Criteria for assessing industry attractiveness
Criteria for assessing competitive strength
Industry or market size or volume
Industry or market growth or potential
Industry or market profitability
Capital, investment or cost intensity
Technological level and stability
Innovation intensity and potential
Competitive intensity or entrance barriers
Cyclical, seasonal or inflation independence
Dependence of regulation and government
Risks (industry, technology, environment, etc.)
Market share or growth of market share
Corporate size and financial strength
Market, customer or technological knowledge
Product quality
Corporate or product image
Production, sales or service effectiveness
Low operating costs and productivity
Price competitiveness
Distribution network or geographic advantages
Competence of management and employees
Source: adapted from [Kotler et al. 2005, p. 62, Grünig and Kühn 2005b, p. 176, Waibel an Käppeli 2006, pp. 85f]
The two attributes ‘industry attractiveness’ and ‘competitive strength’ with the terms ‘low’, ‘medium’ and ‘high’ define nine squares (classes C1 to C9 in Figure 20a). To each class in the
industry attractiveness/competitive strength portfolio, general recommendations for strategies
are assigned: the strategy ‘growth/investment’ (invest and tolerate any negative free cash
flow), ‘selective’ and ‘harvest/divestment’ (invest a minimum, divest if cash flow is negative).
Keeping strictly to these norm strategies could be problematic, as following examples show.
Although SBU 2 is almost in the same position as SBU 1, it is in a red field and may wrongly
disinvested, but in green SBU 1 is invested. In SBU 3 it also divested, although it is not in such
a bad position as SBU 4 and a selective strategy would be appropriate.
With fuzzy classification and the disappearance of the classes sharp borders (Figure 20b), the
problems are solved; it can be fuzzily invested or disinvested in businesses for optimal growth.
b)
Double
Try
or quit SBU 1 harder
SBU 2
Proceed
Selected
with care
withdrawal
C7)
Low
C9)
C5)
C8)
SBU 3
Phased
Divest
withdrawal
SBU 4
Low
Medium
C1)
Leader
C2)
Growth
C6)
Cash
Generation
High
Source: [Schawel and Billing 2004, p.147]
Competitive strength
High
C3)
Medium
C4)
1
0
Growth/investment strategy
C4)
C3)
C1)
Double
or quit
Try
harder
Leader
C7)
C5)
C2)
Selected
withdrawal
Proceed
with care
Growth
C9)
C8)
C6)
Divest
Phased
withdrawal
Cash
Generation
Competitive
strength
Industry attractiveness
Industry attractiveness
μ unattractive μ medium attractive μ attractive
a) Sharp classification
0
Selective strategy
1
Harvest/divestment strategy
μ low competitive μ medium competitive
μ high competitive
Figure 20: Sharp (a) and Fuzzy Classified (b) McKinsey/General Electrics Portfolio
- 30 -
3.3
Fuzzy SWOT Analysis
3.3.1 Definition
SWOT stands for Strengths, Weaknesses, Opportunities and Threats and is an often used
strategic analysis and planning method both in business management theory and practice.
According to [Kotler et al. 2005, pp. 58f], SWOT analysis is “a distillation of the findings of the
internal and external audits which draws attention to the critical organisational strengths and
weaknesses and the opportunities and threats facing the company.”
The strengths and weaknesses analysis is an internal audit that focuses on the corporate
performance, strategy, resources, capabilities or developments. For instance, the following
elements can be classified: company’s sales, materials, production, products, services, customers, financial resources, employees and skills, know-how, innovative ability or flexibility.
The opportunities and threats analysis is carried out by examining external elements of
the demographic, economic, political, legal, sociological, technological or cultural environment and of competitors (e.g. their products, prices, distribution, resources or customers).
b)
Weaknesses
Convert
Reduce
Opportunities
Threats
C4)
C3)
Positive
Negative
Strengths
C1)
Element 1
Weaknesses
Element 3
C4)
1
Element 4
Opportunities
μ external
Reduce
Evaluation
External
Convert
Element 2
C3)
Threats
Element 5
0
Evaluation
Strengths
C2)
Avoid
C1)
Match
C2)
Perspective
μ internal
Perspective
Internal
a)
0
1
μ positive
μ negative
Figure 21: Sharp (a) and Fuzzy (b) SWOT Matrix
So far, the SWOT analysis has been applied in a sharp manner in most cases: the elements
or attributes in the SWOT are classified sharply, i.e. an attribute is classified exactly and only
to one class (to C1, C2, C3 or to C4 in Figure 21a).
In fact, in SWOT dominates a "negative-or-positive-" and a "internal-or-external-thinking".
However, it is often inadequate to classify objects sharply, as Figure 21b shows:
- 31 -
Elements 1 and 2 are considered as weaknesses. However, element 2 was evaluated not
as negative as 1 and should be viewed as positive too, as a weakness and as a strength.
Although element 3 and 4 are located nearly at the same position, they sharply belong to
different, opposite classes: element 3 is internal and negative, 4 external and positive.
Even though element 4 and 5 are both classified in the same class C4, their positions are
actually very different. While element 5 is certainly a high opportunity, for 4 this is not clear:
it could quickly change into strength, weakness or into a threat.
As these examples point out: it makes sense to consider transitions between different classes
step like. With a fuzzy SWOT analysis (Figure 21b), the classification problem can be avoided,
since the classified elements can be partly internal, external, positive and negative at the
same time. This reflects reality better, since it is rarely the case that a classified element,
characteristic, object or fact has only positive or only negative points. Fuzzy classification not
Degree of strength
Degree of weakness
(competitive advantages or KPIs or
KSF to retain)
C2-4)
C2-3)
Unimportant
strengths
Development
strengths
(to keep or to
selectively improve)
(to improve)
1
0
1
μ unimportant
C2)
C1)
Strengths
Weaknesses
C4)
C3)
Opportunities
Threats
0
C4-4)
C4-3)
Negligible
opportunities
Limited
opportunities
(to ignore)
(to improve
attractiveness )
0
1
μ very improbably
μ very probably
μ very serious
(to focus on
and to invest)
analogue:
Damage
C3-2)
High
risks
C3-1)
Extreme
risks
C3-4)
Low
risks
C3-3)
Moderate
risks
Fuzzy Risk Matrix
μ not serious
(to improve
success probability)
1
Probability
0
C4-1)
High
opportunities
(to keep low)
(to monitor and
selectively reduce)
μ unimportant
μ very important
Seriousness of threat
Success probability
μ very attractive
μ unattractive
1
C4-2)
C1-3)
Secondary
weaknesses
d) Fuzzy Threat Matrix
Attractiveness of opportunity
Golden
opportunities
C1-4)
Unimportant
weaknesses
0
1
μ very important
c) Fuzzy Opportunity Matrix
C1-1)
Primary
weaknesses
(to monitor & reduce (competitive disadif necessary)
vanteges to reduce)
μ low weakness
(to selectively
retain)
C1-2)
Irrelevant
weaknesses
0
C3-2)
C3-1)
Latent but
high threats
Major
threats
(to monitor; e.g.
emergency planning)
(to pay high attention
and to avoid by
contingency plan-
C3-4)
C3-3)
Minor
threats
Probable but
low threats
(to ignore)
(to monitor and to
avoid if necessary)
0
1
μ very improbably
μ very probably
Figure 22: Fuzzy Strength (a), Weakness (b), Opportunity (c) and Threat (d) Matrices
- 32 -
Probability of occurrence
0
C2-1)
Strategic
strengths
Importance of strength
μ low strength
1
C2-2)
Ineffectual
strengths
μ high weakness
b) Fuzzy Weakness Matrix
μ high strength
a) Fuzzy Strength Matrix
Importance of weakness
can be applied to the SWOT matrix only, but also to each of the four classes in Figure 22.
Each strength of the fuzzy strength matrix (Figure 22a) can be evaluated fuzzily by its degree of importance, whether it is rather a ‘strategic strength’ (a Key Success Factors; KSF), an
‘ineffectual’, an ‘unimportant’ or a ‘development strength’ to improve. Management can acquire
information on all ‘strategic strengths’ by querying the membership degrees to C2-1:
classify
from
with
strengths
strength matrix
class is strategic strengths
Appendix 2 (Checklist for Performing; Fuzzy Strengths/Weaknesses Analysis) shows a list,
how marketing, finance, manufactering or organisation performance can be evaluated fuzzily.
Management also needs to monitor key macroenvironment forces (demographic-economic,
natural, technological, political-legal, and social-cultural) and significant microenvironment
actors (customers, competitors, suppliers, distributors, dealers) that affect its ability to earn
profits. For each development or trend of the external environment, management has to identify the associated opportunities and threats [Kotler and Keller 2005, p. 52]. To evaluate opportunities, the manager can use a Market Opportunity Analysis (MOA) to determine the attractiveness and probability of success, by defining a fuzzy opportunity matrix (see Figure 22c).
To determine fuzzily the best marketing opportunities to invest (C4-1), the user queries:
classify
from
with
opportunities
opportunity matrix
class is high opportunities
In addition, threats have to be observed as well: an environmental threat is a challenge posed
by an unfavourable trend or development that would lead, in the absence of defensive marketing action, to lower sales or profit [Kotler and Keller 2005, p. 53]. Threats should be classified
according to their seriousness and their probability of occurrence (fuzzy threat matrix in
Figure 22d). Analogical to the threat matrix, a risk matrix classifies different risks by their
‘amount of damage’ and the ‘probability of occurrence’. The risk map is an important instrument to analyse, evaluate and to classify potential risks for a division, a business unit or for the
whole company. The ‘probability of occurrence’ can be estimated either quantitatively on a
metric scale, or qualitatively on an ordinal scale from ‘very improbably’ (rare likelihood of risk
occurrence) to ‘very probably’ (risk occurs almost certain), and the ‘amount of damage’ from
‘insignificant’ to ‘catastrophic’.
As shown in Figure 23a, the classes C1 to C25 are assigned to four different level of risk: low,
moderate, high and extreme. Risks classified above the threshold (dashed diagonal) have to
be surveyed exactly. A risk manager can follow different strategies to accept, retain or limit,
reduce, avoid or to transfer risks to third parties (i.e. to assurances).
So far, the risk matrix has been applied in a sharp manner, although this can be problematic
as the examples in Figure 23a show: risk A and B are nearly in the same position, but they are
classified in very different classes: risk A is a low risk to accept and B is a high risk to reduce.
- 33 -
Amount of damage
C20)
Z
C4)
C2)
C12)
C8)
C5)
C13)
Risk C
C9)
C6)
C14)
C10)
C17)
C1)
X
C3)
Risk D
Y
a
C23)
\
C25)
Very low
C21)
Risk B
C18)
Risk A
[
C24)
Low
C22)
C19)
Medium
High
High risk to transfer
or to reduce
Low risk to accept
or to retain
μ catastropic
C16)
C7)
]
C15)
Very high
μ insignificant
C11)
insignificant minor moderate major catastropic
b)
Threshold of risk
1
Extreme risk to avoid
or to transfer
Moderate risk to reduce
or to accept
0
C1)
Improbable risks
with high amount
of damage
Probable risks
with high amount
of damage
C4)
C3)
Improbable risks
with low amount
of damage
Probable risks
with low amount
of damage
0
1
Examples of risks:
C2)
a)
Amount of damage
μ very probably
μ very improbably
Source of examples: [Gladen 2006, p. 130]
X Market risk Y Natural disasters Z Contractual risk [ Personell risk \ Legal risk ] Production risk
Figure 23: Sharp (a) and Fuzzy (b) Risk Matrix
Risk C and D, one the other hand, are classified in the same class, although the amount of
damage and the probability of occurrence of risk D is higher. With fuzzy classification (Figure
23b), such mistakes do not happen and risks of the matrix can be classified, managed and
controlled more exactly. To receive the membership degrees of all risks, where the amount of
damage is catastrophic and the probability of occurrence is very high (C1), it has to be queried:
classify
from
with
risks
risk matrix
probability of occurrence is very probably and
amount of damage is catastropic
This section illustrated that the use of a fuzzy SWOT analysis is manifold. It enables
a more exactly and differentiated description of the classified elements in all matrices
to discern new market and marketing opportunities
to classify and survey different kind of threats or risks in the fuzzy risks matrix
the analysis of the company’s prospects for sales and profitability
to focus on the corporate activities and resources
to improve deficiencies and shortcomings and to defend or to extend strengths
to reveal the real core competences or competitive advantages, and disadvantages
the identification of Key Performance Indicators (KPI) or Key Success Factors (KSF)
the formulation or adoption of corporate, business or marketing strategies
a clear semantic and a reduction of redundant internal classes
to monitor the SWOT matrices or the risk matrix dynamically and fuzzily over time
the definition of a strength, weakness, opportunity or threat and a SWOT indicator and
the implementation of trigger mechanisms, if SWOT or risk indicators are degrading.
- 34 -
3.4
Fuzzy ABC Analysis
3.4.1 Definition
The ABC analysis is the most widely used method of customer, material or product segmentation. In CRM practice, the ABC analysis is used between 29% and 88% of all companies,
depending on industry and study (see e.g. [Plinke 1997, Rudolf-Sipötz 2001, Köhler 2005,
Bruhn and Georgi 2006, Günter and Helm 2006, Reinecke and Tomczak 2006, Homburg and
Krohmer 2006]). ABC analysis is used to reduce complexity, to analyse and monitor the customer structure, and to prioritise investments. It classifies customers into three different
segments, into A-, B- and C-customer. In Table 7, 20 customers are sorted by their turnover.
Table 7: Sharp ABC Analysis
#
Cumulative share
of customers (%)
1
2
3
4
5
6
7
8
…
19
20
5%
10%
15%
20%
25%
30%
35%
40%
…
95%
100%
Class
Class def. Number of
Customer
(sharp)
Customers
A
[0,…,11.9]
B
[12,…,35.9]
5
C
[36,…,100]
13
2
Marshall
Miller
Brown
Wright
O’Connor
Cooper
Smith
Ford
…
Graham
Forrester
Total
Turnover
2006 (€)
21’760
19’040
9’520
4’080
3’400
2’040
1’224
816
…
408
340
68’000
Cumulative
Turnover (%)
32%
60%
74%
80%
85%
88%
89.8%
91%
…
99.5%
100%
"20:80rule"
The cumulative shares show, how many customers generate how much of the turnover. In
CRM literature (for instance [Homburg and Beutin 2006, p. 230]), often the 20:80-rule, or the
Pareto-rule is mentioned, which states that 20% of the customers generate 80% of the turnover. Usually, very few A-customers have a high value percentage. In Table 7, only two Acustomers, Marshall and Miller, generate 60% of the turnover. The B segment has a medium
value and a majority of the customers (C) have a low share in turnover.
The Lorenz or concentration curve in the example of Figure 24 indicates a concentrated distribution of customer turnover. In the case of an equipartition, the curve would be a diagonal.
So far, the rather uncritical discussion about ABC analysis for customer segmentation in literature on marketing is always done in a sharp way. However, it is problematic to classify customers sharply. First, the definition and fixing of the three classes (A: [0,…,11.9], B:
[12,…,35.9] and C: [36,…,100] in Table 7) is arbitrary. Second, sharp classification can be
unfair and lead to wrong decisions as the examples of the four customers in Figure 24 show:
- 35 -
Although Ford and Smith have similar turnovers, Ford is classified to class C and Smith to B.
Even though Brown’s turnover (9’520 €) is totally different from that of Smith (1’224 €), they
belong to same class B. However, Brown is rather an A-customer like Miller and should be
considered as a key account too. If Brown or Ford had little higher turnover, in some cases
may be only a few euros, they would slip into a higher class and were managed differently.
Cumulative turnover (%)
#6
90%
#4
80%
#7
Sharp ABC Analysis
#10
#8 #9
#5
#20
#15 #16 #17 #18 #19
#11 #12 #13 #14
Lorenz curve
(concentration
curve)
#3
70%
#2
60%
50%
Cumulative turnover (%)
B-customers
A-customers
C-customers
Ford
Smith
Cumulative share
of customers (%)
100%
Brown
#1
40%
Miller
30%
“20:80-Rule”
20%
A-customers B-customers
10%
C-customers
0%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0
1
0
μA
μB
1
μC
Cumulative share
of customers (%)
With fuzzy classification,
the transitions between
A-, B- and C-customers
became fluent.
Figure 24: Fuzzy ABC Analysis
With fuzzy classification, the transitions between A-, B- and C-customers' become fluent and
customers can be fuzzily classified by their real turnover (compare Figure 24 and Table 8).
Considered fuzzily, only Customer #1 is an A-, #5 a B-, and #10-20 are C-customers only. For
the rest a membership degree for both classes [A and B] or [B and C] are calculated.
Table 8: Fuzzy ABC Analysis
#
Cumulative share
of customers (%)
1
2
3
4
5
6
7
8
9
…
19
20
5%
10%
15%
20%
25%
30%
35%
40%
45%
…
95%
100%
Class
A
(A, B)
B
(B, C)
C
Class definition
(fuzzy)
([A: 100%])
([A: 64%], [B: 36%])
([A: 32%], [B: 68%])
([A: 6%], [B: 94%])
([B: 100%])
([B: 74%], [C: 26%])
([B: 57%], [C: 43%])
([B: 38%], [C: 62%])
([B: 10%], [C: 90%])
([C: 100%])
- 36 -
Customer
Marshall
Miller
Brown
Wright
O’Connor
Cooper
Smith
Ford
Spencer
…
Graham
Forrester
Total
Turnover 2006
(in €)
21’760
19’040
9’520
4’080
3’400
2’040
1’224
816
748
…
408
340
68’000
Cumulative
turnover (%)
32%
60%
74%
80%
85%
88%
89.8%
91%
92.1%
…
99.5%
100%
Another restriction of the discussed example of ABC analysis is the isolated consideration of
only one single criterion, turnover. Customer turnover provides no information about the costs
and the profitability of a customer. Consequently, a differentiated fuzzy ABC analysis should
consider different customer performance indicators for profitability and investments (Figure
25a), for example customer contribution margins, net profit or the Return on Customer (ROC).
CRM should not consider financial metrics only, but also qualitative indicators about the customer relationship, i.e. a fuzzy ABC analysis of customers’ satisfaction, loyalty or retention
(Figure 25b). In this case, very (dis)satisfied or (dis)loyal customers are identified.
In addition, even a highly unprofitable C-customer can be important for a company, since he
may has a very good recommendation, information or cooperation behaviour (Figure 25c), e.g.
a high number of recommendations and suggestions, or profound product knowledge.
0
μA
μB
μC
Fuzzy ABC analysis
of customer
relationship
A-customers B-customers C-customers
0
0
1
1
μA
μB
μC
Performance measure of recommendation,
information & cooperation (I151-173)
Fuzzy ABC analysis of
customer recommendation,
information & cooperation
0
0
1
1
Cumulative share of customers
Customer performance measure
of relationship (I111-150)
Fuzzy ABC analysis
of customer revenue,
profitability & investment
1
c)
b)
a)
Customer performance measure
of profitability & investments (I1-110)
0
μA
μB
μC
1
Examples of customer performance
Examples of customer relationship
Examples of customer recommendation
I#
I#
indicators for revenue & profitability
indicators
information or cooperation indicators
I21
Demanded volume
I124 Customer value
I152 Number of recommendations
I39-41 Share of wallet or customer
I130 Customer involvement
I155 Potential reference recipients
I50-53 Cross-/up-selling
I131 Customer commitment
I156 Role as opinion leader
I55,56 Repurchases
I132 Customer attachment
I158 Consulting/helpdesk intensity
I69-74 Contribution margins I-IV
I133 Customer trust
I163 Number/quality of suggestions
I75-78 Customer gross/net profit
I126 Customer satisfaction
I164 Number of complaints
I79
Customer equity
I134 Customer loyalty
I167 Number of returns
I80,81 Customer Lifetime Value
I137 Customer retention
I168 Product expertise or knowledge
I83-85 Customer potential
I147 Duration of customer relationship
I169 Response rate
I102 Total customer costs
I148 Intensity of customer relationship
I171 Cooperation behaviour
I104 Return on customer (ROC)
I149 Quality of relationship
I172 Expertise for cooperation
For Detail on customer performance indicators (I 1 - I 173) see Appendix 4: 170+ Customer Performance Indicators (pp. 136ff)
I#
Figure 25: Fuzzy ABC Analysis with Different Customer Performance Indicators
As these examples point out: a fuzzy ABC analysis can be adapted with any kind of criteria or
indicators. In addition, the fuzzy ABC analysis can be combined with other analysis methods,
for instance with the fuzzy portfolio analysis.
The combination of the fuzzy ABC analysis with the customer attractiveness/competitive position portfolio (discussed in Chapter 2) defines a fuzzy classification space with six classes, C1
to C6 in Figure 26. ‘A-star-customers to maintain’ (C1) are customers with a high degree of
attractiveness, e.g. customers with high potential for future turnover or customer lifetime value,
and with a strong competitive position (e.g. customers with a high share of wallet).
- 37 -
Customer attractiveness (or potential)
Customer attractiveness (or potential)
C1)
C-development- B-development A-starcustomers
customers
customers
(to invest)
(to invest) (to maintain)
μ unpromising
C6)
C4)
C2)
C-renunciation- B-absorption- A-absorptioncustomers
customers
customers
(not to invest)
(to skim)
(to skim)
1
0
0
1
μ weak (C)
μ medium (B)
C2)
C4)
μ strong (A)
Ford
C6)
Acquisition
strategies
Recovery
strategies
Consequent
implementation
C1)
Competitive Position
C3)
μ promising
Miller
C5)
Brown
Smith
C5)
C3)
Penetration
strategies
Disinvestment
strategies
Moderate
implementation
Retention
strategies
Moderate
& aligned
strategies
Figure 26: Combination of the Fuzzy Portfolio and ABC Analysis
‘A-absorption-customers’ in C2 are ‘strong’ too, but their attractiveness is lower than in C1. In
‘B-development-customers’ (C3) has to be invested, but unpromising ‘B-absorption-customers’
(C4) are rather to skim, like those of C2. A ‘C-development-customer’ (C5) is still promising. In
contrast, ‘C-renunciation-customers’ (C6) are less attractive and should not be invested in.
Considered sharply, customer Ford in the example belongs entirely to C6 and would not be
invested in; fuzzily she belongs to four classes (C2, C4, C5, C6) and is part of different strategies. Customer Smith and Brown also belong to four classes. Miller belongs to C1 and to C2,
i.e. he is not a 100%-A-star-customer anymore: his ‘competitive position’ still can be improved.
This combination of the fuzzy portfolio and ABC analysis is an interesting method for the formulation and implementation of customer strategies. According to the corporate positioning in
the market, to the target segment and to the customer acquisition strategy, a company has
to acquire new, promising customers to maintain or increase sales, profits and market share.
To develop C- or B-customers, the company has to define and implement development and
penetration strategies to intensify customer relationships and to strengthen the company’s
competitive position at the customers. This can be realised e.g. by cross-/up-selling strategies
or by increasing purchase frequency and intensity. In addition, the company should augment
customer retention by different marketing actions discussed in Chapter 4. The intensity of the
strategy implementations has to vary according to the level of a customer’s attractiveness.
With the simultaneous consideration of quantitative and qualitative criteria, the fuzzy ABC
analysis segments customers differentiated, fairly and non-discriminatory. CRM can treat the
customers according to their value for the company. As a result, strategic and operational decisions about customers or customer segments (e.g. key accounts), can be enhanced.
Finally, the fuzzy portfolio and ABC analysis enables an improved personalisation, individualisation and differentiation of the products and services and better mass customisation.
- 38 -
3.5
Fuzzy Scoring Methods
3.5.1 Definition
In contrast to the ABC and portfolio analysis, which consider only one or two criteria, with
scoring methods several quantitative or qualitative variables can be taken into account.
The scoring approach assigns a predefined, weighted score to each value of a customer variable. All scores of the different variables are added to the customer’s total score.
The most well-known example of a scoring method is the so called RFM method. RFM stands
for ‘Recency of last purchase’, ‘Frequency of last purchase’ and ‘Monetary value’ and is a
widely accepted and often used measurement tool in direct marketing and CRM (see for instance [Link and Hildebrand 1993, Blattberg et al. 2001, Köhler 2005, Homburg and Sieben
2003, Neckel and Knobloch 2005, Günter and Helm 2006, Bauer et al. 2006, Krafft 2007]).
Empirical analyses show significant correlations between these three variables and the repurchase behaviour of customers, e.g. the response rate to mailings [Köhler 2005, Krafft 2007].
High frequencies and high monetary values of customer repurchases are more likely
the more recent a customer purchased (recency)
the more frequent a customer ordered in defined period (frequency) and
the higher customer turnovers were in the last years (monetary value).
The more recent and frequent a customer purchased products from the company in a period,
and the higher the monetary value is, the more the points assigned to a customer. Table 9
shows an example of the RFM method used in mail order business. The assignment of the
points to the classes obviously happens in a sharp manner. If a customer ordered for 99 €, for
instance, he is classified sharply in the Monetary class ‘51-100 €’ and receives plus 25 points.
Table 9: Example of the RFM Method with Sharp Classes
Variables
Initial score
Date of last purchase (Recency)
Number of orders
(Frequency)
Ø turnover of last 3
orders (Monetary)
Number
of returns
Number of mailings
since last purchase
Customers
Points per variable
+25
≤ 6 months:
+40
7-9 months: 10-12 months: 13-18 months: 19-24 months: earlier:
+25
+15
+5
-5
-15
Number of orders or purchases multiplied by 6
≤ 25 €:
+5
26-50 €:
+15
0-1:
2-3:
0
-5
Per main catalogue:
-12
51-100 €:
+25
101-150 €:
+35
4-6:
7-10:
-10
-20
Per special catalogue:
-6
151-200 €:
+40
>200 €:
+45
11-15:
-30
Per mailing:
-2
>15:
-40
Brown
Miller
+25
+25
28 weeks: 27 weeks:
+25
+40
7·6 =
+42
7·6 =
+42
99 €:
+25
102 €:
+35
0:
0
1 mailing:
-2
Total score
115
0:
0
1 mailing:
-2
140
Source: adapted from [Link and Hildebrand 1993, p. 49, Neckel and Knobloch 2005, p. 211]
- 39 -
However, this frequently used RFM model with sharp classes is very problematic. Customers
Brown and Miller have the same values for four variables (see Table 9): both have an initial
score of +25, the same number of orders (+42), returns (0) and mailings (-2). Nevertheless,
there are two little differences: the date of Brown’s last purchase was 28 weeks ago (class: ‘7-9
months’), Miller’s last one was 27 weeks ago (class: ‘≤ 6 months’), and the average turnover of
Miller (102 €; class: ‘101-150 €’) was 3 € higher than this one of Brown (99 €; class: ’51-100 €’).
Since Miller ordered only one week earlier than Brown, and Miller only had a 3 € higher turnover, they are classified in two different classes and Miller (140 points) receives 25 points
more than Brown (115 points). Although Miller and Brown had the same purchase behaviour,
Miller had a 20% higher score than Brown and is may managed and treated differently by the
mail order company. In addition, the opposite can happen too: although two customers have
totally different purchase behaviour, they may have the same number of points.
These examples show how imprecise and inadequate methods with sharp classes can be.
To derive a fuzzy Recency Frequency Monetary method, the following example of the RFM
approach shall be considered. The attribute or linguistic variable ‘recency’ is described by the
terms ‘long ago’ and ‘very recent’, the attribute ‘frequency’ by the terms ‘rare’ and ‘frequent’
and the third attribute, ‘monetary value’, is divided into the terms ‘low value’ and ‘high value’.
The contexts of the three qualifying attributes can be defined as follows:
The attribute domain of ‘recency’ is defined by [0, 730] days and is divided into the equivalence classes [0, 365] days for ‘very recent’ last repurchase and [366, 730] days for a repurchase which took places a ‘long (time) ago’.
The domain of ‘frequency’, measured by the number of repurchases, is [0, 100] and has
two equivalence classes: [0, 9] for ‘rare’ repurchases, and [10, 100] for ‘frequent’ ones.
The domain of the third variable ‘monetary value’ is defined by [0, 200] euros of customer
average turnover, divided into the equivalence classes [0, 99] euros for ‘low value’ and into
an average turnover of [100, 200] euros, which is considered as ‘high value’.
Table 10: RFM Method: Definition of Classes and Scores
Class
C1
C2
C3
C4
C5
C6
C7
C8
Recency
[Days last purchase]
[0, 365]
[0, 365]
[366, 730]
[366, 730]
[0, 365]
[0, 365]
[366, 730]
[366, 730]
RFM attributes, [equivalence classes] and (terms)
Frequency
Monetary value
(Term)
Very recent
Very recent
Long ago
Long ago
Very recent
Very recent
Long ago
Long ago
[# of purchases]
[10, 100]
[0, 9]
[10, 100]
[0, 9]
[10, 100]
[0, 9]
[10, 100]
[0, 9]
(Term)
Frequent
Rare
Frequent
Rare
Frequent
Rare
Frequent
Rare
- 40 -
[Ø turnover]
[0, 99]
[0, 99]
[0, 99]
[0, 99]
[100, 200]
[100, 200]
[100, 200]
[100, 200]
(Term)
Low value
Low value
Low value
Low value
High value
High value
High value
High value
Scores
70 p
40 p
30 p
0p
100 p
60 p
50 p
20 p
In Table 10, eight classes (C1 to C8) are defined by the RFM attributes and terms. For each
class, a number of points are defined: the more recent and frequent a customer purchases
products and the higher the monetary value is, the more points are assigned to the customer.
The four customers are now sharply RFM classified and receive following scores (Table 11):
Table 11: Sharp RFM Scoring of Customers
Customer
Class
Smith
Ford
Brown
Miller
C3
C4
C5
C5
RFM attributes, [values of the customers] and (terms)
Recency
Frequency
Monetary value
[Days last purch.]
378
723
342
14
(Term)
Long ago
Long ago
Very recent
Very recent
[# of purch.]
11
7
13
38
(Term)
Frequent
Rare
Frequent
Frequent
[Ø turnover]
92
12
117
193
Score
(Term)
Low value
Low value
High value
High value
30 p
0p
100 p
100 p
Here again, the same problem of sharp classification emerges: although Smith and Brown
have quite similar values, they are classified in different classes and to Smith are assigned 70
points less. In contrast, Miller is classified with 100 points in the same class as Brown, although Miller’s performance is much better. By the definition of fuzzy sets and membership
functions, the RFM classification space becomes fuzzy (compare Figure 27b).
b) Fuzzy RFM Method
Recency
Monetary value
C5) 100 p
C1) 70 p
C2) 40 p
C7) 50 p
C8) 20 p
Smith
high
value
C3) 30 p
C4) 0 p
low
value
rare
Miller
frequent
Brown
Smith
Frequency
μ very recent
C6) 60 p
1
8.97
+ 3.86
+ 3.35
+0
+ 17.52
+ 8.06
+ 7.70
+ 2.35
Miller
C5) 100 points
= 51.8 points Brown
μ lang ago
long ago
very recent
Recency
C1)
C2)
C3)
C4)
C5)
C6)
C7)
C8)
Monetary value
a) Sharp RFM Method
C3) 12.86
C4) + 0
C1) 10.43
C2) + 5.22
Smith
C3) + 5.20
C4) + 0
C5) + 9.71
C6) + 5.02
C7) + 5.73
C8) + 1.99
= 43.3 points
μ high
value
Ford
μ low
value
= 12.9 points
0
0
Frequency
Ford
C5) 100 points C5) 100 points C3) 30 points C4) 0 points
1
μ rare
μ frequent
Figure 27: Sharp (a) and Fuzzy (b) RFM Method
With fuzzy classification, the scores of the customers can be calculated more exactly and fairly
(for the calculation see Table 12 and Appendix 1). Classified fuzzily, 43.3 points are assigned
to Smith, what is better than the 30 points in the sharp classification. Ford is also classified
better (sharp: 0; fuzzy: 12.9 points), whereas Brown (51.8 points) is classified much lower in
comparison to the sharp classification (100 points). Only Miller, who performs as good as possible in ‘recency’, ‘frequency’ and ‘monetary value’, still has the same 100 points.
- 41 -
Table 12: Fuzzy RFM Scoring of Customers
Class
C1
C2
C3
C4
C5
C6
C7
C8
Mnorm(Oi│Ck)
functions to each class
μvery recent
μvery recent
μlong ago
μlong ago
μvery recent
μvery recent
μlong ago
μlong ago
μfrequent
μrare
μfrequent
μrare
μfrequent
μrare
μfrequent
μrare
Ford
Fuzzy Calculated RFM Score
Smith
Brown
0 0.14902
μlow value 70
0 0.13051
μlow value 40
μlow value 30 0.42857 0.17340
0.57143 0.15235
μlow value 0
0 0.09708
μhigh value 100
0 0.08360
μhigh value 60
0 0.11456
μhigh value 50
0 0.09948
μhigh value 20
1
1
Total
0.12810
0.09641
0.11167
0.08296
0.17521
0.13437
0.15395
0.11732
1
Miller
Ford
0
0
0
0
1
0
0
0
1
Smith
Brown
0
0
12.86
0
0
0
0
0
10.43
5.22
5.20
0
9.71
5.02
5.73
1.99
8.97
3.86
3.35
0
17.52
8.06
7.70
2.35
Miller
0
0
0
0
100
0
0
0
12.86
43.3
51.81
100
The membership degrees to the different classes and the total score of the fuzzy RFM model
can be considered as an important indicator, how likely a customer is to purchase again by
the company and how loyal a customer is. According to the membership degrees to the different classes, the customer manager could define incentives to animate customers to buy
again, to purchase more frequent by the company, and at a higher monetary value:
Customers who have recently bought the company’s products (C1, C2, C5, and C6) might
receive a thank-you letter or an e-mail for buying from the company and for the confidence
shown. In addition, these mailings may contain personalised product recommendations or
cross- and up-selling offers (like the e-mails from Amazon, for instance).
To customers who bought or buy very frequently (C1, C3, C5, C7), the company should
send mailings (e.g. catalogues) to inform about existing or new products and special offers.
For customers with high monetary values a one-time discount is offered. The discount in
percent (C5: 20%, C7: 10%, C6 and C1: 5%) also depends on the frequency and recency of
purchases. Figure 28a shows the combination of the RFM incentives.
C3)
C4)
C4)
Smith
C3)
Ford
frequent
C7)
Monetary
value
C8)
rare
C1)
C1)
high
value
low
value
Frequency
C5)
Low discount (5%)
High discount (20%)
2 reminders
2 reminders
8 mailings per month 8 mailings per month
C2)
No discount (0%)
2 reminders
1 mailings per month
C3)
C7)
Medium discount (10%)
No discount (0%)
1 reminders
0 reminders
4 mailings per month 4 mailings per month
C4)
No discount (0%)
0 reminders
0 mailings
Recency
Smith
Discount: 4.3%
1 reminders
4 mailings/month
C6)
Low discount (5%)
2 reminders
Miller
Discount: 20%
2 reminders
8 mailings/month
1
Brown
Discount: 6.4%
2 reminders
4 mailings/month
Ford
Discount: 0%
0 reminders
1 mailings/month
0
0
Frequency
C8)
No discount (0%)
1 reminders
1
μ rare
Figure 28: Fuzzy RFM Incentives
- 42 -
μ frequent
Monetary value
Smith Ford
C2)
C5)
μ very recent
C5)
b) Fuzzy RFM Incentives
Miller
C6)
μ lang ago
C5)
very recent
Miller Brown
Recency
long ago
a) Definition of
RFM incentives
μ high
value
μ low
value
Classified sharply, Smith receives no discount, although he has similar values like Brown, who
receives a high discount (20%). As a result, Smith may feel discriminated or betrayed by the
company and he does his shopping elsewhere. Miller might feel the same way too, because
he has the same discount as Brown, although his performance is much better than Brown’s.
With fuzzy classification (Figure 28b), fair incentives can be offered and personal discounts
calculated. Smith receives a discount of 4.3% (0.097·20 + 0.115·10 + 0.084·5 + 0.149·5) and
Brown a discount of 6.4% (0.175·20 + 0.154·10 + 0.134·5 + 0.128·5). Miller still has the maximum of 20%. With personalised discounts the company offers the same incentives for all
customers to repurchase by the company. In addition, Smith and Brown receive an adequate
number of reminders according to their purchase behaviour and are informed about the company and its products. The service of a reminder or mailing should be based on permission
marketing, so that customers are not annoyed about undesired information or advertising.
Fuzzy scoring models in combination with a fuzzy hierarchical classification are not limited to
three attributes, as discussed in this example of RFM analysis. By the assignment of weighted
scores to any number of qualitative or quantitative criteria, scoring models can be applied
flexibly and independently to different kinds of analyses in CRM.
Both approaches, sharp and fuzzy scoring methods, are confronted with the same problem of
weighting or assigning the "right" number of points to each class. Since the weighting of the
classes’ score (e.g. C1: 70 points) is often done subjectively and intuitively, RFM methods are
not as objective as they pretend to be. However, scoring and RFM methods are more valid,
significant and objective, if certain valuation rules are considered (see [Plinke 1997, p. 140]).
In addition, the weighting and scoring of the classes can be improved using empirical data
and regression analyses (e.g. logistic regression).
However, a fuzzy RFM analysis can be undertaken without knowing the optimal weights and
scores: if the manager wants to know all clients who are most likely to repurchase, he queries:
classify
from
with
customers
RFM
recency is very recent and frequency is frequent
and monetary value is high value
This fuzzy classification query would return all absolute membership degrees of the customers
to the class C5 (Ford: 0%, Smith 25.3%, Brown 45.7% and Miller 100%).
With the fuzzy scoring approach, a company can identify, classify, analyse, score, profile,
evaluate and segment customers and customer groups according to their value for the company (compare Section 5.1: Fuzzy Customer Segmentation with Important Indicators).
In addition, fuzzy scoring and profiling of customers facilitates the selective communication to
target customers or fuzzy segments, the adoption and customisation of the marketing mix and
an effective management of customer relations. As a result, the allocation of limited ressources, like a marketing or CRM budget, can be optimised.
- 43 -
Chapter 4
Analytical Customer Relationship Management
- 44 -
Chapter 4: Analytical Customer Relationship Management
4.1
Customer Relationship Management (CRM)
4.1.1 Overview
In Chapter 2, the fuzzy classification approach and fCQL (fuzzy Classification Query Language)
were described and applied to different management tools in Chapter 3.
This Chapter will now discuss the application of fuzzy classification and the fuzzy Classification
Management Tools (fCMT) to the field of Customer Relationship Management (CRM).
Fuzzy classification could be categorised as a multidimensional data analysis method. Consequently, the fuzzy classification approach with fCQL are especially suited for the different tasks
and processes of analytical Customer Relationship Management (aCRM).
However, it is a principal task of aCRM to analyse, classify, evaluate and segment customers
and their performance according to their value for the company in order to manage and maximise customers like any other asset of the company [Blattberg et al. 2001].
That means: analytical customer relationship management needs a comprehensive and holistic
Customer Performance Measurement (CPM; content of Section 4.2). To measure customer
performance comprehensively, CPM has to define, collect, record and manage an adequate
customer data and Customer Performance Indicators (CPI; discussed in Section 4.3).
The fuzzy classification and evaluation of customer performance indicators is an important condition to fuzzily segment customers (content of Chapter 5) and to define and implement appropriate strategies for a customer or for a customer segment.
These processes and the structure of Chapter 4, Analytical Customer Relationship Manage-
Customer Performance Measurement (CPM) (Section 4.2)
Customer Performance Indicators (CPI) (Section 4.3)
applied to
Management tools (fCMT) (Chapter 3)
applied to
Fuzzy classification (Chapter 2)
Customer Relationship Management (CRM) (Section 4.1)
Fuzzy customer performance analysis, classification and evaluation
Fuzzy customer segmentation with important indicators (Section 5.1)
Action planning and implementation of customer strategies
Analytical CRM
(Chapter 4)
Operational CRM
Strategic CRM
Figure 29: Structure of Chapter 4 and 5
- 45 -
Fuzzy market segmentation (Section 5.2)
ment, and Chapter 5, Fuzzy Customer Segmentation, are shown in Figure 29.
4.1.2 The Development to the Customer Oriented Company
After the change from seller’s to buyer’s markets about 40 years ago, the quality orientation,
Total Quality Management (TCM), became increasing important in companies. Hence, until
the end of the 1980s, firms focused mainly on the improvement of manufacturing processes, on
the product quality and the integration of in-house product data (see Figure 30).
Thereafter, companies started to align the product quality to the customers needs and, within
the scope of Business Process Re-Engineering (BPR), to redesign business processes. Due
to information systems, such as Sales Force Automation (SFA) or Computer Aided Selling
(CAS), business processes were customised and production or sales data integrated.
Since the 1990s, many companies have to align the complete value chain and the organisation
to customers, especially in high competitive markets. With the rise of Customer Relationship
Management (CRM), many companies became more customer-oriented. Thereby, the sale of a
product or service is not considered as a business transaction only, but as the beginning of a
customer relationship. In addition, a consistent view of customers in enterprises often is only
possible by integrating all customer relevant data in a CRM information system.
Focus: customer satisfaction
Focus: customer retention
Integration of
all customerrelated data
Process orientation
Integration of
the production
and sales data
Integration of
the in-house
product data
Business Process
Re-Engineering (e.g. SFA/CAS)
Customer orientation
Customer Relationship
Management (CRM)
CRM Trends: increasing
IT-applications
systematisation
individualisation
differentiation
profit orientation
Quality orientation
Total Quality Management (TQM)
1980
1990
2000
Source: adapted from [Sieben 2001, p. 298, Rapp 2005 p. 42]
Figure 30: The Development to the Customer-Oriented Company
Within the field of CRM, several trends can be observed: CRM became more and more systematic, individual, differentiated and, due to innovations in IT, highly technical in the last years. The
profit orientation and the controlling of CRM and CRM processes also increased.
The main ideas of customer (relationship) management are not new, but as old as people doing
business. Concepts like customer satisfaction and retention, which have been discussed since
the 1970s, are still the basic for long-term and profitable relationships. However, the importance
and potential of CRM is highly increasing due to technological innovations and software in IT.
- 46 -
4.1.3
CRM and Customer Management
CRM is only one, but an important approach in the domain of customer management and
marketing. Figure 31 illustrates the development of the different fields in customer management.
Most of them are promising for business applications for fuzzy classification.
According to [Belz and Bieger 2004, 2006], the weight and diffusions of the different approaches
can vary strongly over different industries.
Weight of different approaches
in customer management
Small customer management
fc
Partnership systems
Customer acquisition fc
Global account management
Key account management fc
One-to-one marketing fc
Direct marketing fc
Customer relationship management
fc
Customer retention fc
Lifestyle segments
Market segmentation
fc
fc
Mass customisation fc
Mass marketing
1970
Promising
fc fields for fuzzy
classification
1980
1990
Primary focus of the paper
2000
Secondary focus
2010
t
Not specified
Source: adapted from [Belz and Bieger 2004, p. 56]
Figure 31: Applications of Fuzzy Classification in the Domain of Customer Management
The primary focus of this thesis is the application of fuzzy classification in the domain of Customer Relationship Management (CRM), which includes customer segmentation, acquisitions
and retention (compare Figure 31).
Especially in saturated markets, which is the case in many industries, customer retention is increasingly important, but also difficult.
Mass customisation and one-to-one marketing are particularly interesting for fuzzy classification
as it facilitates to identify and segment customers according to their individual behaviour towards the company.
- 47 -
2) Customized marketing fc
1) Individual marketing fc
(Focus: product & customer)
(Focus: product)
fc Personalisation
fc Product design
fc Product configuration
…
fc Mikromarketing
Self-customisation fc Customer profiling
Learning relationship
fc Mass customisation
fc Database marketing
fc One-to-one marketing
4) Mass/transactional marketing
3) Relationship marketing fc
(Focus: sale)
(Focus: customer)
Partnership
Standard products
…
Transaction
Customer integration
…
Repurchases
fc Possible marketing concepts for fuzzy classification
fc Loyalty programs
fc Retention programs
fc Satisfaction controlling
Intensity of customer relationship
Individualisation of the product/service
Source: adapted from [Muther 1998, p. 54], [Payne 2006, p. 8]
Figure 32: Fuzzy Classification and Individual Marketing
Fuzzy classification allows treating customers or customer segments more individually. Therefore, fuzzy classification is not only promising for customised marketing (for instance for the
fuzzy classification of products, their design or configuration) and relationship marketing (e.g.
to classify less loyal customers in order to launch loyalty programs), but also for individual
marketing like mass customisation, customer profiling or for database marketing (in Figure 32).
The idea of one-to-one marketing, which originally trace back to [Peppers and Rogers 1993,
1997], is to learn from the customer ("learning relationship") and to improve the relationship and
knowledge about the customer and his needs by cooperation (for instance through customers’
feedback or observations) in order to treat customers individually. One-to-one (or individual)
marketing and mass marketing differ in many topics. Table 13 contrasts these two concepts and
shows the possible applications for fuzzy classification in one-to-one marketing.
Table 13: Mass Marketing vs. One-to-One Marketing and Applications for Fuzzy Classification
Mass Marketing
Average customer
All customers
No segmentation
Customer anonymity
Standard product
Mass production
Mass distribution
Mass advertising
One-way message
Mass promotion
Economics of scale
Share of market
Customer attraction
Short-term focus
One-to-One Marketing
Individual customer
Profitable customers
Customer segmentation
Customer profile
Customised marketing offering
Customised production
Individualised distribution
Individualised message
Two-way message
Individualised incentives
Economics of scope
Share of customer
Customer retention
Long-term focus
Applications for Fuzzy Classification
fc
Performance measurement of individual customers
Fuzzy customer analysis and segmentation
(microsegmentation), key account management
Fuzzy classified customer profile
Fuzzy classification based customisation, individualistion, configuration or personalisation of products/services
Individualised campaigns and messages based on fuzzy
segmentation of customers or (target) segments
Fuzzy calculation of personal prices, offers or discounts
Optimised share of customer/wallet by cross-/up-selling
Retention programs for based on fuzzy retention portfolios
Source: adapted from the scheme of [Kotler and Keller 2005, p. 155]
- 48 -
4.1.4 Definition of CRM
The term Customer Relationship Management (CRM) is defined differently. Put simply, CRM
is “information-enabled relationship marketing” [Payne 2006, p. 11]. A rather technical definition
considers CRM as “the automation of horizontally integrated business processes involving front
office customer points (marketing, sales, service and support) via multiple, interconnected delivery channels [Peelen 2005, p. 3].” Other definitions describe CRM as a process that addresses
all aspects of identifying customers, creating customer knowledge, building customer relationships, and shaping their perceptions of the organisation and its products. The Gartner Group
describes CRM as „an IT enabled business strategy, the outcomes of which optimise profitability, revenue and customer satisfaction by organising around customer segments, fostering customer-satisfying behaviours and implementing customer-centric processes” [Payne 2006, p. 5].
Phase model
Processes (tasks)
Market research
Subject area
fc can be used for the identification, classification,
analysis and development of promising markets or
market segments (Chapter 5.2 Market Segmentation).
fc
Demand analysis fc
Public relations
Information
Advertising
fc
Sales promotion
fc
Campaigns
fc
Marketing
fc
Customising
fc
Service
Price calculation fc
Financing
fc
Sales
Contract conclusion fc
Order processing
Handling of payment fc
Purchase
Finance
Delivery
Startup
Sales
Installation
Maintenance
Repairs
After sales
Service
Complaints
Database marketing and fc support the configuration,
customisation, individualisation or personalisation
of products, services and communication.
Thanks to fc, prices, estimates, accounts, paying
conditions (e.g. warranties) and contracts can be
calculated and offered more flexibly and personally
in order to set incentives to improve buying attitudes,
retain customers and to drive customer equity & profit.
With fc important customer information, indicators
or values of finance and accounting (e.g. customer
turnover, contribution margins, profits, equity, customer
lifetime value) can be better analysed and evaluated
in order to manage customer relations effectively.
With fc, services and customer care can be aligned
more personally, customer- and segment-oriented
(e.g. additional services for strategic key customers).
By fuzzy classifying customers or customer segments,
customer care can be realised more precisely and
focused (e.g. on key, periled, unsatisfied customers).
Customer service fc
Customer care
fc enables to realise individual campaigns e.g. for
important, valuable customers (key account management) and for new or imperilled customers.
Through adequate fuzzy customer segmentation,
customers (or segments) can be informed specifically
(depending on their needs or their purchase history).
Tender preparation fc
Offer
fc can help to classify and analyse needs and
demands of customers as well as to develop and
provide corresponding products and services.
fc allows advertising and promoting more effectively
and efficiently by addressing the target customers or
segments with adequate communication instruments.
Individual campaign fc
Information
Use of fuzzy classification (fc)
fc
fc facilitates to bind customers with individual loyalty
programs or incentives, like fuzzy personal accounts.
Customer retention fc
Source: adapted from [Schumacher and Meyer 2004, p. 39]
Figure 33: The Use of Fuzzy Classification in Typical Tasks of CRM
- 49 -
Figure 33 shows typical tasks and processes in CRM, mostly promising for fuzzy classification.
In CRM, a distinction is often made between strategic, analytical and operational CRM (see for
instance [Meier 2003b, Hippner and Wilde 2004a, 2006, Peelen 2006]). Therefore, CRM application architectures (in Figure 34) often contain a strategic, analytical and an operational layer.
Customers
Brown
Miller
Personal
contact
Phone
Mail
WWW
E-Mail
Mobile
technologies
Customer Relationship Communications (CRC)
Service
Sales
Management Information System (MIS)
After sales
Analytical CRM (aCRM)
Customer Relationship Operations (CRO)
Strategic CRM
Mass media
Data mining
& OLAP
Fuzzy
queries &
gathering
of data
fCQL
toolkit
Enterprise Ressource Planning (ERP)
Supply Chain
Management (SCM)
CRM
software
Computer Integrated
Manufactering (CIM)
fc
Closed loop
DWH
architecture
Relational
Database Management System
Customer Relationship Analytics (CRA)
fc
fc
External data
CRM service systems
fc Customer Performance Measurement (CPM)
Operational CRM
Front office
Ford
Customer contact points
Marketing
Back office
Strategic and operational objectives
Smith
Process-based strategic planning
Fuzzy customer segmentation and targeting
Customer acquisition strategies and programs
Customer loyalty/retention or recovery strategies and programs
Add-on selling (cross-/down-/up-selling) strategies
Source: adapted from [Hippner et al. 2001b, p. 201, Meier 2003b, p.10, Neckel and Knobloch 2005 p. 45]
Figure 34: CRM Application Architecture
At the strategic layer at the bottom, strategies and operational objectives for customer acquisition, retention and recovery are defined as well as add-on selling strategies.
The operational layer at the top, with the components Customer Relationship Communications
(CRC) and Customer Relationship Operations (CRO), is in touch with the customers. Customers communicate und interact with the company through different channels or media.
The analytical layer in the middle, analytical Customer Relationship Management (aCRM),
includes an Information (IS) or a Management Information System (MIS), a Relational Database
Management System (RDBMS), a customer Data Warehouse (DWH), CRM software, data mining tools or other CRM service systems, which contain customer data. The query process with
fCQL deals with the RDBMS and DWH within the layer of aCRM, as shown in Figure 34.
- 50 -
Stationary
networks
Stationary
user; sales or
customer manager
Analytical CRM
RDBMS
DWH
Customer data
(customer information)
Mobile
networks
Internet
Server
Desktop
WLAN
LAN
EDGE
HSDPA
UMTS
(E)GPRS
Mobile Analytical CRM
Laptop
Bluetooth
Tablet PC
PDA
Smart phone
Cell phone
CRM
eCRM
Mobile
CRM
Customers
Intranet
fCQL & SQL
queries
Mobile user; sales or
customer manager
classify
from
with
Operational
CRM
Figure 35: Mobile Analytical Customer Relationship Management
To undertake fuzzy queries with fCQL, the desktop user (e.g. a sales or customer manager) can
access the RDBMS or DWH over a conventional, stationary network (intranet; see Figure 35). In
the case of mobile analytical CRM, a field manager has wireless connection to the customer
data over mobile networks and the internet, using a laptop, PDA or a smart phone. With mobile
technologies, a customer or sales manager has flexible access to relevant and actual customer
information (anytime – anywhere). In addition, electronic and mobile CRM improve operational
activities, for instance to prioritise customer care or services, and to improve time management.
4.1.5 Objectives and Key Points of CRM
CRM is not an end in itself. Its main objectives are to increase customer profits and customer
equity, which is defined here as the monetary value of a company’s current customers.
In order to augment the profitability of customer relationships, a number of requirements have to
be fulfilled: first of all, the company has to focus and align its processes towards its customers,
in order to identify, recognise and analyse the needs and wants of the target customers, or of a
segment, regarding the company and its products or services. Second, the products or services
offered should meet the customers’ expectations and create a visible value for them.
Customer value is defined here as the value creation from a customer perspective, that
means the entire product, services, personnel and image values that a buyer receives from a
marketing offer (compare [Kotler et al. 2005, p. 464] and Subsection 5.1.6: Customer Value).
Consequently, the term ‘customer value from the customer perspective’ is not to be confused
with the term ‘customer value from the company perspective’ or Customer Lifetime Value (CLV).
However, customer value contributes significantly to customer satisfaction, the fulfilment of the
customer’s needs or expectations. Increasing satisfaction and commitment raises customer
loyalty and customer retention [Reichheld and Sasser 1990, Boulding et al. 1993, Anderson
and Sullivan 1993, Rapp 1995, Oliver 1996, Bolton 1998, Mital and Kamakura 2001, Homburg
2006, Krafft 2007, Bruhn 2007, Bruhn and Homburg 2005, 2006, Homburg and Krohmer 2006].
- 51 -
Empirical research show significant positive correlations of satisfaction (or loyalty) and
operating margins like sales, turnover and cash flows [Bolton 1998, Rust et al. 1995],
Return on Investment (ROI, e.g. [Anderson et al. 1997], for a review see [Zeithaml 2000]),
Return on Customer Investment (ROCI; for instance [Hayman and Schultz 1999]),
Return on Relationship (ROR; see for example [Gummesson 1999]),
accounting returns [Ittner and Larcker 1998]
firm value and a firm’s raw market value [Ittner and Larcker 1996] and
shareholder value and stock prices [Anderson et al. 2004, Fornell et al. 2006].
Increasing customer satisfaction and thereby higher retention secures future revenues [Fornell
1992, Rust et al. 1995, 2005] and reduces costs of future customer transactions, such as
ones associated with sales, transport, service and payment (see for instance [Reichheld 1993,
1996, Reichheld and Sasser 1996, Srivasatava et al. 1998]). Consequently, sales, turnover
and net cash flows are higher. At the same time, greater customer retention indicates a more
stable customer base that provides a relatively predictable source of futures revenue due to
repurchases and add-on sellings In addition, recommendations of satisfied customers lead
to lower acquisition costs and to additional revenues from new customers. As a result, the
long-term profits of the firm increase, their volatility tends to be lower, and the risk associated
with the anticipated cash flows is reduced. Therefore, market value and shareholder value
increase (see [Anderson et al. 2004, p. 173]). All the discussed empirical findings of research
on CRM can be summarised in a CRM success chain (compare Figure 36).
Customer
investments
Customer
orientation
n
Customer
attitude
Customer
value
o
Customer
satisfaction
Customer
needs
Customer
expectations
Behavioural
intentions
Customer
loyalty
p
Customer
behaviour
Customer
equity,CLV
Customer
retention
Recommendations
word of mouth
q
Customer
results
Market
value
Network effects
Market
share
r
Shareholder
Cu. Profit,
Contr. margins
Repurchases
Perceived value &
price/performance
Commitment
& attachment
Cross-/up-selling
Turnover,
sales, cash flow
Share of wallet
Image
Quality of
relationship
Perceived product
or service quality
Switching
Enthusiasm
costs
factors
Price sensitivity
Marketing management
Customer costs
(sales, marketing,…)
Accounting & finance
Firm & shareholder value management
Product and service management
Customer Performance Measurement (CPM) & Customer Relationship Management (CRM)
Performance measurement and management
Figure 36: CRM Success Chain
- 52 -
Each indicator of the success chain can be assigned to a psychological construct of a simple
model: products and services (n; customer investments) influence customer attitudes (o),
such as the perceived customer value and the perceived product, service or relationship quality,
which effect customer satisfaction. Customer attitudes towards the company and its products
and services lead to behavioural intentions (p), for instance, the intentions to repurchase the
same or other products from the company. Customer behaviour (q) is whether they really
repurchase (or not), cross-buy or recommend products to friends, for instance. Their behaviour
leads to customer results (r), e.g. customer turnover, contribution margins and profits.
It is a strategic task for firms to analyse, control and manage the different customer indicators
proposed in the CRM success chain. This is a challenge for both marketing management, on
the left side of Figure 36, and for managerial accounting or finance on the right.
Such performance measurement and management should not only consider organisational,
production- or process-related and financial indicators, measures, numbers, metrics or figures,
but, in particular, market- and customer-related performance indicators.
The "small CRM success chain" in Figure 36, or similar success chains, discussed for instance
in [Bruhn 2007], contain important constructs of marketing (for instance customer satisfaction,
loyalty, retention). However, this small CRM success chain does not provide enough customer
information and customer performance indicators for a customer performance measurement
system, since the aggregation level of information is too high.
Consequently, more indicators have to be defined and taken into account.
That means that the "small CRM success chain" has to be enlarged to a "big CRM success
chain" with a comprehensive number of different indicators.
“Only what get’s measured is managed and done.”
According to this management slogan, CRM and marketing cannot manage its clients, if customers cannot be described, measured and evaluated. Typically, these processes are based on
customer data and information. Consequently, CRM requires a number of relevant customer
criteria and customer performance indicators, as discussed in the following section.
- 53 -
4.2
Customer Performance Measurement
4.2.1 Definitions
Entering rather new territory of research, this section develops a new concept of customer performance measurement and management by considering a comprehensive number of different,
but relevant customer performance indicators.
First of all, since there are no common definitions of several basic expressions for this purpose,
the different terms have to be derived from related expressions and are defined as follows.
Performance is, among others, the observable or measurable behaviour of a person [Oxford
1989]. Derived there from, customer performance can be defined:
Customer Performance (CP) is a measurable monetary or non-monetary result of a customer
relationship in a defined period.
To operationalise ‘performance’, performance measurement is used. However, performance
measurement is a topic which is often discussed but rarely defined [Neely et al. 1995].
Performance Measurement (PM), for the purpose of this work, is understood as the measurement, analysis and communication of performance [Wettstein 2002, p. 19]. Applied on customer
performance, customer performance measurement can be defined as follows:
Customer Performance Measurement (CPM) is the acquisition, analysis and the evaluation of
performance-related customer information.
However, CPM has many facets and can be characterised by following dimensions of customer performance measurement: the unit, format, planning, interval, time, alignment, connection with incentives, CRM layer and the aggregation level. The values of these dimensions can
be very different, as shown in Figure 37.
1) Unit
2) Format
discrete membercustomer
qualitative
ship functions
loyalty non-monetary
continuous functions
customer
quantitative
monetary
turnover
8) Aggregation
level
high
all customers,
customer segment
customer
strategies
7) CRM layer
one customer
operational
strategic
variation
long-term
low – high
1–
100
short-term
Customer Performance
Measurement (CPM)
low
sale
3) Planning interval
∆X↓
improvement X↑
6) Connection with incentives
5 years
1 month
ex post
ex ante
lagging-indicator
leading-indicator
internal
4) Time
CRM, employee
external
customer
5) Alignment
Source: adapted from [Müller-Stewens 1998, p. 37, Gleich 2001, p. 11, Reinecke 2004, p. 48]
Figure 37: Dimensions of Customer Performance Measurement
- 54 -
Performance management is “a philosophy which is supported by performance measurement”
[Lebas 1995, p. 44]. That means that performance measurement is a part of performance management. However, performance management is understood as a management function
(analysis, planning, implementation and control) and encompasses the methodologies, metrics,
processes, software tools and systems that manage the performance [Cokins 2004].
Consequently, customer performance management is defined here as a CRM function and
encompasses all customer performance indicators, instruments, processes, software tools and
systems of CRM to analyse and control customer performance.
Finally, a Performance Measurement System (PMS) is defined as “a set of metrics used to
quantify both, the effectiveness and the efficiency of actions” [Neely et al. 1995, p. 5]. However,
a Customer Performance Measurement System (CPMS), as an instrument of analytical
CRM, is defined here as an information or CRM system used to analyse, evaluate, control and
communicate customer performance and customer strategies.
4.2.2 Processes of Customer Performance Measurement
Following the discussed CRM success chain, each company should select and define an adequate number of important customer performance indicators (o in Figure 38; the term customer
performance indicator is defined and discussed in the next subsection).
n
Requirements
for indicators
o
Selection of customer
performance indicators
Z
Collection of
customer data
[
r
Analysis
of results
s
Evaluation &
segmentation
t
Action
planning
Figure 38: Processes of Customer Performance Measurement
However, the chosen indicators have to meet several requirements (n) of performance measurement theory and practice (see for instance [Bruhn 2003a, p. 89, Reinecke 2004, p. 329,
Vollmuth 2006, p. 23, Probst 2006, p. 18]).
Customer Performance Indicators (CPIs) in particular have to be
customer-specific (causality and traceability of CPIs to a customer)
relevant (high importance of CPIs for customer relationship management and marketing)
well-defined (precise definition and description of CPIs)
available (availability and accessibility of CPIs in an IS, MIS or within a company)
measurable (measurability of CPIs with continuous or discrete membership functions)
objective (intersubjective traceability of CPIs)
- 55 -
comprehensible (plausibility and understandability of CPIs)
up-to-date (up-to-dateness of CPIs)
user-friendly (user-friendliness of CPIs)
accepted (acceptance of CPIs by employees and managers of CRM or by management)
comparable (comparability of CPIs over time or with competitors)
economical (proportion of the costs of data collection to benefit of CPIs)
decision-/target-oriented (importance of CPIs for action planning and decision making)
reliable (high reliability of CPIs and low random error) and
sensitive (CPIs should be suited for an early warning system or a trigger mechanism).
However, after the selection of customer performance indicators, employees or managers of
CRM, marketing and IT have to collect, record, store and administrate customer data (Z in
Figure 38) in a Customer Performance Measurement System (CPMS) or in an information system. Since information management mostly has to classify customer data, for instance as ‘low’,
‘medium’ or ‘high’ performance of indicator X, one- or multidimensional fuzzy classification ([)
is a crucial step in the processes of customer performance measurement.
This thesis suggests to classify all customer performance indicators fuzzily in order to avoid
misclassifications and to improve the quality of customer evaluations and CRM.
Fuzzy classification of customer data is a precondition to analyse (\), evaluate and to fuzzily
segment customers (]). Based on different fuzzy classified and company-relevant customer
performance indicators, customers can be analysed, segmented by CRM or marketing.
As a result, customer specific CRM or marketing strategies and actions can be planned, communicated and realised (^) in order to improve customer performance on an individual and on a
corporate level.
- 56 -
4.3
Customer Performance Indicators
4.3.1 Definitions
To simplify and aggregate customer data, different indicators and measures are used for performance measurement. Consequently, customer performance measurement has to rely on an
adequate number of customer performance indicators.
A Customer Performance Indicator (CPI) is defined here as a customer-related monetary or
non-monetary criterion (a measure, metric, index, figure or ratio) about customer performance.
In this thesis, Customer Performance Indicators (CPIs) are categorised under six categories:
1) Customer Performance Indicators for Revenue and Profitability (CPIP)
2) Customer Investment Indicators (CII)
3) Customer Relationship Indicators (CRI)
4) Customer Recommendation Indicators (CReI)
5) Customer Information Indicators (CInfI) and
6) Customer Cooperation Indicators (CCI).
Appendix 4 (pp. 136ff) shows a collection of 170+ Customer Performance Indicators (CPIs),
with the definition, operationalisation, measuring units and the purpose of each indicator.
In addition, each indicator can be either assigned to the construct ‘customer investment’, ‘customer attitude’, ‘customer behavioural intention’ ‘customer behaviour’ or ‘customer result’.
Most indicators can be measured at an individual level (*) to analyse the performance of a single customer, or as an aggregate indicator (∑*) to evaluate the performance of all customers or
of a segment. Depending on the company, the size of the enterprise, market or on the industry,
some indicators are more important, other less. However, the important indicators are called:
Key Customer Performance Indicators (KCPI; symbol:
) are defined here as important
monetary or non-monetary customer performance indicators reflecting critical success factors
for customer relationships. In contrast, leverages (symbol:
) are influenceable or controllable
variables to manage successfully CRM processes in order to improve customer performance.
Customer (key) performance indicators, have different functions. They are used for the
comparison of the target and the actual customer performance
operationalisation of objectives and the ‘management by objectives’ of marketing and CRM
prioritisation and realisation of objectives and decisions referring to the customers and CRM
justification of decisions or actions (indicators, data and facts instead of acts on instinct)
communication and management of customer-related tasks and processes
controlling and monitoring of customer and market performance and for action planning.
- 57 -
4.3.2 Categories of Customer Performance Indicators
As mentioned, the CPIs are categorised under six different categories:
1. Customer Performance Indicators for Revenue and Profitability (CPIP) contain criteria,
which are dependent on the customer’s past, actual or future performance regarding his
purchases, e.g. purchased products (I 20 in Appendix 4), number, intensity, frequency of
orders (I 17, I 26, I 28), volume (I 21) or value (I 23, I 29) of purchases and repurchases (I 56),
turnover or sales (I 31), share of wallet (I 39), cross- and up-selling (I 50 - I 53),
payment history (I 66), e.g. punctuality of payment (I 63) and creditworthiness (I 68), and
profitability (I 78), e.g. customer contribution margins I to IV (I 69 - I 73), gross or net profit
(I 75, I 76) and growth of profit (I 77),
customer equity (I 79) and Customer Lifetime Value (CLV; I 80).
As shown in Figure 39, CPIPs, for instance customer profit, often are operational, monetary,
quantitative, lagging and internal indicators to improve. However, each CPIP has different
characteristics, depending on the task and context of customer performance measurement.
1) Unit
2) Format
non-monetary
qualitative
monetary
8) Aggregation
level
high
operational
7) CRM layer
quantitative
long-term
short-term
Example of CPIP:
customer profit (I 76)
low
strategic
variation
ex post
ex ante
4) Time
internal
improvement
external
5) Alignment
Figure 39: Measurement Dimensions of the CPIP ‘Customer Profit’
2. Customer Investment Indicators (CII) consider all customer costs or investments incurred
in a customer relationship in a defined period. Some examples:
customer acquisition, retention and recovery costs (I 87-I 92), customer efficiency (I 108)
transaction (I 94), sales (I 95), service (I 97), communication (I 99), contact costs (I 100)
marketing costs per customer (I 101) or total customer costs (I 102)
"return-on-"ratios like Return on Sales (ROS; I 103), Return on Customer (ROC; I 104),
Return on Relationship (ROR; I 105), Return on Customer Satisfaction (I 106), Return on
Retention (I 107), Return on Marketing (ROM; I 109) or Return on Investment (ROI; I 110).
3. Customer Relationship Indicators (CRI) are mainly qualitative, non-monetary, long-term,
external and strategic indicators about a customer relationship (see Figure 40), such as
- 58 -
customer perceived product (I 118, I 119), service (I 120) or relationship (I 149) quality, perceived price-performance ratio (I 121, I 122) and customer value (I 123, I 124)
customer satisfaction in general (I 125 - I 127), with a product (I 128) or service (I 129) and
customer commitment (I 131), attachment (I 132), loyalty (I 134) or retention (I 137 - I 142).
1) Unit
2) Format
non-monetary
qualitative
monetary
8) Aggregation
level
high
quantitative
long-term
short-term
Example of CRI:
customer loyalty (I 134)
low
operational
strategic
7) CRM layer
ex post
variation
ex ante
4) Time
internal
improvement
external
5) Alignment
Figure 40: Measurement Dimensions of the CRI ‘Customer Loyalty’
4. Customer Recommendation Indicators (CReI) assess, for instance, the number (I 152)
and intensity (I 153) of customer recommendations (Figure 41) or positive word of mouth.
1) Unit
2) Format
qualitative
non-monetary
monetary
8) Aggregation
level
high
low
7) CRM layer
quantitative
long-term
short-term
Example of CReI: number
of recommendations (I 152)
operational
strategic
variation
improvement
ex post
ex ante
4) Time
internal
external
5) Alignment
Figure 41: Measurement Dimensions of the CReI ‘Number of Customer Recommendations’
5. Customer Information Indicators (CInfI) evaluate e.g. customer contacts (I 159), responses
(I 169), number/quality of suggestions (I 163), complaints (I 164) or his product expertise (I 168).
6. Customer Cooperation Indicators (CCI) inform about a customer’s intention to cooperate
(I 171) or his expertise for cooperation (I 172) or other indicators for cooperation.
Figure 42 shows the coherence and relations of the 170+ Customer Performance Indicators
(CPIs) in a "big CRM success chain". However, the cause-and-effects of the big success chain
are ideal, theoretical causalities to illustrate the context and purpose of the different indicators.
- 59 -
Customer
value (I 124)
Customer satisfaction
(IFigure
126-129) 42:
Customer
Customer
acquisition (I 6)
Enthusiasm
factors
Customer
needs
Value
drivers
(Fulfilment of) customer
expectations (I 125)
Corporate
Image (I 115)
Brand Image
(I 116)
Price-performance-ratio (I 122)
Recommendations,
word of mouth (I 151f)
Commitment (I 131)
and attachment (I 132)
Perceived value
or utility (I 123)
Perceived product
quality (I 118,119)
Perceived service
quality (I 120)
Perceived relationship quality (I 149)
Intensity of relationship (I 148)
Intention/intention to
switch (I 140,141)
Switching costs (I 145; e.g.
functional/contractual I 146)
Trust
Involvement
(I 133)
(I 130)
Awareness
Number of new
customers (I 4)
Number of customers (I 7-9)
# of migrated
customers (I 10)
Share of
wallet (I 39-41)
Price sensitivity
Ad awareness (I 112)
Payment history
Intention to dialog,
response rate (I157,169)
Number/quality of
suggestions (I 163)
Product knowledge/
expertise (I 113,168)
Expertise for
cooperation (I 172)
Consulting/helpdesk
intensity (I 158)
Number/quality of
complaints (I 164)
Demand of technical
service (I 166)
Possible cooperation
topics (I 173)
Customer initiated
contacts (I 159,162)
Complaint
satisfaction (I 165)
Number of returns
(I 167)
(I 60,61)
(I 63-68)
Cross- & upselling (I 50-54)
Repurchase
intentions (I 55)
Market
value
Growth (sales I 35,
Market share
(I 43,44)
Retention
costs/efficiency
(I 89,90)
Customer
efficiency (I 108)
Return on relationship (I 105)
Transaction
costs (I 94)
Service costs
(I 97)
(After) sales
costs (I 95,98)
Market penetration (I 48,49)
Return on
investment (I 110)
Return on marketing (I 109)
Return on
customer (I 104)
Return on sales
Customer
profit (I 75-78)
Contribution
margins (I 69-74)
Distribution
& logistic (I 95)
Communication costs (I 98)
Firm
value
profit, m.share I 45)
(I 87,88)
Recovery
costs/efficiency
Shareholder
value
Total customer
costs (I 102)
Marketing
costs (I 101)
Contact costs
(I 100)
(I 103)
Customer turnover/sale (I 31-35)
Cash
flows (I 30)
Customer Investment Indicators (I 87-110)
Purchased
products (I 20)
Purchased
volume (I 21)
# of orders
Order value
(I 56,57)
(I 17)
(I 23)
Heavy usage
index (I 22)
Purchase
intensity (I 26)
Recency
Frequency
(I 27)
(I 25,28)
Monetary
value (I 29)
Repurchases
Customer Information
Indicators (I 157-170)
Acquisition
costs/efficiency
(I 91,92)
# of recovered
customers (I 11)
(I 111,114)
Intention to
cooperate (I 171)
Customer Cooperation
Indicators (I 171-173)
Customer equity (I 79) and
Customer retention
(I 137-139;
142-144,147) Performance
134)
loyalty (I Chain
CustomerIndicators
Lifetime Value (I 80)
CRM Success
with
170+ Customer
Key Customer Performance Indicator (KCPI)
Leverage
Customer Relationship Indicators (I 111-150)
Customer Performance Indicators for Revenue and Profitability (I 1-86)
Figure 42: CRM Success Chain with 170+ Customer Performance Indicators (For Definitions and Details on Indicators see Appendix 4, pp. 136ff)
- 60 -
Purchase Data
Customer
orientation (I 117)
4.3.3 Customer Performance Indicators in Business Practice
Business practice focuses mainly on financial performance indicators, ratios, numbers and
figures like sales (1. in Figure 43), cash flows, contributions margins (3.), profits (4.) or market
share (2.; see [Ambler 2000, Rudolf-Sipötz 2001, Reinecke 2004, Barwise and Farley 2004,
Farris et al. 2006, Krafft 2007]). Consequently, most companies evaluate financial, easily
measurable customer performance indicators, such as turnover (27./34.) or contribution
margins (26./35.). Qualitative criteria like customer satisfaction (5./33.), loyalty or retention
(14.) or the perceived service and product quality (17./18.) are also often measured indicators
in daily business. In addition, CRM practice collects customer data like the number of new
customers (23.) or complaints (32.) or the customers’ payment history (25./36.).
Marketing & sales performance indicators
1. Sales (turnover and/or volume; I 21,31-35)
2. Market share (I 43)
3. Gross margins or gross contributions (I 69-74)
4. Net profit (I 76)
5. Customer satisfaction (I 125-129)
6. Return on sales (I 103)
7. Sales growth relative to market growth (I 45)
8. Relative market share (I 44)
9. Sales per employee
10. Relative price (compared with main competitor)
11. % of new customers relative to portfolio (I 5)
12. Proportion of sales generated by new products
13. Distribution level
14. Customer loyalty or retention (I 134-147)
15. Proportion of own customers to potential total
16. Awareness (I 111-113)
17. Perceived service quality (I 120)
18. Perceived product quality (I 118,119)
19. Brand equity (brand strength)
20. Customer equity or lifetime value (I 79,80)
21. Capital turnover
22. Commitment (I 131; purchase intention, I 55)
Indicators for customer acquisition
23. Number of new customers (I 4)
24. Potential of new customers (I 84)
25. Payment history of new customers (I 67)
26. Contribution margin of new customers (I 74)
27. Share of new customer’s sales to total (I 31)
28. Poached customers from competitors (I 3)
29. Number of prospective customers (I 1)
30. Sales or turnover of first-time buyers (I 32)
31. Acquisition costs (I 87)
[Reinecke 2004]
[Rudolf-Sipötz 2001]
78%
79% 81%
76%
91.5%
83%
68%
50%
63%
53%
53%
52%
50%
70%
63%
81%
36%
43%
57%
73%
66%
64%
38%
20%
65%
25%
36%
78%
41%
64%
77%
40%
55%
20%
40%
17%
32%
15%
[Ambler 2000]
46%
[Barwise and Farley 2004]
43%
54%
36%
66%
39%
20%
37%
77%
22%
18%
Empirical studies
Indicators for customer retention
32. Number of complaints (I 164)
33. Customer satisfaction (I 125-129)
34. Customer turnover or sales (I 31,33-35)
35. Contribution margins (I 69-73)
36. Payment history (I 63-66,68)
37. Share of wallet (I 39,41)
38. Frequency (I 28)
39. Duration of relationship (I 147)
40. Churn rate (I 144)
41. Product portfolio of customers (I 58,59)
42. Retention costs (I 89)
91%
96%
65%
[Reinecke 2004]: n = 419/276
Percentage of usage
64%
55%
54%
44%
62%
63%
24%
28%
27%
36%
35%
12%
85%
81%
59%
52%
[Rudolf-Sipötz 2001]: n = 155
Percentage of usage
[Ambler 2000]: n = 200
Percentage of usage
[Barwise and Farley 2004]: n = 697
Percentage of usage
Source: [Ambler 2000, p. 8], [Rudolf-Sipötz
2001, p. 74], [Reinecke 2004, p. 153, 274,
294], [Barwise and Farley 2004, p. 259]
Figure 43: Empirical Results of Customer Performance Measurement in Companies
- 61 -
Chapter 5
Fuzzy Customer Segmentation
- 62 -
Chapter 5: Fuzzy Customer Segmentation
5.1
Fuzzy Customer Segmentation with Important Indicators
5.1.1 Definitions
Each company has different kinds of customers. Customers have different needs, different
expectations about products or services, different buying or using behaviour of purchased
products and different information or communication behaviour. In addition, from a company
point of view, customers perform differently. In the words of [Peppers and Rogers 1997, p. 31]:
“Customers have different needs from a firm, and they represent different valuations to a firm.”
Consequently, it makes sense to divide customers into groups with similar characteristics or
performance. This process of dividing different customers into homogeneous groups, is called
customer segmentation. Based on customer segmentation, the company can adapt the
marketing mix to the different customer segments, e.g. by mass customised products or by
segment specific communication. Customer-oriented definitions of customer segmentation
are often used in literature. [Bruhn 2003a, p. 88] states: “Customer segmentation refers to a
classification of all potential and current customers, based on their market reaction, into inherently homogeneous but externally heterogeneous sub-groups (i.e. customer segments)”.
However, a company could follow another approach by asking: who are our most interesting,
valuable, profitable or promising customers? This company-oriented interpretation of customer segmentation has another context and other objectives: the identification and separation
of the most valuable customers for the company. Customer criteria, which can be influenced
by the company, are called endogenous criteria. The following endogenous groups of segmentation criteria can be applied for customer segmentation [Bruhn 2003a, p. 90]:
Economic endogenous criteria, for example customer sales or turnover (I 31), contribution
margins (I 69-I 74), profits (I 75-I 78), customer equity (I 79) or customer lifetime value (I 80,I 81)
Behavioural endogenous criteria, like customer retention (I 137), recommendations (I 152),
add-on-selling (I 50-I 53), repurchases (I 56), punctuality of payment or creditworthiness (I 68)
Psychological endogenous criteria, for instance the perceived service, product, or relationship quality (I 118-I 120; I 149), customer value (I 124), satisfaction (I 126) or loyalty (I 134).
Based on these endogenous criteria, for example ‘valuable’ and ‘not valuable’, ‘promising’ and
‘non-promising’, or ‘satisfied’ and ‘dissatisfied’ customers can be separated from each other.
Since a customer should not be labelled sharply only ‘valuable’ or ‘not valuable’, customer
segmentation and evaluation should be done fuzzily.
Fuzzy customer segmentation is defined here as the fuzzy classification of the company’s
current customers into similar, fuzzy segments, using different customer performance indicators (i.e. economic, behavioural and/or psychological endogenous segmentation criteria).
- 63 -
5.1.2 Fuzzy Clustering
As defined in Section 2.1, any class or a combination of classes of a one-, two- or multidimensional fuzzy classification can be defined as a fuzzy segment.
However, it was assumed that the classes where the data is classified in are known and fix. In
reality, data usually do not fit in given classes. Since the classes should be adapted to the
data and not the other way round, the classes have to be identified first. This identification can
be done by cluster analysis. Cluster analysis is a multivariate data analysis method to reduce
complexity of a dataset by grouping elements or objectives to groups, i.e. to clusters.
In contrast to classification, cluster analysis or clustering does not rely on predefined classes.
[Han and Kamber 2006, p. 383] define: “The process of grouping a set of physical or abstract
objects into classes of similar objects is called clustering”. To derive fuzzy classes from the
data, non-disjunctive, fuzzy cluster algorithms can be used, as shown in Figure 44.
Methods of cluster analysis
Disjunctive (sharp) methods
Hierarchical
methods
Partitional
methods
Divisive
Agglomorative
Non-disjunctive methods
Overlapping
methods
Fuzzy-c-means
Mixture
models
Gustafson-Kessel
Gath-Gewa
Fuzzy
methods
FMLE-method
Figure 44: Fuzzy Methods of Cluster Analysis
Considering non-disjunctive methods of cluster analysis based on fuzzy logic, different algorithms are discussed in data mining literature. [Höppner et al. 1999], for instance, examine the
fuzzy-c-means-, Gustafson-Kessel-, Gate-Geva-algorithm and fuzzy-shell-clustering-methods.
The most famous fuzzy algorithm, the fuzzy-C-Means-algorithm (FCM) is an advancement of
the hard c-means. In contrast to the hard c-means, where objects are assigned clearly to clusters, the FCM defines membership degrees between 0 and 1 for each object to the clusters.
The FCM assumes that all clusters have approximately the same shape and size.
The Gustafson-Kessel-algorithm, in contrast, takes the structures of the data into account.
The Gate-Geva-algorithm is an extension of the Gustafson-Kessel-algorithm and considers
additionally the size and density of the clusters. The less important fuzzy-shell-clusteringmethods describe mathematically the shape and distance of data to the geometric structure.
Finally, the Fuzzy-Maximum-Likelihood-Estimation-algorithm (FMLE-algorithm) is based
on a probabilistic evaluation (see for instance [Timm 2002]).
- 64 -
a) Sharp Customer Segments (Ssi)
b) Fuzzy Customer Segments (Sfi)
Indicator 2
Ss1
Ss2
Miller 100% Ss1
Brown 100% Ss1
Ss5
Ford
100% Ss2
Ss4
1
Indicator 1
Smith 100% Ss2
Sf3
Sf1
Sf2
00
1
Brown 53% Sf1
47% Sf2
Smith
48% Ss1
52% Sf2
Ford
48% Sf2
Sf5 28% Sf4
24% Sf5
μ low indicator 1
Miller 100% Sf1
Sf4
Indicator 1
Ss3
μ low indicator 2 μ high indicator 2
Indicator 2
μ high indicator 1
Figure 45: Sharp (a) and Fuzzy (b) Customer Segments
In Figure 45a, for example, five sharp segment are defined (Ss1 to Ss5) by a sharp cluster algorithm. Ford and Smith both belong entirely to segment 2 (Ss2), Brown and Miller to segment
1 (Ss1). Such a sharp segmentation can be problematic, as the examples of Smith and Brown
show: although they have nearly the same values of the indicator 1 and 2, they are classified
in two different segments. If segment 2 (Ss2) would be defined a little bit larger (dashed circle
in Figure 45a), Brown would belong to the same segment 2 as Smith. If segment 1 (Ss1) would
be enlarged (dotted circle), both Smith and Brown belong to segment 1.
Using a fuzzy clustering algorithm, for instance the fuzzy-C-Means-, Gustafson-Kessel, GateGeva- or the Fuzzy-Maximum-Likelihood-Estimation-algorithm, the borders between the different segments become fluent (Figure 45b). That means that the sharp borders between the
segments disappear. Fuzzy classified elements can belong to several segments at the same
time: Ford belongs to three segments (48% to Sf2, 28% to Sf4 and 24% Sf5); Smith and Brown
belong both nearly half to segment 1 and 2. With fuzzy customer segmentation and the definition of fuzzy segments, customers’ positions can be shown more exactly. The probability of
misclassifications is reduced and customers segments can be managed more flexible.
Since the logic of fuzzy segmentation does not depend on the exact form of the segments or
the segmentation, and to simplify matters, a normal fuzzy classification with four classes (as
defined in Section 2.1) will be used for fuzzy customer segmentation in the following sections.
5.1.3 Methods of Customer Segmentation
In literature on marketing, different approaches and methods of customer segmentation
and evaluation are discussed (for a review see [Meffert 2000, Rudolf-Sipötz 2001, Reichold
2006, Günter and Helm 2006, Homburg and Krohmer 2006, Bauer et al. 2006, Krafft 2007]).
- 65 -
Methods of customer
evaluation and segmentation
Monetary
Turnover
Contribution
margins
Profits
Equity, CLV
Non-monetary
Customer value
Satisfaction
Loyalty (ladder)
Duration of CR
Retention, etc.
fc
(Fuzzy) ABC analysis
One-dimensional
Multidimensional
fc
(Fuzzy) scoring
methods
fc
(Fuzzy) portfolio
analysis
fc
static
dynamic
Application
Customer performance indicators
Figure 46: Methods of Customer Segmentation
In Figure 46, a distinction is made between one-dimensional customer evaluation or segmentation methods (for instance the ABC analysis or the present value method of finance), and
multidimensional methods (for example scoring methods and portfolio analyses discussed in
Chapter 3, or other mathematical models).
One-dimensional methods consider, on the one hand, monetary measures, such as customer
turnover, contribution margins, profit or Customer Lifetime Value (CLV), and, on the other
hand, non-monetary indicators like customer value, satisfaction, loyalty or retention.
Fuzzy classification can be categorised as a multidimensional approach of customer evaluation and segmentation, which can be applied to other methods, as fuzzy portfolio analysis,
fuzzy scoring model or as fuzzy ABC analysis (discussed in Chapter 3). In addition, both
monetary and non-monetary customer performance indicators can be classified fuzzily.
In order to evaluate customers comprehensively and to manage them successfully, different
customer data have to be collected and integrated in a corporate information system. Customer data often are displayed in an information dashboard, accessible for the responsible
customer managers and employees of CRM, marketing or of the executive board.
The information dashboard of relevant customer data in Figure 47 shows a categorisation
of following five categories of customer data:
1) Profile data contain conventional customer data like address data, i.e. the customer’s
identification number, name, mail and e-mail address, phone numbers, or demographic
data as customer’s age, gender, civil and family status, education, profession, etc.
In addition, profile data may include psychographic data as a customer’s hobbies, interests, lifestyle and his buying or payment behaviour.
2) Purchase data inform about a customer’s purchase history, i.e. all purchase dates, his
number, volume or value of purchased products or services, his purchase recency and
frequency or the duration of the customer relationship.
- 66 -
fc
1) Profile data
Adress data
#, Name, address,
e-mail, phone, etc.
Profile data
Demographic data
(age, civil & family
status, education)
Psychographic data
(interests, lifestyle)
Buying and payment behavior
2) Purchase data

Dates of purchase
Purchased products
Purchased volume
Heavy usage index
Frequency
Recency
Monetary
Ø order value
Duration of RS
4) Service data
3) Customer Performance Indicators (CPIs)
Customer
relationship
indicators
0.4 0.5
0.3
0.6
0.2
0.7
0.8
0.1
0.468
0.9
0
1
Customer
orientation
¨Subsection 5.1.5
Perceived product/
service quality
Customer value
¨Subsection 5.1.6
Customer
satisfaction
¨Subsection 5.1.7
Customer attachment/commitment
Customer loyalty
¨Subsection 5.1.8
Customer retention
¨Subsection 5.1.9
Customer Lifetime
Cycle
Performance
indicators for
profitability
0.4 0.5
0.3
0.6
0.2
0.7
0.8
0.1
0.739
0.9
0
1
RFM method
Repurchases
¨Subsection 5.1.10
Add-on-selling
¨Subsection 5.1.11
Share of Wallet
¨Chapter 5.1.12
Price sensitivity
Sales or turnover
¨Subsection 5.1.13
Contribution margins
¨Subsection 5.1.14
Customer profits
¨Subsection 5.1.15
Customer equity
or Customer Lifetime Value (CLV)
¨Subsection 5.1.16
fc Inverse: promising performance indicators for fuzzy customer segmentation
Customer
investment
indicators

General
0.4 0.5
0.3
0.6
0.2
0.7
0.8
0.1
0.964
0
1
Acquisition costs
Retention costs
Recovery costs
Administration costs
Transaction costs
Communication costs
Service costs
Contact costs
(After) sales costs
Total customer costs
Marketing costs
Return on’s:
- sales (ROS)
- customer satisfaction
- customer (ROC)
- relationship (ROR)
- Marketing (ROM)
- Investment (ROI)
customer
requests
Demands
of technical
services
Number and
quality of
complaints
Number of
returns
5) Contact data
Date of contact
Number, types,
channel, intensity, costs of
communication
Frequency
of actions
Customer
adviser
Source: adapted from [Homburg and Sieben 2003, p. 427]
Figure 47: Information Dashboard of Relevant Customer Data
3) Customer Performance Indicators (CPIs) hold a number of relevant performance indicators for the customer relationship, profitability and investment as discussed in the last Section 4.3. Following 12 Subsections 5.1.5 to 5.1.16 discuss some of these indicators shown
in the middle of the ‘information dashboard of relevant customer data’ in Figure 47.
4) Service data contain information about the services a customer demanded, e.g. the numbers of general requests, support, the demand of technical services or the number and the
quality of complaints or returns.
5) Contact data (action and reaction data) include all information about the number, dates,
types, channels, intensity, frequency or costs of communication activities or of contacts
with a customer. In Appendix 4, most service and contact data are categorised under customer information indicators.
Some of the profile, purchase, service or contact data is generated directly at the points of
customer contact, for instance in the front office (customer relationship communication and
operations in the architecture of Figure 34).
However, it is the challenge and principal task of analytical CRM to collect, integrate, store,
analyse, administrate and to manage all existing customer data.
Most of the customer data of the dashboard shown in Figure 47 can be used for the fuzzy customer segmentation process and for the definition of fuzzy customers segments:
The customer manager may want to segment the customers according to their profile data,
to their demographic or psychographic data (e.g. all young customers with similar lifestyles).
- 67 -
Regarding the purchase data, all customers, for instance, with similar purchase frequencies
or recency can be chosen and addressed for a marketing campaign.
Another campaign may be launched in the segment of dissatisfied customers to know the
reasons for dissatisfaction and to improve satisfaction.
A specific retention program may be realised for the segment ‘key accounts’; for this, all
customers with high turnover or profits are selected.
Depending on the task, situation or strategy of CRM, different customer data, criteria or performance indicators can be used for ‘fuzzy customer classification’, for ‘fuzzy customer
Customer
strategies
Fuzzy customer
segments, CPIs
Information
demand
Requirements
Fuzzy customer
segmentation
Fuzzy
Customer data;
Customer Performance classified
data, CPIs
Indicators (CPIs)
Customer performance
measurement
Counter
measures
CPIs and KPIs
Fuzzy clusters
and segments
Evaluations
& profiles
Customer evaluation,
scoring and profiling
Fuzzy customer
classification
Fuzzy classified data, CPIs
& fuzzy customer segments
Early warning system
Marketing
Early warning system
(trigger mechanism)
and sales
(trigger mechanism)
Campaign
management
Fuzzy customer
segments
Evaluations,
scores & profiles
Retention
programs
Key account
management
Counter measures
Analytical CRM
Profile, purchase, service,
contact data; CPIs
CRM strategy
development
Operational CRM
Customer Relationship Communication
(CRC) and Operations (CRO)
Strategic CRM
segmentation’ and for ‘customer evaluation, scoring and profiling’ (see Figure 48).
Figure 48: Context of Fuzzy Customer Segmentation
Fuzzy classified customer data, CPIs and fuzzy segments allow to evaluate, qualify, score or
profile customers more exactly and fairly. This enables, for instance, an improved Key Account Management (KAM), i.e. the management of a company’s most important customers
and the formulation of customer or segment specific loyalty or retention programs.
Campaign management is more effective and efficient by adapting communication, information or offers to the different needs and expectations of different fuzzy customer segments.
In addition, an early warning system can warn the customer manager, if the performance of
a customer, of a fuzzy customer segment or of the whole customer portfolio is rapidly decreasing in order to launch counter measures of strategic or operational CRM and marketing.
- 68 -
5.1.4 Selected Indicators for Fuzzy Customer Segmentation
The way customers can be segmented fuzzily, will now be discussed with some examples of
customer performance indicators of the ‘information dashboard of relevant customer data’
(Figure 47) and of the ‘small CRM success chain’. These indicators are chosen because they
can be considered as important Key Customer Performance Indicators (KCPIs) or leverages.
The following indicators in Figure 49 are discussed in the Subsections 5.1.5 to 5.1.16.
Customer
orientation
Customer
value
Customer
satisfaction
(¨5.1.5)
(¨5.1.6)
(¨5.1.7)
Customer
loyalty
(¨5.1.8)
Perceived
value/utility
(¨5.1.9)
(¨5.1.16)
Network effects
Repurchases
(¨5.1.10)
Commitment
& attachment
Perceived
quality
Image
Customer
equity,CLV
Recommendations
Customer
needs
Customer
expectation
Customer
retention
Quality of
relationship
Key Customer Performance Indicator (KCPI)
Add-on selling
(¨5.1.11)
Switching Enthusiasm
factors
barriers
Share of wallet
(¨5.1.12)
Price sensitivity
Leverage
Shareholder
Market
value
Market
share
Profits
(¨5.1.15)
ROR,
ROCI
Contr. margins
(¨5.1.14)
Customer turnover,
sales, cash flow
(¨5.1.13)
Customer costs
Figure 49: Selected Indicators of the CRM Success Chain for Fuzzy Customer Segmentation
By combining different customer performance indicators of the small (Figure 49) or big CRM
success chain (Figure 42; Appendix 4), fuzzy customer portfolios analysis allow managers
to identify and manage fuzzy customer segments. To drive the CRM success chain, a company has to maintain the good performance of all customers with high membership degrees to
the promising classes CX-1 in Figure 50. In addition, strategic CRM has to define adequate
strategies to improve the performance of customers classified in the less promising classes.
C1-4)
C1-3)
C2-1)
C2-4)
C2-3)
C6-2)
C6-1)
C6-4)
C6-3)
C5-2)
C5-1)
C5-4)
C5-3)
C3-2)
C3-1)
C3-4)
C3-3)
Repurchases or add-on selling
Customer retention
Customer equity or profit
Customer equity
Market share
C2-2)
C1-1)
C4-2)
C4-1)
C4-4)
C4-3)
Customer retention
C1-2)
Customer loyalty or retention
Customer value
Customer value
Figure 50: Driving the CRM Success Chain by Optimising Fuzzy Classified Portfolios
- 69 -
5.1.5 Customer Orientation
Customer
orientation
Customer
value
Customer
satisfaction
Customer
retention
Customer
loyalty
Customer
equity
Market
value
Shareholder
To succeed or simply to survive in today’s marketplace, a company has to be customer oriented or customer-centred [Kotler and Keller 2005]. Such a customer-driven company focuses on customer needs and wishes in designing its marketing strategies and on delivering
superior value to customers. According to Kotler, only high customer orientation allows a customer-value-creating marketing, the second step of the success chain.
Customer orientation (I 117) can be defined as the focus on meeting the needs and wants of
a company’s customers. Customer orientation is a comprehensive, continuous analysis and
evaluation of individual customer expectations as well as their internal and external transformation in output. In addition, it includes all interactions in the context of the relationship marketing concept with the objective to establish stable and profitable customer relationships
[Bruhn 2003b, p. 15]. Customer orientation can be also considered as the proximity to customers [Homburg 2000].
However, customer orientation is not for free, but causes considerable costs and investments,
such as administration (I 93), transaction (I 94), service (I 97), contact (I 100) or communication
costs (I 99). Since maximal orientation is mostly not affordable, a company should determine
an optimal degree of customer orientation by analysing its costs and benefit in order to improve the efficiency of customer orientation (see Figure 51a). On an individual level, different
customers may need different degrees of orientation. The company has to focus on customers
with a high information potential, i. e. with an intensive response rate (I 169), a high intention to
dialog (I 157) and/or good cooperation behaviour (I 171 - I 173; see Figure 51b).
b) Fuzzy Customer Orientation Portfolio
0
C1)
Costly
orientation
(low costs,
high benfits)
(high costs,
high benefit)
C4)
C3)
Insufficient
orientation
Inefficient
orientation
(low costs,
low benefit)
(high costs,
low benefit)
0
1
μ low costs
Response rate (I 169; intention to dialog, I 157)
1
0
C1)
Retain high
customer
orientation
C4)
C3)
Retain low
customer
orientation
Reduce
customer
orientation
0
1
μ high costs
C2)
Augment
customer
orientation
Customer orientation (I 117)
1
C2)
Efficient
orientation
Costs of customer orientation
μ low benefit
μ high benefit
Benefit of customer orientation
μ low response rate μ high response rate
a) Fuzzy Cost-Benefit Analysis
μ low orientation
μ high orientation
Figure 51: Fuzzy Cost-Benefit Analysis (a) and Portfolio of Customer Orientation (b)
- 70 -
5.1.6 Customer Value
Customer
orientation
Customer
value
Customer
satisfaction
Customer
loyalty
Customer
retention
Customer
equity
Market
value
Shareholder
Customers choose these products or services, which have the highest value for them. That
means “customers are value-maximisers, within the bound of search costs and limited knowledge, mobility and income. They form expectations of value and act upon them. Then they
compare the actual value they receive in consuming the product to the value expected, and
this affects their satisfaction and repurchase behaviour” [Kotler et al. 2005, p. 463].
The customer delivered value is defined as the difference between total customer value and
total customer cost of a marketing offer, i.e. the ‘profit’ to the customer. Total customer value
is thereby the total of the entire product, services, personnel and image values that a buyer
receives from a marketing offer and total customer cost is the total of all the monetary, time,
energy and psychic costs associated with a marketing offer [Kotler et al. 2005, p. 464].
According to Kotler’s explanations, customer value (I 124) is defined here as value creation
from a customer perspective. This customer point of view of customer value is common in
CRM literature (see [Rust et al. 2000, Eggert 2006, Helm and Günter 2006, Bauer et al. 2006,
Belz and Bieger 2006]). In addition, a distinction is made between the basic value (e.g. technical-functional value) and the added-value (socio-psychological value). It is a strategic task
to identify, create, maintain and control important customer value drivers and to manage
them by portfolios analysis. Since customer value contributes to customer satisfaction, drivers
of customer value and satisfaction often go hand in hand. A selection of customer value and
satisfaction drivers is listed in Table 14. More value factors are shown in Appendix 3.
Table 14: Drivers of Customer Value and Satisfaction
Customer Value Drivers
; Company’s reliability
; Company’s (core) competencies
; Company’s expertise / knowledge
; Company’s experience
; Company’s network
; Goals, goal compatibility
; Bonds (structural, social, economic)
Customer Satisfaction Drivers
Service specific criteria of customer satisfaction
Product specific criteria of customer satisfaction
Reaction to customer problems
; Ability to meet specifications
Reactivity and flexibility
; Failure, error and rejection rate
Availability of contact persons
; Availability of products
Convenience and accuracy of documentation
; Constant quality and reproducibility
Reliability
; Sales assistance
Frequency of delivery
; Product literature
Delivery within the agreed time
; Technical assistance
Terms of payment and financing
; Maintainability and longevity
Handling of complaints
; Completeness of delivery
Service quality and innovation
; Product training
Warranties
; Product development
;: Nominal fuzzy classification possible ;: Discrete fuzzy classification possible

Price, monetary sacrifices
Quality (of products or services)
Technical values
Functional values
Psychological or emotional values
Social values (added-value)
Relationship values
;
;
;
;
;
;
;

Product innovation and creativity
Individualisation or personalisation
Accuracy, flexibility, efficiency
Coordination and organisation
Reputation, image, status, prestige
Problem identification
Empathy and trust
;
;
;
;
;
;
;
;
;
;
;
;
;
;
;
;
;
;
Source: adapted from [Leino 2004, pp. 45f, Belz and Bieger 2004, p. 101, Waibel and Käppeli 2006, p. 172]
- 71 -
5.1.7 Customer Satisfaction
Customer
orientation
Customer
value
Customer
satisfaction
Customer
retention
Customer
loyalty
Customer
equity
Market
value
Shareholder
Customer satisfaction is considered as a Key Performance Indicator (KPI) of corporate success. From the company’s point of view, customer satisfaction can be considered as the
company’s ability to fulfil the economical, emotional and psychological needs of its customers.
From the customer’s point of view, customer satisfaction as an attitude can be defined as
the fulfilment of customers’ requirements or needs. Customer (dis)satisfaction with a product (I
128),
service (I 129) or the company itself (I 126), depends on the customer’s comparison of ex-
pectations (I 125) and perception of experience. Dissatisfied customers often migrate to
competitors and to their offers. This does not only result in lower sales, but often leads to
negative word of mouth. Empirical studies show that only 5% of the dissatisfied customers
complain, but they share their experiences in average with ten other persons and 90% do not
buy the company’s products anymore [Waibel and Käppeli 2006, p. 171].
Several methods are used to track satisfaction: companies can install complaint or suggestion
systems (e.g. customer hotlines, suggestion boxes, complaint forms) or make periodic satisfaction surveys. Thereby it is important to ask customers about their intention to recommend
a product or service (I 151; “would you recommend this product or service to friends?”).
With fuzzy classification, the assignment of a customer to one class or several classes is more
precise. This enables, for instance, the analysis of the customer’s overall (dis)satisfaction in
relation to the satisfaction with a certain product X (see Figure 52a). Regarding the overall
satisfaction and the customer’s willingness or intention to switch, four different basic strategies
are defined in Figure 52b. With fuzzy classification, customers can be assigned differentiated
1
0
C1)
Overall
satisfaction
C4)
C3)
Overall
dissatisfaction
Satisfaction
with product
X only
0
1
Customer satisfaction (I 126)
μ satisfaction
C2)
Dissatisfaction
with product
X only
b)
1
0
C1)
Retain
satisfaction
Bind
customers
C4)
C3)
Reduce
dissatisfaction
Improve
satisfaction
0
1
μ dissatisfaction with X μ satisfaction with X
C2)
μ unwilling to switch μ willing to switch
Figure 52: Examples of Fuzzy Classified Customer Satisfaction Portfolios
- 72 -
Willingness/intention
to switch (I 140,I 141)
μ dissatisfaction
μ satisfaction
μ dissatisfaction
a)
Satisfaction with product X (I 128)
to the basic strategies and can be managed according to their satisfaction level.
5.1.8 Customer Loyalty
Customer
orientation
Customer
value
Customer
satisfaction
Customer
loyalty
Customer
retention
Customer
equity
Market
value
Shareholder
It is well documented in literature on marketing that higher customer satisfaction leads usually
to higher loyalty. Customer loyalty (I 134) is defined here as the behaviour customers exhibit,
when they make frequent repurchases of products of the company or plan to do so, and if they
intend to recommend the company to others. Different methods and approaches are discussed in literature, how to measure loyalty. Directly measurable indicators for loyalty are:
number of repurchases (I 56), repurchase intentions (I 55) and cross buying (I 50 - I 54)
intention to recommend (I 151) and number of recommendations (I 152)
duration and intensity of the customer relationship (I 147, I 148)
share of wallet (i.e. the percentage of a customer’s requirements of a product or service
category that are fulfilled by a particular product or service of the company; I 38 - I 41)
RFM method discussed in Section 3.5: “Recency of last purchase” (I 27), “Frequency of
purchase” (I 28) and “Monetary value” (I 29) or the
u
t
Repeat buyer (I 8)
Customers (I 7)
First-time buyer (I 6)
Brown
Prospective customers (I 1)
r
Product interesent
q
Advertising contacted
n
m
Potential buyer (I 1)
Pre-buying phase
o
Miller
Sequence buyer (I 8)
s
Z
μ regular buyer
Regular buyer (I 9)
Smith
Contacted
Intesive user
Ford
User
Light user
Potential
to contact
μ usage potential
w
v
After-buying phase
loyalty ladder, which classifies customer loyalty into different steps (m-w in Figure 53)
Usage notice
Potential
user
Usage potential
Source: adapted from [Kreutzer 1992, p. 335, Neckel and Knobloch 2005, p. 28, Krafft 2002, p. 66]
Figure 53: Loyalty Ladder
- 73 -
0
1
According to the loyalty ladder, regular customers or regular buyers (I 9) are the most loyal
customers. Most companies in business practice analyse their regular customers (see [RudolfSipötz 2001, pp. 67ff]): the main criteria for the definition of a regular customer are customer
turnover (I 31), contribution margins (I 69 - I 73), customer penetration (I 38), share of wallet (I
39),
number and rhythm of purchases (I 20; I 25; I 56) and the duration of the relationship (I 147).
To augment loyalty is essential to enrich customer relationships, e.g. by a high degree of
added-value, trust, exchange of information or cooperation ([Homburg 2001]; see Figure 54a).
Another widely used way to bind customers and improve future financial performance at the
firm level is to augment switching costs (Figure 54b). Switching costs place a technical, functional, contractual, financial, psychological or emotional burden on the customer, who intends
to switch away from the offered products or services, or from the company itself, to a competitor. Switching costs include all costs of determining the relationship to the company and setting up a new one until it provides the same benefit (see [Büschken 2004, Dikolli et al. 2006]).
a) Higher Loyalty through Enriched Relationships b) Higher Retention through Lock-in Effects
Ford
1
0
Naked
relationship
Forrester
00
1
C4) Smith
C3)
Brown
No
loyalty
Ford
1
Latent
loyalty
Forrester
00
1
μ low attachment
μ high turnover
True
loyalty
C2)
O’Connor
Improve
loyalty
C4) Smith
μ low turnover
Pseudo
loyalty
C1) Miller
Attachment (I 132)
μ few repurchases μ many repurchases
Spencer
μ satisfaction
Turnover or sales (I 31)
Repurchases (I 56)
C2)
μ dissatisfaction
d) Fuzzy Classified Loyalty/Turnover Portfolio
c) Fuzzy Classified Loyalty Portfolio
O’Connor
Forrester
Low switching costs
μ satisfaction
μ dissatisfaction
O’Connor
Ford
1
0
1
High
switching
costs
Smith
Miller
Don’t
invest
Ford
1
0
Commit
customer
C3)
Augment
turnover
Brown
0
1
μ high attachment
C1) Miller
Customer loyalty (I 134)
μ low loyalty
O’Connor
Brown
Enriched
μ low retention
Miller
Smith Brown
μ high retention
Customer loyalty or retention (I 137)
μ high loyalty
Customer loyalty (I 134)
μ low loyalty
μ high loyalty
Source: adapted from [a) Homburg 2001, p. 50; b) Büschken 2004, p. 15; c) Bauer et al. 2006, p. 132; d) Werro et al. 2005a, p. 10]
Figure 54: Examples of Fuzzy Classified Customer Loyalty Portfolios
- 74 -
In Figure 54c, fuzzy classification facilitates to identify and segment the true loyal customers
(Miller), to distinguish pseudo loyal clients (O’Connor) from more promising ones (Spencer),
latent loyal customers (Forrester) from quite loyal ones (Brown) or separate very disloyal
(Ford) from more loyal customers (Smith). With the fuzzy classified portfolio in Figure 54d, it
can be distinguished between customers to invest (Smith) or not (Ford), if loyalty (O’Connor)
or turnover (Brown) has to be improved or which customers have to be committed (Miller).
Dynamically, fuzzy classification provides valuable information by analysing, evaluating and
controlling individual effects of retention and loyalty programs or of switching barriers.
In addition, fuzzy classification enables the offering of individual and personal loyalty rewards,
like personal discounts (see Werro et al. 2005b), bonus points, special offers or presents.
Customer clubs, frequent flyer programs, communities and account or store cards are other
examples of incentives for customers to stay loyal and repurchase by the company.
5.1.9 Customer Retention
Customer
orientation
Customer
value
Customer
satisfaction
Customer
loyalty
Customer
retention
Customer
equity
Market
value
Shareholder
Customer retention (I 137), in a marketing sense, means holding on to customers [Peelen
2006, p. 239]. In contrast to loyalty, customer retention can be defined from a customer- and
from a company point of view. Retention from the customer’s point of view exits, if a customer
has certain reasons to repurchase from the company. However, customer retention is usually
defined from the company’s point of view [Homburg and Bruhn 2006, Reinecke and Dittrich
2006], and contains all actions, which lead to customer repurchases or cross-buying and avoid
that customers migrate to competitors. It can be distinguished between real and intentional
customer retention with different determinants shown in Figure 55.
Customer retention (I 137)
Real customer retention (q)
Intentional customer retention (p)
Repur- Recom- Cross- Purchase Customer Migrated Repurchase Intention to Cross-buy- Price
chases mendations buying intensity penetration customers
intention recommend ing intention premium
(I 51)
(I 61)
(I 56)
(I 152)
(I 26) (I 38, I 39)
(I 54)
(I 55)
(I 10)
(I 151)
Source: adapted from [Bruhn 2003a, p. 104]
Figure 55: Determinants of Customer Retention
As customer retention is crucial to augment customer equity and profit, CRM and marketing
require an adequate number of relevant retention indicators to manage and control different
levels of customer retention: customer attitudes (o in Figure 56), intentional customer retention (p), real customer retention (q) and the retention results (r).
- 75 -
q Real customer
retention
p
Intentional
customer retention
o
Attitude
n Investments in
customer retention
Repurchases (recency, frequency, rhythm; I 20-I 28), cross-/upbuying (I 50-I 53), share of wallet (I 39), payment behaviour
(I 66), recommendations (I 56), duration of relationship (I 147)
Repurchase intentions (I 55), cross-buying intentions (I 54),
intention to recommend (I 151), intention to switch (I 141),
intention to dialog (I 157), intention to cooperate (I 171)
Perceived product (I 118), service (I 120), relationship quality
(I 149) or price-performance ratio (I 122), customer satisfaction
(I 126), commitment (I 131), trust (I 133), image (I 115)
Customer retention costs (I 89) or total customer costs (I 102):
marketing (I 101), administration (I 93), transaction (I 94), contacts (I 100), sale (I 95), logistic (I 96) or after sales (I 98) costs
Retention in case of habitualised purchase behaviour
Customer Investment Indicators (CII)
Retention
results
Category of indicator
Customer Relationship, Information,
Recommendation and Cooperation
Indicators (CRI, CInfI, CReI, CCR)
r
Turnover (I 31), cash flow (I 30), contribution margins I-IV
(I 70 - I 73), customer gross (I 75) or net profit (I 76), customer
equity (I 79), number of customers (I 7), market share (I 43)
Customer Performance Indicator for
Revenue and Profitability (CPIP)
Controlling level of retention Indicators of customer retention
Source: adapted from [Reinecke and Dittrich 2006, p. 329]
Figure 56: Controlling Level and Indicators of Customer Retention
Investments in customer retention (n in Figure 56) can be measured easily by customer
investment indicators (e.g. retention costs; or total customer costs). However, the evaluation of
retention indicators about attitudes and intentional customer retention (e.g. repurchase or
cross-buying intentions) on the controlling level o and p, is difficult, since they are hidden,
implicit, inconstant over time and differ from customer to customer. Nevertheless, intentions
determine real customer retention (q; like the number of repurchases or cross-buying) and
the retention results (r), which can be analysed by customer performance indicators for
revenue and profitability (e.g. customer turnover or profit). In case of habitualised purchasing
behaviour, a customer who, for instance, has always bought his bread from the same bakery,
can be seen as bound, although he may have no attitudes or clear intentions.
Combining any customer retention indicators of the five controlling levels of customer retention
(o↔p/q/r; p↔q/r), fuzzy portfolio analysis enables the classification of indicators of
customer attitude, intentional or real customer retention and the evaluation of retention results.
Considering attitude/intentional retention portfolios, it is very advisable to reduce negative
attitudes (e.g. dissatisfaction) of customers towards the company, who intend to share their
negative feelings with many other persons (C2 in Figure 57a). CRM should set incentives (C3)
and maintain customers with positive attitudes and high intentional retention (C1).
However, since even very positive customer attitudes (like high satisfaction) towards the company and its products or services do not necessarily lead to higher real customer retention
(e.g. in the case of variety seeking in Figure 57b), fuzzy classified retention portfolios can be
useful to identify very delighted customers to retain (Miller) or real uncertain customers (Ford).
Intentional/real customer retention portfolios (Figure 57c) provide valuable information whether
customers really do repurchase or only intend to do.
- 76 -
0
C4)
C3)
Don’t care
and don’t
invest
Augment
intention
(set incentives)
μ dissatisfied
C4) Smith
0
Variety
seekers
(to monitor)
(to bind or not)
0
1
μ satisfied
C3)
Uncertain
customers
Ford
a) Fuzzy Attitude/Intentional Retention Portfolio
μ dissatisfied
μ satisfied
b) Fuzzy Attitude/Real Retention Portfolio
o↔p
Investments inn
customer retention
(to invest)
Brown
1
0
1
(to skim)
C1) Miller
Delighted
customers
o↔q
Attitude
o
Intentional p
customer retention
Real customer q
retention
Retention
results
p↔r
p↔q
c) Fuzzy Intentional/Real Retention Portfolio
1
0
(to skim)
(to retain)
C4)
C3)
Little
customer
retention
High customer
intentional
retention
(don’t invest)
(to realise)
0
1
μ low intention
μ high profits
C1)
High
customer
retention
Retention results: customer profits
μ low profits
C2)
High real
customer
retention
d) Fuzzy Real Retention/Results Portfolio
Repurchase intention (I 55) and
Cross-/up-buying intentions (I 54)
Repurchases (I 56) and
cross-/up-buying (I 50-I 53)
r
C1)
Acquire,
diversify,
innovate
Defend
customer
excellence
C4)
C3)
Consolidate Reduce costs
customer/ proor improve
efficiency
duct portfolio
1
0
0
1
μ high intention
C2)
Real customer retention
μ low intention
1
Maintain
intention
C2)
Locked-up
customers
C1)
Repurchases (I 56), share of wallet (I 39)
μ few repurch. μ many repurchases
C2)
Reduce intention (improve
satisfaction)
μ high intention
Intention to recommend (I 151)
μ low retention
μ high retention
Figure 57: Fuzzy Classified Portfolios of Customer Retention Indicators
Attitudes and behavioural intentions are interesting psychological constructs, but what counts
in business practice is real customer retention (i.e. many repurchases, cross-/up-buying, longterm relationships, etc.) and retention results. Possible retention results are customer turnover (I 31), contribution margins (I 70 - I 73) and customer profits (I 75, I 76). A company which
does not realise high profits in the long term is not successful, even if market shares are high
and all customers loyal (zero migration; C3 in Figure 57d). In such situations, the organisation
may have to be restructured, customer costs to be reduced or the efficiency to be increased to
achieve customer excellence (in C1). A company in a highly competitive and dynamic market
with low customer retention (C2), is forced to constantly acquire new customers, to diversify its
product or service portfolio and/or to innovate in order to retain high customer profits.
- 77 -
5.1.10 Repurchases
Customer
orientation
Customer
value
Customer
satisfaction
Customer
retention
Customer
loyalty
Customer
equity, CLV
Market
value
Shareholder
Repurchases
Satisfied, loyal or bound customers are more likely to repurchase the same products or service from a company. As a result, customer repurchases (I 56), repurchase intentions (I 55) or
the probability of repurchases (I 57) are meaningful indicators for customer loyalty and retention. The more often a customer repurchases from the company, the higher is the customer
equity or Customer Lifetime Value (CLV). To increase or retain repurchases, it is a strategic
task to maintain customer satisfaction or loyalty, and to augment the ease of purchase, e.g.
by a high availability or good distribution of products and services. As discussed in the
RFM example in Section 3.5, purchase incentives or loyalty programs may raise the number,
frequency and monetary value of repurchases. In addition, reminders, catalogues, brochures,
newsletters or e-mails can stimulate customers to buy again or more often (see Figure 58).
b)
0
Augment
repurchases
Retain
customer
C4)
C3)
Don’t
invest
Augment
monetary
value
μ very recent
C1)
0
1
Recency (of last purchase; I 27)
1
0
C1)
Augment
frequency
Retain
customer
C4)
C3)
Don’t
invest
Reactivate
customer
0
1
C2)
Frecency of purchases (I 28)
1
C2)
Number of repurchases (I 56),
probability of repurchases (I 57)
μ low value
μ high value
Monetary value (I 29)
μ long ago
a)
μ rare
μ frequent
Figure 58: Examples of Fuzzy Classified Repurchase Portfolios
5.1.11 Add-on Selling
Customer
orientation
Customer
value
Customer
satisfaction
Customer
loyalty
Customer
retention
Customer
equity, CLV
Market
value
Shareholder
Add-on selling
A strategic topic and instrument of CRM is add-on selling. Add-on selling can be defined as
“the activity associated with selling any additional products or services to current customers”
[Blattberg et al. 2001, p. 95]. Add-on selling includes down-selling (the selling of less valuable
or cheaper products or services; I 50), cross-selling (of other, similar or new products; I 51)
and up-selling (more expensive or valuable products of the company; I 53).
- 78 -
b)
High
cross-selling
potential (I 52)
C4)
C3)
0
Exploitation
μ high potential
Retention
Disinvestment
1
C1)
0
1
μ low loyalty
Cross-selling potential (I 52)
C2)
1
0
C4)
C3)
Downgrading
(to weaken
relationship)
Absorption
(to maintain
relationship)
0
1
μ high loyalty
C1)
Penetration
Upgrading
(to provide
(to strengthen
cross- & uprelationship)
selling offers)
Cross-selling (I 51)
or up-selling (I 53)
μ low potential
C2)
Customer loyalty (I 134) or
μ high potential
Customer potential (I 83)
μ low potential
a)
μ low cross-selling μ high cross-selling
Source a): adapted from [Bruhn and Georgi 2006, p. 47]
Figure 59: Examples of Fuzzy Classified Add-on Selling Portfolios
By analysing customer loyalty (I 134) or retention (I 137), the potential for turnover or profit (I 83),
customers needs, the purchased product mix by the company or by the competitors (I 58) and
the purchase behaviour in general, the cross-selling potential (I 52) can be evaluated.
For promising customers or customer segments with high cross- or up-selling potential (C1 in
Figure 59a; C1 and C2 in Figure 59b), customer penetration strategies (to provide offers) or
upgrading strategies (to strengthen relationships) have to be formulated and implemented.
If customers do not buy many different products and have rather low cross-buying potential,
customer relationships should be managed less cost-intensive (C4: downgrading).
5.1.12 Share of Wallet
Customer
orientation
Customer
value
Customer
satisfaction
Customer
loyalty
Customer
retention
Customer
equity, CLV
Market
value
Shareholder
Share of wallet
The customer performance indicator share of wallet (I 39) measures the share of the customer’s expenditures for a specific product or services in relation to the total expenditures.
Following example explains the idea of the share of wallet: if a customer purchases the total
amount of a product A by the company 1 in period 1, he has a maximum share of wallet of
brand A (see Figure 60a). In period 2, the customer may switch to brand B by the company 2.
He has now a maximum (100%) share of wallet of brand B, and a minimal one (0%) of A.
However, the customer does not necessarily choose A or B ("crisp" choice in Figure 60a), but
rather choose partially A (10%), partially B (80%) and partially C (10%). This example of [Rust
et al. 2000] shows: the idea of fuzzy logic can be easily applied to the concept of share of wallet to analyse the "fuzzy" choices of a customer. As exemplified in Figure 60b, in period 2 the
share of wallet of brand B was increasing and this one of brand A decreasing.
- 79 -
a) "Crisp" (Sharp) Choice
Maximum share of wallet
of brand A (by company 1)
Brand A
Brand B
0%
A
B
Period 1
Period 2
Period
100%
Maximum share of wallet
of brand B (by company 2)
b) "Fuzzy" Choice (share of wallet)
Increasing share of wallet
of brand B (by company 2)
Brand A & B
100%
B
B
A
0%
Period 1
Period
60%
Brand C
C
A
Period 2
Decreasing share of wallet
of brand A (by company 1)
Source cartoon: [Rust et al. 2000, p. 43]
Figure 60: Crisp (a) and Fuzzy (b) Choice
It might be interesting to compare the share of wallet fuzzily with the level of customer penetration (I 38; the percentage of all demanded products bought from the company) to define
penetration or add-on selling strategies (in Figure 61a). Since a company is very dependent (I
136)
of a customer with a high market share (I 42), it has to follow intensive customer acquisi-
tion and penetration strategies in order to increase the own market share (see Figure 61b).
b)
Retain
customer
C4)
C3)
Penetrate
customer
1
0
C1)
Add-on
selling
1
0
1
Market share of customer (I 42)
0
C1)
Intensive
retention
C4)
C3)
Moderate
acquisition
penetration
Moderate
retention
0
1
μ low share of wallet μ high share of wallet
C2)
Intensive
acquisition and
penetration
μ low share of wallet μ high share of wallet
Figure 61: Examples of Fuzzy Classified Share of Wallet Portfolios
- 80 -
Share of wallet (I 39), or
customer penetration (I 38)
C2)
Augment
share of wallet
(of product X)
Share of wallet (I 39)
μ low penetration μ high penetration
μ low market share μ high market share
a)
5.1.13 Turnover
Customer
orientation
Customer
value
Customer
satisfaction
Customer
retention
Customer
loyalty
Customer
equity, CLV
Market
value
Shareholder
Turnover, sales
and cash flows
According to empirical studies, the financial indicators customer turnover, sales or revenues
(I 31) are regularly measured in most of the companies (see for instance [Reinecke 2004]).
This kind of data can be gathered easily from the accounting or marketing department or is
available in an information system.
To classify customer turnover sharply is problematic, since only small changes of turnover or
sales may cause different classifications of customers.
However, a fuzzy classification of customer turnover can be one- or multidimensional. The
fuzzy classification in Figure 62a, described by [Meier et al. 2005], combines the attribute
turnover with the ‘punctuality of payment’ (I 63) and defines four basic strategies.
In Figure 62b, a dynamic fuzzy classification with the attribute ‘cumulative turnover’ (I 34) and
the negative or positive ‘growth of turnover’ (I 35) is considered. A sharp classification would
be a mistake, as the examples of Smith and Brown show again; although they have approximately the same values, Brown is labelled as a ‘top’ (C1) and Smith as a ‘flop customer’ (C4).
Fuzzily, the customers are named and managed more sophisticated and manifold.
b)
Commit
customer
C4)
C3)
Don’t
invest
1
0
C1)
Augment
turnover
μ unpunctual
C1)
Top
Customers
(to maintain)
Miller
Brown
Flop
customers
(not to invest)
C3)
Churn
customers
(to recover)
Ford
0
0
1
μ punctual
C2)
Growth
customers
(to invest)
C4) Smith
1
0
1
Growth of turnover or sales (I 35)
Cumulative turnover (I 34)
μ low turnover
C2)
Improve
punctuality
of payment
Punctuality of payment (I 63)
μ high turnover
Turnover (I 31)
μ high neg. growth μ high pos. growth
a)
μ low cum. turnover μ high cum. turnover
Source: a) adapted from [Meier et al. 2005, p. 20]
Figure 62: Examples of Fuzzy Classified Turnover Portfolios
The evaluation of customer’s turnover or revenue does not allow making any statement about
the customer’s costs or profitability. An isolated consideration of the customers’ monthly or
annual turnover may lead to misinterpretations and has to be supplemented with the following
discussed indicators: customer’s contribution margins and customer gross or net profit.
- 81 -
5.1.14 Contribution Margins
Customer
orientation
Customer
value
Customer
satisfaction
Customer
retention
Customer
loyalty
Customer
equity, CLV
Market
value
Shareholder
Contribution
margins
Since cost-intensive customers with high turnover are not necessarily profitable, customer
contribution margin accounting is required to determine the profitability of the customers.
The main problem in doing so is to identify, allocate and to trace total customer costs (I 102),
such as administration (I 93), transaction (I 94), sales (I 95), logistic (I 96), service (I 97), after
sales (I 98), communication (I 99), contact (I 100) or marketing (I 101) costs. General or overhead
costs which are not, or only partly traceable to a customer, are not accounted in the example
of multi-level customer contribution margin accounting shown in Figure 63a, nevertheless they
should be. However, customer-related allocation of costs is necessary in order to calculate the
profitability of a customer. Therefore, a customer-oriented calculation of direct costs is useful.
With the calculation scheme shown in Figure 63a, four different customer contribution margins I (I 70), II (I 71), III (I 72) and IV (I 73) can be calculated and evaluated fuzzily.
= Customer net sales revenue
– Costs of goods/services sold
= Customer contribution margin I
– Customer-driven marketing costs
(I 101; e.g. mailings, calls, trials, card)
= Customer contribution margin II
– Customer-driven sales costs (I 95;
e.g. sales force, ordering, billing)
= Customer contribution margin III
– Customer-driven service and logistic
costs (I 96, I 97, I 98); e.g. after sales
service, delivery of goods, helpdesk)
= Customer contribution margin IV
High
With fuzzy classification, customers
can be better analysed and segmented by their contribution margin
500
200
Medium
750
Brown
Miller
Turnover (I 31; in €)
– customer-related revenue reductions
(e.g. volume discount, cash discount)
1000
100
50
Smith
Low
Customer gross sales revenue
per period
≥1250
0
-50
-100
-150
Negative
a)
Calculation Scheme for
Customer Contribution Margins
Customer contribution margin I-IV (I 73; in €)
μ negative contribution margin μ positive contribution margin high contribution margin
b)
Correlation between
Annual Contribution Margin I IV and Turnover per Customer
Ford
-200
1
Low
0
0
250
500
Medium
750
1000
High
1250 1500 3000 6000 9000
0
1
μ low turnover
Source calculation scheme: adapted from [Bruhn and Georgi 2006, p. 40, Bauer et al. 2006, p. 173]
Figure 63: Customer Contribution Margin Accounting
- 82 -
μ high turnover
As shown in Figure 63b, Ford and many other customers with low turnover have a lightly
negative contribution margin. This means that a high share of the customers is not profitable.
Usually, the contribution margins and their deviation rise with increasing turnover. Since Miller
causes higher costs, he is less profitable than Mr. Brown, despite higher turnover. A detailed
margin accounting can provide important evidence for different customer costs and customer
profitability and therefore for the evaluation of customer attractiveness (I 82).
In Figure 64, four customer contribution margins portfolios give valuable information about
customer costs of marketing (I 101), sales (I 95), service (I 97) or logistic (I 96), about the level of
the customer contribution margins I to IV (I 70 - I 73), and about simple strategies.
a) Fuzzy Contribution Margin I Portfolio
Customer net sales revenue (I 31)
b) Fuzzy Contribution Margin II Portfolio
C3)
1
0
1
0
1
1
C4)
C3)
Consolidate
product or customer portfolio
Reduce
sales costs
(I 95)
00
1
μ low margin II
μ low margin IV μ high margin IV
C1)
Retain
customer and
product
C3)
Consolidate
product or cusomer portfolio
Reduce
marketing
costs (I 101)
μ low margin I
μ high margin I
Contribution margin IV (I 73)
Contribution margin II (I 71)
μ low margin III μ high margin III
1
C2)
C4)
d) Fuzzy Contribution Margin IV Portfolio
Contribution margin III (I 72)
Increase
contribution
margin II
C1)
Retain
customer and
product
00
μ low sales revenue μ high sales revenue
c) Fuzzy Contribution Margin III Portfolio
C2)
Increase
contribution
margin I
Contribution margin I (I 70)
C4)
Consolidate Reduce costs
product or cus- of goods or
stomer portfolio
services
μ high margin II
C1)
Retain
customer and
product
1
C1)
Increase
contribution
margin III
Retain
customer and
product
C4)
C3)
Consolidate
Reduce
product or cus- service/logistic
omer portfolio costs (I 97,98)
00
1
μ high margin II
C2)
μ low margin III
Contribution margin III (I 72)
μ low margin I
C2)
Increase
turnover
or sales
Contribution margin II (I 71)
μ low margin II
μ high margin I
Contribution margin I (I 70)
μ high margin III
Figure 64: Fuzzy Classified Customer Contribution Margins Portfolios
In addition, fuzzy classification is useful for identifying and separating valuable customers with
high contribution margins from unattractive ones with low or lower margins, and for managing
these customers adequately by CRM and marketing.
- 83 -
5.1.15 Profitability
Customer
orientation
Customer
value
Customer
satisfaction
Customer
loyalty
Customer
retention
Customer
equity, CLV
Market
value
Shareholder
Profits
According to [Kotler et al. 2005, p. 474], a profitable customer is a person, whose revenues
(I 31) over time exceed the company’s costs of attracting (I 87), selling (I 95) and servicing (I 97)
that customer. Customer gross profit (I 75) is defined here as the customer’s total turnover (I
31)
minus total customer’s costs (I 102). Customer net profit (I 76) additionally considers taxes,
interests, depreciations or other expenses. ‘Customer profitability’ (I 78) and ‘customer retention’ (I 137), both measured either at a customer, customer segment or at the enterprise level,
and the ‘number of customers’ (I 7) can be linked to a multidimensional fuzzy classification (in
Figure 65b). Depending on the profitability and retention of a customer (segment), and on the
number of customers, Figure 65a defines the directions of growth. In addition, the development (growth) of all indicators are classified dynamically and fuzzily in Figure 65c.
C3)
low retention high retention
high
number
low
number
Customer retention
C1) Exploit channel or
C5) Defend
marketing opportunities
C2) Offer innovation, cut C6) Improve customer
price, bind customers loyalty or swichting cost
C7) Reduce customer
C3) Consolidate
costs, raise price
product portfolio
C4) Restructure
C8) Consolidate
business
customer base
Source strategies of a): adapted from [Plaster and Alderman 2006, p. 3]
Description of the Development 2005-2007
n In 2005, the retention rate of the company
o
p
q
r
has decreased and the company lost many
customers, although profits increased.
CRM reacts and follows fuzzily strategy C2):
prices are reduced and loyalty programs
launched in order to reduce to churn rate.
Due to lower prices, profits decreased in
2006. However, customer retention and the
number of customers strongly increased.
To raise profits again, management reduces
production, marketing and customer costs.
As a result, profits were increasing again in
2007 and the number of customers and
customer retention remain high.
C6)
C5)
C2)
C1)
C8)
1
Number of
customers (I 7)
C7)
Number of
customers
μ high profits
C8)
C4)
Customer profitability (I 78)
C5)
C1)
C7)
C4)
μ low profits
C2)
b) Fuzzy Classification of Customer Profitability
C3)
μ high
number
μ low
number
00
Customer retention
(I 137)
μ low retention rate μ high retention rate
c) Dynamic Fuzzy Classification of Growth
Development of
customer profitability (I 77)
μ declining profits μ increasing profits
low profits high profits
Customer
profitability C6)
1
r
n
2005
2005
o
2007
2007
q
2006
2007
2006
2005
00
2005
2007
μ decreasing rate
2006
μ increasing
number
μ decreasing
number
Development of the
customer retention
μ increasing rate rate
Figure 65: Fuzzy Classification of Customer Profitability
- 84 -
2006
p
Development
of the number
of customers
a) Defining the Directions of Growth
To augment the number of customers, the company should exploit channel or marketing opportunities in the case of high customer retention and profits (C1 in Figure 65a) or offering
innovations, cutting the prices and bind customers, if customer retention and the number of
customer is low (C2). If profits are low despite high retention and a high number of customers
(C7), customer costs have be to reduced and prices to be raised. If the number of customers
(C3) or customer retention is low as well (C8), the product portfolio or customer base has to be
consolidated. Businesses have probably to be restructured, if all three indicators are low (C4).
Using these three strategic customer indicators and fuzzy classification, the performance of a
customer, of a fuzzy segment or of the whole company can be classified precisely in order to
define and implement the different strategies of growth.
However, the company has different opportunities to achieve customer growth. For instance, it
can either follow the profit maximising growth strategy, the market penetration or the customer retention strategy, or all strategies simultaneously (see Figure 66). The company has
to choose the strategy or axis that provides the best opportunity for profitable growth. Understanding fuzzily the size and shape of the cubes, CRM has to follow these growth strategies
with the best future theoretical state ("could be") and practical condition ("should be").
The main advantage of this approach of customer strategy definition and implementation is
that it can be applied with any kind of strategic customer indicators, which can be dynamically
and fuzzily analysed, evaluated and controlled by managers or by CRM software.
a) "All-in-One" Customer Growth Strategy
b) Customer Profit Maximising Growth Strategy
Future state
"should be" operations
Current
state
Number of
customers
Customer retention
All-in-one
strategy
Customer profitability
Profit-maximising strategy
Future state
"could be" operations
Current
state
Number of
customers
Current
state
Number of
customers
Number of
customers
Customer
profitability
Customer retention
Current
state
Customer retention
Customer retention strategy
Customer retention
Source: a) adapted from [Plaster and Alderman 2006, p. 2]
c) Market Penetration Growth Strategy
d) Customer Retention Growth Strategy
Figure 66: Customer Growth Strategies
- 85 -
5.1.16 Customer Equity and Customer Lifetime Value (CLV)
Customer
value
Customer
orientation
Customer
satisfaction
Customer
loyalty
Customer
equity, CLV
Customer
retention
Market
value
Shareholder
Literature on customer equity increased quickly during the last years. However, there is no
consistent definition of customer equity (see [Cornelsen 2000, Rudolf-Sipötz 2001, Blattberg et
al. 2001, Rust et al. 2000, 2004, 2005, Rogers and Peppers 2005, Bauer et al. 2006, Reichold
2006, Günter and Helm 2006, Bejou and Gopalkrishnan 2006, Aksoy et al. 2007, Krafft 2007]).
Customer equity can be defined as a synonym of Customer Lifetime Value (CLV; I 80): “A
firm’s Customer Equity is the total of the discounted lifetime values of all of its customers”
[Rust et al. 2000, p. 4]. Since it is difficult or even impossible to forecast or discount future customer cash flows, customer equity is here not understood as a discounted or a dynamic value.
Customer equity (I 79), which can be considered as “the satisfaction of company with a customer” [Palloks-Kahlen 2006], is defined here as the current total monetary and non-monetary
economical value of a customer for a company.
Customer equity management is “a dynamic, integrative marketing system that uses financial valuation techniques and data about customers to optimise the acquisition of, retention of,
and selling of additional products to a firm’s customers, and maximise the value to the company of the customers relationship throughout its life cycle” [Blattberg et al. 2001, p. 3].
Considering customer performance indicators of revenue and profitability, customer equity or
CLV is higher with increasing customer (cumulative) turnover, contribution margins or net
profit and customer retention. Figure 67a fuzzily segments customers with high, medium or
low equity. As an aggregate, the total customer equity affects market value and market share,
as well as the firm value and the shareholder value. If the customer equity share is low, the
C3)
0
0
1
Augment customer
C4)
Low
Medium
customer customer equity
equity (/CLV) (short-term)
Market share (I 43,I 44)
μ high share
C1)
Cumulative turnover (I 34)
or profits (I 76)
μ low retention μ high retention
C2)
Medium
High
customer equity customer
(long-term)
equity (/CLV)
1
b)
μ low share
a)
1
0
C1)
Declining
company
Healthy
company
C4)
C3)
Sick
company
Growing
company
0
1
μ low cum. turnover μ high cum. turnover
C2)
Customer equity (share; I 79)
market share of a company declines or rests on a low level (see Figure 67b).
μ low equity share μ high equity share
Source: b) adapted from [Rust et al. 2000, p. 162]
Figure 67: Examples of Fuzzy Classified Customer Equity Portfolios
- 86 -
However, to augment customer equity (and market share/value), the company has to analyse
on which customers it should focus and which customers should be bound in order to establish long-term, profitable relationships. The company has to evaluate all customers who are
attractive, using, for instance, fuzzy portfolio analysis.
The customer attractiveness/equity portfolio discussed by [Palloks-Kahlen 2006] consists
of two dimensions (see Figure 68): the vertical axis indicates the level of ‘customer satisfaction
with the company’ (I 126), or customer value (I 124) in a broader sense. In addition, the horizontal axis describes the ‘satisfaction of the company with a customer’, which can be seen as
customer attractiveness (I 82) or customer equity (I 79), measured, for instance, by the customer contribution margins I-IV (I 70 - I 73). The six classes C1-C6 reflect different types of customers: regular buyers (I 9; C1 in Figure 68;) are convinced and profitable customers to maintain and suited for establishing long-term relationships. Satisfied customers (C5) in the "corridor for active CRM and customer retention" are to be developed by augmenting satisfaction
and/or contribution margins. CRM, customer development and customer retention should focus on promising customers (of C2) in order to turn them into regular buyers.
Customer satisfaction (I 126) or value (I 124)
C2)
Promising
customer
Ford
μ low
Disappointed
and unprofitable
customer
Direction of
differentiated
customer
development
Smith
C3)
Profitable
absorption
customer
high
μ medium
C5)
Satisfied, but
unprofitable
customer
Corridor for
active customer
retention (CRM)
C4)
Convinced, but
unprofitable cu.
C1)
Regular
buyer
medium
Area of
long-term
relationships
C5)
Satisfied but
unprofitable cu.
C2)
Promising
customer
low
μ high
C1)
C4)
Convinced, but
Regular buyer
unprofitable
Miller
customer Brown
C6)
1
Customer satisfaction (or value)
C6)
C3)
Disappointed & Profitable
unprofitable cu. absorption cu.
low
high
Contribution margin I-IV
Customer attractiveness (equity)
(satisfaction of a customer with the company)
Source: adapted from [Palloks-Kahlen 2006, p. 302]
Customer equity (I 79)
00
(satisfaction of the company with a customer)
measured by: contribution margins I-IV (I 70 - I 74)
1
μ low contribution margin μ high contribution margin
Figure 68: Fuzzy Classified Customer Satisfaction/Equity Portfolio
To distinguish sharply customers of these different classes is a problem, once again: why
should Brown be labelled as ‘convinced but unprofitable customer’, but Miller as ‘regular
buyer’, although they have nearly the same values? In addition, Ford should not be considered
and managed just as ‘disappointed and unprofitable customer’ and Smith not only as ‘promising customer’. With fuzzy classification, the transitions between the different classes and CRM
strategies become fluent and customer are mostly mixes of different classes and they can
therefore be evaluated, developed and managed according to their real measured values.
- 87 -
However, different drivers influence customer equity, and not only customer turnover or contribution margins. [Blattberg et al. 2001], for instance, define three drivers of customer equity:
value equity (driven by quality, price, convenience), brand equity (determined by customer
brand awareness, attitude, brand ethics) and retention equity (driven by loyalty, affininity,
special recognition, treatment programs and others).
An interesting conceptualisation and operationalisation of customer equity is discussed by
[Rudolf-Sipötz 2001]. Using a factor analysis method, different determinants and customer
performance indicators of the concept customer equity were empirically tested.
The indicators and the different determinants (categories of potential) of customer equity are
shown in Table 15. To improve customer performance and customer equity, diverse measures
and actions of operational customer equity management can be implemented.
Table 15: Determinants and Indicators of Customer Equity
Market potential of customer
Resource potential of customer
Customer equity
Determinant Customer Performance Indicator
Customer contribution margin II (I 71)
Profit
Customer contribution margin III (I 72)
potential
Customer contribution margin IV (I 73)
Development of sales/turnover (I 35)
Development
Potential of contribution margins (I 84)
potential
Probability of repurchases (I 57)
and
Phase in customer lifetime cycle
Product portfolio by competitor (I 58)
Cross-buying
Need for diversification
potential
Cross-buying intention (I 54)
Loyalty
Trust (I 133)
potential
Duration of relationship (I 147)
Expertise for cooperation (I 172)
Intention to cooperate (I 171)
Cooperation Cooperation behaviour
potential
Potential cooperation topics (I 173)
Lead user
Product expertise (I 168)
Intention to recommend (I 151)
Number of recommendations (I 152)
Reference
potential
Recommendation intensity (I 153)
Potential reference recipients (I 155)
Role as opinion leader (I 156)
General intention to dialog (I 157)
Information
Number of complaints (I 164)
potential
Response rate (I 169)
Number/quality of suggestions (I 164)
Synergy
Compound effect in customer base
potential
Actions of Equity Management
¨ Improvement of customer efficiency (I 108)
(e.g. reduction of customer costs)
¨ Analysis of customer’s product mix (I 58)
¨ Analysis of needs and demand (I 117)
¨ Analysis and enhancement of
customer value (I 124) and value drivers
¨ Analysis and enhancement of addedvalue, e.g. customising or personalisation
¨ Product and service bundling
¨ Diversification and innovation management
¨ Customer loyalty and retention programs
¨ Customer care and consulting (I 158)
¨ Customer relationship investments (I 87ff)
¨ Strategic alliances and networks
¨ Partnerships
¨ Lead user programs
¨ Cooperation programs (e.g. for R&D)
¨ Compatible information systems
¨ Suggestion boxes, etc.
¨ Member-get-member-/tell-a-friend-program
(incentives for recommendations)
¨ Information and public relations
¨ Satisfaction and complaint management
¨ Customer surveys and questionnaires
¨ Hotline, call center, help/information desk
¨ Group discussions, conferences
¨ User groups
¨ Customer communities, clubs, cards, etc.
¨ Economics of scales (e.g. in R&D)
¨ Internal coordination (e.g. centralisation)
Source: adapted from [Rudolf-Sipötz 2001, p. 95, 170, 186]
[Rudolf-Sipötz 2001, pp. 192ff] undertakes a sharp classification to segment customers by
defining a customer cube with the attributes ‘actual market potential of customer’ (i.e. the
total direct, monetary value of a customer for the company), ‘future market potential’ and ‘resource potential’ (the total indirect, non-monetary value of a customer for a company).
In Figure 69b, the customer equity cube is adapted to a three-dimensional fuzzy classification.
- 88 -
C6)
C2)
C5)
C1)
C8)
C4)
C7)
C3)
low actual
potential
Future market
potential of customer
high actual
potential
high
future potential
low
future potential
Actual market
potential of customers
C1) Take along customers C5) Blue-chip-customers
to augment share of wallet
to invest and maintain
C2) Selective customers
C6) Perspective customer
Don’t invest, but to maintain
to invest and augment profit
C3) Absorption customers C7) Potential customers to
to skim, reduce costs
invest & increase commitment
C4) Renunciation custom.
C8) Future customers
Don’t invest, reduce costs
Resource
potential
C6)
C2)
Future market
low
resource
potential
high
resource
potential
Resource
potential
μ low resource potential μ high resource potential
b) Fuzzy Classification of Customer Equity
a) Customer Segments and Strategies
C5)
C1)
C8)
C4)
C7)
C3)
μ high
future
potential
μ low
future
potential
1
0
Actual market
0
μ low actual potential μ high actual potential
to augment turnover
Source: adapted from [Rudolf-Sipötz 2001, pp. 192ff]
Figure 69: Three-Dimensional Fuzzy Classification of Customer Equity
Classified fuzzily, the transitions between the classes and customer segments become fluent
and the strategies of Figure 69a have to be adapted, aligned and personalised accordingly.
Another, dynamic method to determine customer equity is the calculation of Customer or
Prospect Lifetime Value (CLV, I 80; PLV, I 81). CLV portfolios should also consider monetary
and non-monetary criteria, as shown in Figure 70a. Monetary CLV can be calculated by the
formula of Figure 70c, qualitative criteria by a fuzzy scoring model or a cost-benefit analysis.
C2)
C1)
To bind
To commit
Miller
μ low CLV
C4) Brown
Don’t invest
C3) Smith
To monitor
Ford
1
0
0
1
c)
b)
μ low retention
PLV (Prospect Lifetime Value; I 81)
1
C1)
Low
investment
in prospect
C4)
C3)
Medium
investment
in prospect
No
investment
in prospect
0
1
μ high retention
Monetary criteria
Customer
Lifetime
Value
(CLV)
0
C2)
High
investment
in prospect
Intention to switch (I 141) or
switching probability (I 142)
non-monetary)
μ high PLV
monetary;
μ low PLV
μ high CLV
CLV (I 80;
Customer loyalty (I 134) or
customer retention (I 137)
a)
μ low probability to switch
μ high probability
(Rt − Et )
t
t =0 (1 + i )
n
Revenues: customer turnover (I 31), cum. turnover
(I 132), cash flow (I 30), monetary value (I 29); etc.
Expenses: acquisition (I 87) retention (I 89), service
(I 97), marketing (I 101), total customer (I 102) costs
Non-monetary criteria
Fuzzy Customer recommendation (I 151-I 156), information
scoring model (I 157-I 170) and cooperation (I 171-I 173) indicators
CLV = ∑
Rt = Total customer revenues
(cumulated customer turnover; I 34)
Et = Total customer expenses
(total customer costs; I 102)
t = Duration of customer relationship (I 147)
i = Discount rate
Figure 70: Fuzzy Classified Customer (a) and Prospect (b) Lifetime Value Portfolios
- 89 -
[Rust et al. 2000, p. 191] define a four-tier system to classify customer equity (see Figure 71):
The platinum tier describes the company’s most profitable customers (I 78), typically those
who are heavy and regular users (I 22) of the product, are not overly price sensitive (I 60), are
willing to invest in and try other or new offerings (I 51), and are committed (I 131) or loyal (I 134)
customers of the company. The gold tier differs the platinum tier in that profitability levels are
not as high, perhaps because the customer want price discounts (I 37) that limit the contribution margin I to IV (I 70 - I 73) or they are not as loyal. The iron tier contains essential customers who provide the volume needed to utilise the company’s capacity, but their spending levels, loyalty and profitability are not substantial enough for special treatment. The lead tier
consists of customers who are costly for the company. They demand more attention than they
are due given their spending and profitability and are sometimes problem customers, complaining about the firm to others (I 155), tying up the firm’s resources (I 102).
Brown
Gold
What (fuzzy) segment spends
more with us over time, costs
less to maintain and spreas
positive word of mouth?
Smith
Iron
Least profitable
customers
Smith
Ford
Lead
Miller
Brown
Ford
What (fuzzy) segment costs
us in time, effort, and money
yet does not provide the return
we want? What fuzzy segment
is difficult to do business with?
μ least profitable
Platinum Miller
μ most profitable
b)
a)
Most profitable
customers
0
1
Source a): [Rust et al. 2000, p. 193]
Figure 71: Sharp (a) and Fuzzy Classified (b) Customer Equity Pyramid
However, it is problematic to label customers sharply just as ‘platinum’, ‘gold’, ‘iron’ or ‘lead’,
since the definitions of these classes are arbitrary. When is a client exactly a platinum customer (Miller in Figure 71a) and when a golden one (Brown)? What about customers who are
classified sharply in the same tier, although their customer equity is quite different (Brown and
Smith)? Why should iron Ford, who is as profitable as Smith, not be treated specially as Smith
is? Such questions are quite difficult to answer.
Using fuzzy classification, the transitions between the four tiers becomes fluent and customers
can partly belong to more than one tier at the same time. Miller and Brown are both an "alloy"
of platinum and gold, and also Smith and Ford are partially golden and iron at the same time.
Defining only two terms ‘least profitable’ (or iron) and ‘most profitable’ (or gold) customers in
Figure 71b, the customers can be classified fuzzily in the customer equity pyramid.
The fuzzy classified customer equity pyramid has a more neutral and proper semantic and the
customers are no longer privileged or discriminated.
- 90 -
5.2
Fuzzy Market Segmentation
Just as customers, buyers of a whole market differ in their wishes, resources, locations, buying
attitudes and buying practices. Through market segmentation, companies divide large, heterogeneous markets into smaller segments of buyers with different needs, characteristics or
behaviour, who might require separate products or marketing mixes [Kotler et al. 2005, p. 391].
In contrast to mass marketing, where the same marketing mix is used for all consumers and no
market segmentation is undertaken, segment marketing adapts the marketing mix to segments. Niche marketing, in turn, focuses on subgroups (niches) within these segments, where
often is little competition. In the case of complete market segmentation, micromarketing,
products and marketing programmes are tailored to the needs and wants of narrowly defined
geographic, demographic, psychographic or behavioural segments. In the extreme, micromarketing becomes individual marketing that means tailoring products and marketing programmes to the needs and preferences of individual customers [Kotler et al. 2005, p. 395].
Individual marketing has also been labelled ‘markets-of-one marketing’ or ‘one-to-one marketing’ as discussed in Section 4.1.3.
Figure 72a and b show the two extremes, ‘no segmentation’ (mass marketing) and ‘atomistic
segmentation’ (individual marketing) of a market with six consumers. Considering three income
classes (1: ‘low’, 2: ‘medium’, 3: ‘high’ in Figure 72c), the market is segmented into three segments of different sizes. Three consumers have a ‘low’ income (1), one has a ‘medium’ (2) and
two have a ‘high’ income (3). In addition, the market can be segmented by the age of the consumers (Figure 72d; A: ‘young’, B: ‘old’) into two segments. The use of both criteria, income
and age, divides the market into five market segments (1A, 1B, 2B, 3A and 3B in Figure 72e).
a) No segmentation
(mass marketing)
b) Atomistic segmen- c) Segmentation of income d) Segmentation of
tation (individual
marketing)
groups (1: ‘low’, 2: ‘medium’, 3: ‘high’ income)
1
2
e) Segmentation of
age groups (A;
‘young, B: ‘old’)
income and age
groups
B
1A
B
1A
A
1
3
1
3B
A
3
1B
B
2B
A
3A
Source: adapted from [Kotler and Bliemel 2001, p. 417]
Figure 72: Sharp Market Segmentation
Market segments can be defined in many different ways. The different market segments shown
in Figure 72 are demographic segments. According to [Kotler and Keller 2005, p. 241], another
way to curve up a market is to identify preference segments. Three different patterns of preferences can emerge: homogeneous, diffused or clustered preferences (compare Figure 73).
- 91 -
c)
b)
a)
a) Homogeneous preferences: market with
no natural segments: all consumers have
same preferences.
b) Diffused preferences: consumers of this
market vary greatly in their preferences,
and are scattered throughout the space.
c) Clustered preferences: market with three
distinct preference clusters, so called
natural market segments.
Source: adapted from [Kotler and Keller 2005, p. 242]
Figure 73: Basic Market-Preferences Patterns
However, the patterns in Figure 73 are theoretical and unrealistic illustrations. There are usually no homogeneous preferences in real markets and it is also rarely the case that markets
have clear, distinct and homogeneous clusters of different preferences. Real preferences
mostly range somewhere in between homogeneous, diffused and clustered preferences.
The sharp market segmentation in demographic segments shown in Figure 72 is also a problem. This approach assumes that it can be clearly distinguished between consumers with a
‘low’ (1), ‘medium’ (2) or ‘high’ income (3), and between ‘young’ (A) and ‘old’ (B) consumers.
However, how is a ‘low’, ‘medium’ or a ‘high’ income defined and when is a consumer
‘young’ and when ‘old’? Responses to these questions are usually sharp classifications
shown Figure 74: people between 0 and 39 years are ‘young’, ‘middle-aged’ are between 40
and 59, and people over 60 years are ‘old’. An income between 0 and 2499 € is defined as
‘low’, a ‘medium’ income ranges between 2500 and 4999 €, and people with a ‘high’ income
earn more than 5000 €. Although Miller and Brown have nearly the same age (Miller 38 years
and Brown 41) and same income (Miller: 4920 € and Brown: 5100 €), Miller is classified as
‘young’ and his income as ‘medium’, but Brown as ‘middle-aged’ and his income as ‘high’.
μ young
Under 40
1
...
C1)
C2)
Smith
70
65
60
55
C6)
C4)
C5)
50
45
Brown
40
35
C9)
C7)
C8) Miller
Ford
20
0
1000
2000
under 2'500
3000
4000
5000
2’500 - 4'999
6000
40
39
C3)
Smith
C2)
C1)
C6)
C5)
C4)
Brown
C9)
20’000
over 5'000
C8) Miller C7)
0 Ford
0
2499 2500 4999 5000
low
30
25
60
59
medium
Income
(gross income
per month, in €)
0
1
μ low income
μ medium income
μ high income
Figure 74: Fuzzy Market Segmentation of Income and Age
- 92 -
Income
C3)
old
75
middleaged
40-59 years
μ middle-aged
Over 60
80
Age
young
μ old
Age
…
This example points out: it does not make sense to classify the age and income of a person
sharply: why should a 39 year old person be labelled ‘young’, but a 40 year old one suddenly
‘middle-aged’? Why should a ‘middle-aged’ person of 59 years be classified abruptly as ‘old’
with 60? The transitions between the phases in one’s life are fluent. The membership functions in Figure 74 reflect mathematically these fluent transitions between the phases. With
fuzzy classification, a person can be classified as ‘young’ and ‘middle-aged’ (respectively as
‘middle-aged’ and as ‘old’) at the same time, and the income can be categorised partially as
‘low’ and partially as ‘medium’ (respectively as ‘medium’ and as ‘high).
However, not only socio-demographic market segmentation but also other types and categories of segmenting criteria could or should be classified fuzzily. According to [Kotler et al.
2005, pp. 398ff], consumer markets can be segmented by geographic, demographic, psychographic and behavioural market segmentation (see Appendix 5, p. 141):
Geographic segmentation calls for dividing the market into different geographical units,
such as nations, states, regions, counties, cities or neighbourhoods.
Demographic segmentation consists of dividing the market into groups based on variables
such as age, gender, sexual orientation, family size, family life cycle, income, occupation
education, religion, ethnic community and nationality.
Psychographic segmentation divides buyers into groups based on social class, lifestyle or
personality characteristics.
Behavioural segmentation divides buyers into groups based on their knowledge, attitudes,
uses or responses to a product or service, or based on other criteria of purchase behaviour.
As shown in Appendix 5, most of the market segmentation variables are adequate for fuzzy
classification with discrete or continuous membership functions. Especially behavioural segmentation variables are promising to define fuzzy market segments, marketing strategies and
0
C1)
Fuzzy segment 2 (fS2);
Strategy B
Strategy A
C4)
C3)
Strategy D
Strategy C
0
1
fS3; Strategy C fS1; Strategy A
fS2;
Strategy B
fS5;
Strategy E
1
0
0
1
μ low variable X
μ high variable X
μ low variable X
Figure 75: Fuzzy Market Segments and Strategies
- 93 -
fS4;
Strategy D
μ high variable X
Market segmentation variable X
1
C2)
Market segmentation variable Y
μ low variable Y μ high variable Y
μ low variable Y μ high variable Y
Market segmentation variable Y
Market segmentation variable X
marketing mixes. Figure 75 shows a general example how to define fuzzy market segments.
Chapter 6
Fuzzy Credit Rating
- 94 -
Chapter 6: Fuzzy Credit Rating
6.1
Methods of Sharp Credit Rating
6.1.1 Definitions
Another example of customer or market segmentation is the process of credit rating, which
ends in a rating of the rating subject (for instance a loan applicant) in a rating or risk class.
To manage the credit business profitably, a bank has to classify loan applicants according to
their real creditworthiness (I 68), that means according to their default risk. It is in every bank’s
concern to evaluate loan applicants and their creditworthiness as good as possible.
Creditworthiness is the ability, intention and financial capability of a borrower to repay debt.
Credit scoring is a quantitative approach used to measure and evaluate the creditworthiness
of a loan applicant [Hofstrand 2006]. An aim of credit scoring is to determine the credit risk,
that risk assumed for the possible nonpayment of credit extended.
In Anglo-Saxon literature on credit scoring, often the C’s of credit are mentioned, e.g. Character, Capital, Capacity, Collateral and Condition.
In german literature, a distinction is often made between personal and material creditworthiness. A borrower is considered as personally creditworthy, if he deserves confidence due to
its reliability, professional qualification and its business acumen.
Material creditworthiness is supposed, if the current or expected economical circumstances
of the borrower guarantee the payment of interests and the repayment of the loan.
The following sections discuss the internal rating of banks. Considering conventional methods
of credit rating, subjective expertise is compared to statistical methods.
6.1.2 Subjective Expertise
Subjective heuristics, expertise, checklists and systematic scoring models are widely
used methods of credit rating in banking practice. They contain a number of criteria about the
creditworthiness of a commercial or private borrower. In retail banking, following information
about the personal creditworthiness of a loan applicant for a consumer loan are examined:
age, civil and family status; number and age of the children
profession and qualification
employer, job circumstances and duration of employment
recovery of claims and garnishment
reason for credit.
Considering the material creditworthiness, following information is evaluated:
monthly net income, additional incomes and security of income
property
- 95 -
loan securities
account information at the internal or external bank
alimonies
rent or mortgage rate
leasing rates, other financial obligations and dept service.
Credit agreements often contain the following basic points: amount of credit, interest rate,
repayment conditions, credit period (duration of the credit) and loan securities.
6.1.3 Statistical Methods
In contrast to heuristic scoring, empirical or statistical credit scoring eliminates the subjectiveness of loan officers. Statistical methods divide borrowers into two classes: customers are
either ‘not creditworthy’ or ‘creditworthy’. The membership to one of the classes depends on a
score based on a criteria catalogue, using a discriminant function.
Important statistical methods for credit rating are, for instance: multivariate discriminant analysis, logit analysis and artificial neuronal network.
The multivariate discriminant analysis measures the dependence between different metric
variables (e.g. income) and a nominal grouping variable (‘not creditworthy’ or ‘creditworthy’). In
doing so, the following discriminant function f(z)i
f (z )i = b0 + b1 ⋅ x1i + b2 ⋅ x2i + ... + bK ⋅ xKi
is estimated that way, that the critical dividing value z* separates optimally the ‘creditworthy’
customers from the ‘not creditworthy’ ones (see Figure 76).
It is a challenge to estimate the coefficients (bK) of the discriminant function that the type I and
II errors (α- and β-errors) are as small as possible.
Group 0 (not creditworthy)
znot creditworthy
Group 1 (creditworthy)
z*
Centroid not creditworthy
zcreditworthy
f(z)
Centroid creditworthy
Type I Error (β-error):
Type II Error (α-error):
A loan applicant receives no credit,
although he is creditworthy (/solvent)
A loan applicant receives a credit,
although he is not creditworthy (/solvent)
Dividing value = critical discriminant value
Figure 76: Discriminant Function and Type I and II Errors
- 96 -
The error terms are low, if the centroids z of group 0 and 1 are far away from each other, and
if the variance within each group is very small (for details see [Backhaus et al. 2006]).
In contrast to discriminant analyses, logit analyses or loglinear models consider non-metric,
qualitative variables. They are suited for the analysis of the dependence of several non-metric
independent variables and a nominal grouping variable.
Artificial Neural Networks (ANN) try to reduce type I and II errors by linking logistic, nonlinear functions. A neuronal network can be defined as “a collection of neuron-like processing
units with weighted connections between the units” [Han and Kamber 2006, p. 24].
All neurons, linked to an ANN, receive, process and relay signals. The architecture of a neural
network consists of three layers shown in Figure 77.
not creditworthy
creditworthy
Output data: credit applicant is not creditworthy or creditworthy
Output layer
Hidden layer
Neural
network
Input layer
c1
c2
Input data: creditworthiness criteria ci of a credit applicant
c4
C3
Figure 77: Architecture of a Neural Network for Credit Rating
The input layer receives signals of the criteria ci. Neurons of the hidden layer are weighting
and processing each signal received from an input layer and relay them to another hidden or
to the output layer. The weights of the connections, which are adapted during the learning
process, play an important rule, since they represent the experienced knowledge. With increasing training, the non-linear discriminant function fits optimally to the data set in Figure 78.
c2
c2
Separation with
artificial neural network
Separation with
discriminant function
Artificial neural networks
achieve good classification
results and they often have
a higher hit quote than the
discriminant analysis
Further reading about
credit rating with artificial
neural network see for
instance: [Krause 1992,
Füser 2001, Kilb 2002]
c1
z*
Not creditworthy
c1
creditworthy
f(z)
Source: adapted from [Kilb 2002, p. 51]
Figure 78: Discriminant Functions in Discriminant Analysis and ANN
- 97 -
6.1.4 Disadvantages of Sharp Credit Rating
All considered methods of credit rating have certain advantages, and also disadvantages:
Heuristics and checklists are subjective and it is often discretionary to bring contradictory
statements together. Different credit experts mostly evaluate and weight the same credit
with identical facts quite different.
The disadvantage of the multivariate discriminant analysis is that only metric criteria can
be taken into account. According to [Kilb 2002], the quote of misclassifications of the discriminant analysis in retail banking is relatively high.
Logit analyses or logistic regression analyses have the drawback that non-linear correla-
tions are difficult to model. Time invariance and representativeness of the models and the
data often is not guaranteed.
The crucial weakness of artificial neural networks is that the processes, results and the
relations between in- and output data are not transparent for the normal user. The hidden
layer of neuronal networks is a black box. As a result, artificial neural network are rarely
used for credit rating in business practice.
Statistical methods in general do not consider specific and individual circumstances and
particularities. As a result, statistical methods are often combined with subjective expertise
to hybrid methods in credit rating practice,.
Another disadvantage of subjective expertise and statistical methods of credit rating is that
they usually result in sharp classifications of loan applicants. The material or personal creditworthiness, or other criteria for creditworthiness of loan applicants often are rated sharply as
‘worthy’ or as ‘not worthy’.
In critical or border cases, if a credit applicant is rated near the cut-off-score (the dividing value
which separates the creditworthy borrowers from the rest), sharp credit rating may lead to incorrect and wrong decisions, to misclassifications as the type I and II errors are.
Only a little variation of one of the rating criteria may result in another decision concerning the
loan application.
In addition, the sharp rating of very different risks often results in the same overall credit rating
of a loan applicant. One the other hand, applicants with the same values of the attributes can
be aggregated to a different overall rating.
- 98 -
6.2
Methods of Fuzzy Credit Rating
6.2.1 Existing Literature on Fuzzy Credit Rating
Whether a loan applicant is creditworthy or not, is not a question that can be answered easily
with "yes or no", but rather with "more or less". It is in the nature of credit rating that information and data are inconstant, incomplete, imprecise and fuzzy.
The first authors who recognised this and associated credit rating with fuzzy classification
were Hans-Jürgen Zimmermann and Peter Zysno [Zimmermann and Zysno 1982, 1983,
Zimmermann 1993, 1997, Zysno 1980]. They classified hierarchically the category ‘creditworthiness’ into a four-level pyramid with different sub-categories (see Figure 79).
Creditworthiness
(γ = 0.59)
Personal creditworthiness (δ = 0.95)
Material creditworthiness (δ = 1.05)
(γ = 0.55)
Loan securities (δ = 0.71)
(γ = 0.99)
Other net
property
(δ = 0.61)
(δ = 0.81)
(γ = 0.58)
Income
minus
expenses
(δ = 1.53)
(γ = 0.92)
Continuity
of margin
(δ = 1.26)
(γ = 0.60)
Potential (δ = 0.93)
Liquidity (δ = 1.39)
(γ = 0.90)
Property
minus long
term debts
(γ = 0.78)
(γ = 0.55)
Physical &
mental
potential
(δ = 1.06)
Business behaviour (δ = 0.97)
(γ = 0.76)
(γ = 0.55)
Motivation
Economic
thinking
(δ = 0.80)
(δ = 1.01)
Conformity
social & economic standards
(δ = 0.93)
Source: adapted from [Zimmermann 1993, p. 366]
Figure 79: Hierarchy of Creditworthiness with Weights δ and Parameters γ
The fuzzy set ‘creditworthiness’ is defined as a composition of the other fuzzy sets. The model
by Zimmermann and Zysno includes remarkable findings. By testing empirically the discussed
compensatory operators γ and the weights δ, the prediction results of the creditworthiness of
test loan applicants with fuzzy classification were significantly better than with sharp classification [Zimmermann and Zysno 1982].
In the last years other reviews about fuzzy credit rating were published. [Romaniuk and Hall
1992] developed an expert system (FUZZNET) by using a fuzzy connectionist model. Similarly, [Levy et al. 1991] programmed a system to evaluate a company’s financial position.
[Hofmann et al. 2002] tested empirically different fuzzy approaches, genetic fuzzy classifiers
and neuro fuzzy algorithm. Their conclusion: fuzzy classifiers achieve better credit rating results than neuro fuzzy or conventional, sharp algorithms like C4.5.
[Chen and Chiou 1999] chose a similar approach as this thesis does. In their model they define fuzzy sets, membership functions, membership degrees to five rating levels (A to E), linguistic variables and a fuzzy integral (i.e. an evidence fusion technique).
- 99 -
The model was applied to a credit rating system in Taiwan, operated for small and medium
enterprises. The evaluation criteria of this credit rating system were modelled as the hierarchical decision structure shown in Figure 80.
Liquidity ratios (g11 = 0.60)
Financial structure ratios
(g12 = 0.60)
Financial conditions
1
(g = 0.68)
Profitablitity ratios
13
(g = 0.52)
Efficiency ratios
(g14 = 0.45)
Quick ratio
Current ratio
Debt ratio
Long-term asset efficieny ratio
Interest expense to net sales
Profit margin before tax
Return on net worth before tax
Inventory turnover
Receivable turnover
Total assets turnover
Administrations personal credit (g21 = 0.23)
Administrations experiences (g22 = 0.16)
Overall
credit level
General Management
(g2 = 0.55)
Stockholders’ structure type (g23 = 0.11)
Ø sale growth rate last three years (g24 = 0.16)
Condition of capital increment (g25 = 0.10)
Outstanding check records (g26 = 0.24)
Characters &
Perspectives
(g3 = 0.44)
Equipment & technologies (g31 = 0.24)
Product marketability (g32 = 0.31)
Collateral (g33 = 0.20)
Conditions of industry next y. (g34 = 0.25)
Source: [Chen and Chiou 1999, pp. 408ff]
Figure 80: Credit Rating Hierarchy with the Degree of Importance gi of each Criterion
[Chen and Chiou 1999] proved that with the fuzzy approach the overall credit rating is more
sensitive to changes of credit information, especially to small changes of a single criterion.
“Moreover, the description of the final credit-rating results in terms of the membership degrees
of the five rating levels can provide loan officers with more valuable information in decision
making” [Chen and Chiou 1999, p. 416].
As a result, credit management or contractual terms and conditions, like the amount of credit,
credit period or the interest rate, can be better adapted and aligned to the exact credit information and the default risk of a borrower.
The next section will discuss another fuzzy credit rating approach using the fuzzy Classification Query Language (fCQL) and a credit rating hierarchy for the bank’s internal credit rating of
private borrowers.
- 100 -
6.2.2 Fuzzy Credit Rating with fCQL
The discussed sharp and fuzzy methods of credit rating so far, can be classified in Figure 81.
According to [Füser 2001], fuzzy logic can be categorised as a method of Artificial Intelligence
(AI), beside Artificial Neural Networks (ANN), expert systems, and others.
Conventional methods
Methods of Artifical Intelligence (AI)
Fuzzy credit rating
Statistical
methods
Subjective
methods
Sharp credit rating
adaptive, non-linear
fuzzy information processing
Subjective expertise & checklists
Scoring models (point rating systems)
Univariat analysis
(traditional financial analysis)
Multiple discriminant analysis
Logit models
Theories of
evolution
Artificial
neuronal
networks
Genetic
algorithms
Fuzzy logic
Neuro
fuzzy
models
Fuzzy
classification
NFE
System Fuzzy
Neuronal
expert
expert system
systems
Fractal
Expert
Chaos geometry
systems
theory
Synergetic
static, if-whenrules
fuzzy database
queries
fCQL
toolkit
fCQL
Relational
database system
SQL
Source: adapted from
[Füser 2001, p. 270]
Figure 81: fCQL as a Method of Artificial Intelligence
As discussed in the second chapter, with fuzzy logic, vague or imprecise linguistic variables of
human thinking and colloquial language can be mathematically reproduced and they become
formally accessible for computers and for an information or expert system.
The approach of fuzzy classification and fCQL allows to work with continuous and discrete
variables and the definition of colloquial terms of credit rating, like ‘definitely creditworthy’,
‘rather creditworthy’ or ‘insufficient creditworthy’. Considering continuous variables, even more
precise statements can be made: for example ‘30% moderately creditworthy’, ‘60% creditworthy’, ’80% rather creditworthy’, and so on.
However, the approach of fuzzy classification and fCQL is to be distinguished from artificial
neuronal networks, expert systems and from other methods of AI (in Figure 81).
Based on an example of credit rating practice, a hierarchy of creditworthiness will now be discussed. It is assumed that the ‘overall creditworthiness’ of a loan applicant on the top level (L1
in Figure 82) of the hierarchy of creditworthiness is defined by ‘personal creditworthiness’
and ‘material creditworthiness’ on the second level (L2). On the third level (L3), personal
creditworthiness includes the ‘family situation’ and ‘employment’, on the one hand. On the
other hand, material creditworthiness contains the ‘income’ and ‘outgoings’. These four aggregations are defined by a number of different criteria of credit rating on the fourth level (L4).
They are usually asked for in an application form for a private credit and analysed by the bank.
- 101 -
L1:
Overall creditworthiness
Personal creditworthiness
L2:
Family situation
L3:
Outgoings
Income
Employment
Age
Civil status
Family status
Living conditions
Number/age of children
etc.
L4:
Material creditworthiness
Net income
Security of income
Property
Loan securities
Account information
etc.
Profession
Qualification
Employer
Duration of
employment
etc.
Rent or mortgage rate
Alimonies
Financial obligations
Leasing rates
Reason for credit
etc.
Figure 82: Practice-Related Example of a Hierarchy of Creditworthiness
Regarding the income, for instance, the loan applicant has to answer his income level, additional income, property, loan securities (e.g. surety, guarantee, lien and mortgage), account
information by the internal or external bank, and other criteria.
Following a bottom-up approach of fuzzy credit scoring, all predefined, and weighted scores of
all the criteria on the bottom level L4 are evaluated by bipolar rating scales.
Qualitative attributes or criteria of credit rating are assigned to discrete domains and member-
ship functions. Figure 83a shows an example of a discrete, six-level rating scale to determine
and rate the attribute ‘loan securities’. The other example, Figure 83b, represents a continuous
scale with continuous membership functions of the quantitative attribute ‘income’. To each
value of the membership functions a number of scores is assigned.
a)
b)
Loan securities
insufficient
very low

0
low
sufficient
low rather low rather high high very high

2

4
6
6

8
Equivalence class
10
0
0.2
0.4
0.6
0.8
1
[0, ... , 3'499]
Equivalence class
high
Terms
3499 3500
≥10'000
0.1 scores per 0.01 value of μ high
e.g.: 0.56 = 5.6 scores
0

[very low, low, rather low] [rather high, high, very high]
Linguistic variable
(Attribute)
Income
Equivalence class
[3'500, ... , 10'000]
Equivalence class
Domain
Scale
Score
Context
0
Membership
functions
0.44
0.56
μ insufficient
μ sufficient
1
μ low
μ high
Figure 83: Examples of a Qualitative and a Quantitative Attribute of Fuzzy Credit Scoring
The evaluated score S(Aijkl) of all rating scales of the different attributes (Aijkl) considered in L4
are accumulated with following scoring function.
The scores on the fourth level (L4) define the scores on the third level (L3), ‘family situation’
S(A311), ‘employment’ S(A312), ‘income’ S(A321), and ‘outgoings’ S(A322).
- 102 -
n= 5
n= 5
n= 5
n= 5
l =1
l =1
l =1
l =1
S(A ijkl ) = ∑ S(A 411l ) + ∑ S(A 412l ) + ∑ S(A 421l ) + ∑ S(A 422l )
L4:
L3:
Family situation
Employment
Income
Outgoings
S(A311)
S(A312)
S(A321)
S(A322)
Personal creditworthiness S(A21)
L2:
L1:
(Scoring function)
Material creditworthiness S(A22)
Overall creditworthiness S(A1)
The scores on the third level L3 define the scores of the second level (L2) and first level (L1) of
the hierarchy, that means the personal (S(A21)) and material (S(A22)) and the overall creditworthiness (S(A1)). Appendix 6 (see p. 142) shows formal details, i.e. all attributes, levels, classes,
terms and domains of the scoring function and the hierarchy of creditworthiness.
This example of hierarchical fuzzy credit scoring can also be shown in a matrix: the left side of
Figure 84 shows a fuzzy classification of the ‘overall creditworthiness’, the right side the fuzzy
classification of ‘material creditworthiness’ and, at the bottom, the ‘personal creditworthiness’.
The membership degrees of the classified loan applicants Ford, Smith, Brown and Miller can
be calculated on the different levels Li of the hierarchy of creditworthiness.
This hierarchical fuzzy credit classification approach has the advantage that the degree of
creditworthiness can be queried on the different levels of the hierarchy.
If the rating of one or several levels of the creditworthiness of a bank customer degrades, a
trigger mechanism warns the manager. In addition, possible developments of a customer’s
creditworthiness can be simulated with different scenarios (e.g. worst, realistic or best case).
Income (Score)
Ford
0
0
0
Interest rate: iC3 = 14%
18.5% creditworthy
24.8% pers. not creditworthy
24.8% mat. not creditworthy
31.9% not creditworthy
100% not creditworthy
49 50
100
1
0.44
0.6
0.56
μ pers. not creditworthy
0
24 25
μ pers. creditworthy
Miller
C1-2)
C1-1)
Employment Personally
not creditcreditworthy
worthy
Brown
Smith
C1-3)
Family
situation not
creditworthy
0
24 25
Figure 84: Hierarchical Fuzzy Classification of Creditworthiness
- 103 -
Smith
50
Family situation (Score)
C1-4)
Personally
not
Ford
creditworthy
0
0.4
0
50
Employment (Score)
no credit (interest
rate: iC4 = 18%)
C3) Materially not
creditworthy;
50
Smith
C4) Not creditworthy;
24 25
49
0.4
Brown
0
1
0.6
0.28
Personal
creditworthiness (Score)
0.72
Brown
C2-4)
C2-3)
Materially Income not
Ford not creditcreditworthy
worthy
Outgoings (Score)
50
100% creditworthy
34.8% creditworthy
30.2% pers. not creditworthy
19.1% mat. not creditworthy
15.9% not creditworthy
50
Miller
C2-2)
C2-1)
Outgoings not Materially
creditworthy creditworthy
24 25
100 C2) Personally not C1) Creditworthy Miller
creditworthy
μ mat. not creditworthy
μ mat. creditworthy
Material creditworthiness (Score)
In addition, fuzzy classification or credit scoring enables the calculation of individual interest
rates, i.e. of individual risk premiums. To each class of the fuzzy classification in Figure 84 an
interest rate is assigned; for example: C1: 6%, C2: 10%, C3: 14% and C4: 18%.
The premium is to reflect the risk class as accurate as possible. According to their degree of
creditworthiness, for each of the loan applicant an individual interest rate can be calculated:
Interest rate iFord
= 1·18 = 18%.
Interest rate iSmith = 0.185·6 + 0.248·10 + 0.248·14 + 0.319·18 = 12.8%
Interest rate iBrown = 0.348·6 + 0.302·10 + 0.191·14 + 0.159·18 = 10.6%
Interest rate iMiller
= 1·6 = 6%
Classified sharply, Smith belongs entirely to C4 and has to pay a very high risk premium of
18%, like Ford, although Smith is nearly in the same position as Brown who is classified in C1
and enjoys good conditions: he has to pay an interest rate of 6% only. Although Miller is much
more creditworthy than Brown, he is also classified in C1 and pays the same interests of 6%.
With a fuzzy calculated interest rate, customer Smith is not disadvantaged any longer in
comparison to Brown, but still has to pay quite high interests (of 12.8%). Brown is no longer
privileged and has to pay 10.6%. Miller still has to pay 6% only.
Fuzzy classification enables a fair and non-discriminatory calculation of risk premiums for loan
applicants or bank customers, according to their creditworthiness or default risk.
However, in the case of the fuzzy credit rating, the bank manager or the loan officer still is
confronted with the problem he may not allow a credit beyond a certain limit of default risk
or lack of creditworthiness. This limit (cut-off-score) can be fixed at a certain level of the interest rate (e.g. no higher interest rates than 15%). Alternatively, the credit is refused, if the fuzzy
calculated membership degree of a classified loan applicant to C1 decreases, for instance,
under 10%, or if the membership degree to C2, C3 or to C4 increases over 50%.
Depending on the requirements of bank, or on the knowledge of the loan officer, or depending
on the situation, the credit rating can be more or less strictly.
According to [Gachet 2006], not only individual interest rates can be calculated with fuzzy
classification, but also an individual credit period.
Going one step further, the individual interest rate could be calculated based on the attribute
‘creditworthiness’ (with the terms ‘sufficient’ or ‘excellent’), ‘credit period’ (‘short-term’ and
‘long-term’) and on the attribute ‘amount of credit’ (‘small credit’ or ‘large credit’). Figure 85
shows such a three-dimensional fuzzy classification in credit rating.
To each of the classes an interest rate is assigned under following realistic assumptions: the
nominal interest rate is lower for
long-term credits than for short-term credits
large credits than for small credits and for
clients with an excellent creditworthiness than for clients with a sufficient creditworthiness.
- 104 -
a)
b)
C5) 100%
Creditworthiness
Creditworthiness (Score)
(Score)
Excellent
Credit
period
(months)
Smith
100
99
100
99
60
Short-term
12
50
Small credit
19 20
Large credit
Brown, Miller Smith
C5) 100%
C3) 100%
100
μ sufficient
Sufficient
Long-term
36
35
3
200
μ excellent
200
Amount of
credit (thousand €)
1
0
Ford
55
3
0
1
C4) 100%
C1) 12.8%
C2) 9.6%
C3) 11.2%
C4) 8.3%
C5) 17.5%
C6) 13.4%
C7) 15.4% Brown
C8) 11.7%
C1) 14.9%
Smith
C2) 13.1%
C3) 17.3%
C4) 15.2%
C5) 9.7%
C6) 8.4%
C7) 11.5%
C8) 9.9%
C3) 42.9% Ford
C4) 57.1%
Müller
Credit
period
(months)
60 μ long-term
36
35
12
100 Amount
19 20
μ short-term
of credit
(in thousand €)
μ small credit
μ large credit
Figure 85: Thee-Dimensional Sharp (a) and Fuzzy (b) Credit Rating
The attributes, equivalence classes [in squared brackets] and the terms (in round brackets) of
the eight classes C1 to C8 in Figure 85a) and the different interest rates are shown in Table 16.
Table 16: Interest Rates for Different Loan Categories
Class
C1
C2
C3
C4
C5
C6
C7
C8
Creditworthiness
[Score]
[100, 200]
[100, 200]
[50, 99]
[50, 99]
[100, 200]
[100, 200]
[50, 99]
[50, 99]
Attributes, [equivalence classes] and (terms)
Amount of credit
(Term)
Excellent
Excellent
Sufficient
Sufficient
Excellent
Excellent
Sufficient
Sufficient
[Euros]
[20'000, 100'000]
[3000, 19’000]
[20'000, 100'000]
[3000, 19’000]
[20'000, 100'000]
[3000,19’000]
[20'000, 100'000]
[3000, 19’000]
(Term)
Large credit
Small credit
Large credit
Small credit
Large credit
Small credit
Large credit
Small credit
Credit period
[Months]
[12, 35]
[12, 35 ]
[12, 35]
[12, 35]
[36, 60]
[36, 60]
[36, 60]
[36, 60]
(Term)
Short-term
Short-term
Short-term
Short-term
Long-term
Long-term
Long-term
Long-term
Interest
rate
8%
10%
12%
14%
5%
7%
8%
9%
If the customers from the example above were applying for a credit, they are sharply classified
to the classes of the following Table 17:
Table 17: Sharp Classification of the Loan Applicants
Loan applicant Class
Smith
Ford
Brown
Miller
C3
C4
C5
C5
Attributes, [values of the loan applicants] and (terms)
Creditworthiness
Amount of credit
Credit period
[Score]
97
10
109
189
(Term)
Sufficient
Sufficient
Excellent
Excellent
[Euros]
24'000
16'000
30'000
90'000
(Term)
Large credit
Small credit
Large credit
Large credit
[Months]
32
18
38
54
(Term)
Short-term
Short-term
Long-term
Long-term
Interest
rate
12%
14%
5%
5%
Although Smith and Brown have similar values (their score of creditworthiness: 97, 109; their
amount of credit: 24'000, 30'000 €; their credit period: 32, 38 months), they are classified in
different classes with different interest rates. Smith (12%) has to pay a 2.4 times higher interest rate than Brown (5%). Miller, with 80 scores more, a three times higher amount of credit
and a contract which is two years longer than Browns, has to pay the same 5%.
- 105 -
With the fuzzy classification approach (compare Figure 85b), such discrepancies are eliminated and a exact, individual interest rate can be calculated, according to the values of the
attributes ‘creditworthiness’, ‘credit period’ and to ‘amount of credit’ of a loan applicant:
Interest rate iFord
= 0.429·12 + 0.571·14 = 13.14%
Interest rate iSmith = 0.149·8 + 0.131·10 + 0.173·12 + 0.152·14 + 0.097·5 + 0.084·7 + 0.115·8 + 0.099·9 = 9.59%
Interest rate iBrown = 0.128·8 + 0.096·10 + 0.112·12 + 0.083·14 + 0.175·5 + 0.134·7 + 0.154·8 + 0.117·9 = 8.59%
Interest rate iMiller = 1·5 = 5%
Classified fuzzily, Smith is to pay a lower interest rate of 9.59% than in a sharp classification
(12%). Ford is also to pay a slightly lower rate of 13.14% (sharp: 14%). In contrast, Brown has
to pay a higher rate of 8.59% (sharp: 5%). For Miller the interest rate (5%) remains the same.
6.2.3 Other Applications for Fuzzy Classification in Banking
Considering other important criteria of bank customer in private banking, e.g. a customer’s
assets, capital, turnover, profit, lifetime value or his cross-selling potential, fuzzy classification
is interesting for offering offer individualised and personalised services or bank products.
Such mass customisation, based on the fuzzy classification approach, leads to added-value
for the customer, may to higher customer retention (i.e. profits) and to competitive advantages
for the bank.
Other banking sectors, e.g. the credit rating of companies, are also promising for fuzzy classification. The credit rating of companies or business customers is much more complex than
the rating of private customers, since the evaluation deals with many different rating criteria
and standards. In this case, the application of fuzzy classification is even more pertinent than
the credit rating of private loan applicants discussed in this chapter.
- 106 -
Chapter 7
Conclusion
- 107 -
Chapter 7: Conclusion
7.1
Summary
Conventional data analysis methods in statistics and data mining have certain disadvantages:
classified elements are mostly assigned sharply to one single class. Such sharp classifications
can be arbitrary, problematic, incorrect and unfair, as many examples of the master thesis
explained: although elements had nearly the same values, they were classified in different
classes. In contrast, elements with very different values can be classified in the same class.
In business practice, such misclassifications and discrepancies may have negative effects, for
instance, if a key account is managed in the same way as an unprofitable customer, or if two
similar customers notice that they are treated differently by the company.
With fuzzy classification, these problems do not occur. Fuzzy classification and fCQL (fuzzy
Classification Query Language) combine fuzzy logic and relational databases and allow the
classification of customers, or any other elements, into more than one class at the same time.
Defining attributes, terms and fuzzy sets, which are determined by membership functions over
the whole domain of an attribute, the classification space becomes fuzzy. That means that the
classes sharp borders disappear and there are continuous transitions between the classes.
The fuzzy approach with fCQL has further advantages: it enables the reduction of complexity
without loss of information, the use of numerical (quantitative) and non-numerical (qualitative)
values, a clear semantic and a human-oriented query process with linguistic variables and
terms. In addition, dynamic and multidimensional fuzzy classifications can be undertaken.
This thesis has basic questions and answers. Following basic findings can be summarised:
‘Where’ can fuzzy classification be used in business management?
It could be used, for instance, in different fields of marketing (¨Table 18; ¨RQ1) and of
Customer Relationship Management (CRM; ¨Table 20; ¨RQ3)
‘How’ could fuzzy classification be applied?
Combined with management tools, fuzzy classification can be applied as fuzzy portfolio analysis,
fuzzy SWOT analysis, fuzzy ABC analysis and as fuzzy scoring method (¨Table 19; ¨RQ2).
‘What for’ can fuzzy classification be used?
It is especially suited for Customer Performance Measurement (CPM; ¨Table 21 ¨RQ4), using
relevant Customer Performance Indicators (CPI; ¨Table 22; ¨RQ5). Since the classification of
the indicators is a precondition for the analysis, evaluation and segmentation of customers
(¨Table 23; ¨RQ6), fuzzy classification is a crucial task of customer performance measurement.
In addition, fuzzy customer segmentation can be used for credit rating (¨Table 24; ¨RQ7).
‘Why’ should fuzzy classification be used?
In contrast to sharp methods, fuzzy classification avoids misclassifications, enables a more precise
classification and management of customers according to their values, an improved exploitation of
customer potential and a better allocation of limited ressources for effective and efficient CRM.
- 108 -
Table 18: Results of Research Question (RQ) 1
What are potential fields and topics for business applications for fuzzy classification in general?
Fuzzy classification (fc) supports generally the management functions analysis and control;
for instance, the analysing and controlling of performance indicators in CRM and marketing.
As summarised above, fc is suited for Customer Relationship Management (CRM; ¨Section 4.1;
¨RQ 2), Customer Performance Measurement (CPM; ¨Section 4.2; ¨RQ 3), fuzzy customer
1
segmentation (¨Section 5.1; ¨RQ 4) and for fuzzy credit rating (¨Section 6.2; ¨RQ 6).
Data of market research can be used for fuzzy market segmentation (¨Section 5.2).
Fuzzy classification can be applied to widely used management tools and methods (¨RQ 5),
as fuzzy portfolio analysis (¨Section 3.2), fuzzy SWOT analysis (¨Section 3.3; fuzzy Strength,
Weakness, Opportunity and Threat/risk matrix), fuzzy ABC analysis (¨Section 3.4), fuzzy scoring
methods or fuzzy RFM method (¨Section 3.5) and as fuzzy cost-benefit analysis.
Empirical research confirms that CRM, customer segmentation and performance management
became very important in business practice. These concepts are also most suited for the application of fuzzy classification, as discussed in Chapters 4 and 5, and shown in Figure 86.
However, the most promising topic for the application of fuzzy classification is customer
portfolio analysis, which is easy to understand and implement, and widely used in practice.
According to a study of [Krafft 2007], 44% of all firms use customer portfolios analysis. 32% of
the firms analyse customer portfolios regularly, 48% sporadically [Reinecke and Herzog 2006].
Importance in business practice
...
Business process
reengineering
Total quality
management
...
Benchmarking
Strategic
planning
Mission and
vision statement
Core competences
Promising topics
Supply chain
management
SWOTanalysis
...
Discriminant
analyse
...
Regression
analysis
Mass
marketing
Transaction
marketing
...
Correlation
analysis
Value
chain
...
Conjointanalysis
Association
analysis
Analysis of
variance
Logitanalysis
Cost-benefit
analysis
Five-forcemodel
ABCanalysis
Customer
segmentation
Performance
measurement
Balanced
scorecard
Scoringmodels
Market
segmentation
...
Relationship marketing
Decision
Individual marketing
trees
One-to-one marketing
Mikromarketing
Multidimensional
scaling
...
Fuzzy classification management tool
Fuzzy statistical or data mining method
Sharp or fuzzy application possible
Fuzzy application to marketing concept or field
Portfolio
analysis
CRM
Credit rating
Customised marketing
Cluster analysis
Personalisation
Mass
customisation
Customer profiling
Artificial
neural
network
Suitability for fuzzy application
Management tool
Statistical or data mining method
Marketing concept or field
Conventional or
sharp approach
Figure 86: Promising Management Tools, Methods and Concepts for Fuzzy Classification
- 109 -
C1)
Development
customers
Star
customers
(question marks)
(stars)
Brown
C3)
Smith
C4)
Renunciation
customers
100% (poor dogs)
renunciation
14% C1)
1
0 Ford
0
21% C2)
26% C3)
39% C4)
1
μ weak
Absorption
customers
Miller
100% C1)
star customer
Smith
Brown
Miller
(cash cows)
35% C1) star
24% C2) development
25% C3) absorption
16% C4) renunciation
customer
μ strong
C2)
μ unattractive
b) Cumulative turnover
μ attractive
a)
A-customers B-customers
C-customers
0
1
0
μA
μB
μC
1
Figure 87: Fuzzy Classified Customer Portfolio (a) and Fuzzy ABC Analysis (b)
Conventional portfolio analysis is applied in such a way that sharp classes are defined, which
means that classified elements belong to one single class only. For instance, a customer is
classified as a ‘star’ (Brown in Figure 87a) or as ‘renunciation customer’ (Smith). The problem:
Brown and Smith are classified in different classes, although they have a similar values of the
attributes. With fuzzy classification, the different values of customers are taken exactly into
account: Brown and Smith belong partly to all four classes of the portfolio at the same time.
Besides portfolio analysis, the ABC analysis is used by the majority of companies to segment
customers, products or other objects. Surprisingly, the ABC analysis has also been applied in
a sharp manner so far, although it is problematic to distinguish sharply between A-, B- and Ccustomers (Figure 87b). Classified sharply, Miller is an A-, and Brown a B-customer, although
they have both nearly the same turnover. If a customer (Brown) had little higher turnover, may
be only a few euros, he would slide into class A and might be managed differently as a key
account. In contrast, customers with different turnover can be classified in the same class of a
sharp ABC analysis: Smith is classified as a B-customer although his turnover is much lower
than Brown’s. In a fuzzy ABC analysis, the transitions between A-, B- and C-customers become fluent and the customers can be precisely classified according to their real turnover.
Fuzzily, Brown and Miller are partly A- and B-customers; Smith belongs partially to B and C.
After the discussion of different fuzzy classification management tools (compare Table 19),
CRM and possible fields, tasks or processes for fuzzy classification (see Table 20), Customer
Performance Measurement (CPM) turns out to be an important task of analytical CRM.
CPM is defined here as the acquisition, analysis and the evaluation of performance-related
customer information, using Customer Performance Indicators (CPIs), i.e. customer-related
monetary or non-monetary criteria (measures, metrics, indices, figures or ratios) about customer performance. Since it is a problem to classify and evaluate customer performance
sharply, a fuzzy classification of all indicators in CPM is suggested.
- 110 -
Table 19: Results of Research Question 2
What are potential management tools and methods for fuzzy classification? (¨Chapter 3)
Fuzzy portfolio analysis (¨Section 3.2) facilitates the classification and management of customers, businesses, Strategic Business Units (SBUs) or Fields (SBFs). Further advantages are:
- dynamic analysis and monitoring of a portfolio and implementation of a trigger mechanism
- fuzzy investments proportionally to the membership degrees of business units to each class
- balancing of portfolio (of cash flow producing and requiring business units) by defining a
balance indicator and the implementation of an trigger mechanism, which warns if the
portfolio is not balanced (portfolio with insufficient actual cash flow or future potential or both)
- improved management of businesses and better allocation of limited ressources.
Fuzzy SWOT analysis (Strengths, Weaknesses, Opportunities, Threats) (¨Section 3.3):
2
- fuzzy strength matrix: fuzzy classification of strategic strengths (competitive advantages)
- fuzzy weakness matrix: classification of primary weaknesses (competitive disadvantages)
- fuzzy opportunity matrix: fuzzy classification of high opportunities (for investments) and
- fuzzy threat/risk matrix: fuzzy classification of major threats/extreme risks (risk management).
Fuzzy ABC analysis (¨Section 3.4): fluent transitions between A-, B- and C-customers allow a
fair segmentation of customers according to their real turnover (or according to other indicators).
Combination of fuzzy portfolio and ABC analysis and definition of different customer strategies.
Fuzzy scoring methods (¨Section 3.5): discussion of a sharp Recency Frequency Monetary
value method (problem: 20% higher score of Miller despite same purchase behaviour as Brown)
- development of an improved fuzzy RFM method and the fuzzy RFM scoring of customers
- fuzzy calculation of RFM incentives and personalised accounts.
What are potential fields, processes & instruments for fuzzy classification in CRM? (¨Section 4.1)
Promising fields and concepts for the use of fc marketing and customer management are:
customer acquisition and retention; individual or one-to-one marketing; micromarketing;
key account management ; mass customisation; personalisation; customer profiling.
The possible use of fc in customer-oriented tasks and processes of CRM: customer specific
3
information or advertising; personalised campaigns, loyalty or recovery programs; customisation
of products; fuzzy calculation of individual prices, accounts, incentives, paying conditions or warranties; customer specific services and customer care according to their value to the company.
Definition of a "small CRM success chain" (from customer orientation to shareholder value).
Conclusion: the most promising fields for fuzzy classification in CRM: analytical CRM (aCRM)
and Customer Performance Measurement (¨Section 4.2; ¨RQ 4).
According to Table 21, CRM and CPM require a comprehensive number of customer performance indicators to manage and maximise customers as an asset. This work collected, defined,
operationalised and discussed 170+ Customer Performance Indicators (CPIs; see Table 22,
Appendix 4, pp. 136ff), which can be measured on an individual and on an aggregated level.
- 111 -
What are the benefits of fuzzy classification in customer performance measurement? (¨Section 4.2)
Fuzzy classification enables fair and exact Customer Performance Measurement (CPM).
CPM is a precondition to manage and maximise customer as an asset of the company.
Different dimensions and characteristics can be taken into account for CPM and fuzzy
classification: unit (monetary/non-monetary), format (quantitative/qualitative), planning
4
interval (short-/long-term), time (ex-post/ex-ante), alignment (internal/external), incentives
(variation/improvement), CRM layer (operational/strategic) and aggregation level (low/high).
Consideration of empirical studies of customer performance measurement in business practice.
Conclusion: an adequate number of Customer Performance Indicators (CPIs) is required to
manage customer best possible (¨Section 4.3; ¨RQ 5).
What are important customer performance indicators for CPM? (¨Section 4.3)
170+ Customer Performance Indicators (CPIs) were defined, discussed and operationalised
(¨Appendix 4, pp. 136ff).
Description of different functions of CPIs and requirements for indicators.
Discussion of six different categories of indicators:
1) Customer Performance Indicators of Revenue and Profitability (CPIP), such as customer
turnover, contribution margins I-IV, gross/net profit, customer equity or customer lifetime value
5
2) Customer Investment Indicators (CII): customer costs of acquisition, retention and recovery;
transaction, service, communication, contact, marketing and total customer costs; return on’s
3) Customer Relationship Indicators (CRI), e.g. customer value, satisfaction, loyalty or retention
4) Customer Recommendation Indicators (CReI), e.g. number/intensity of recommendations
5) Customer Information Indicators (Cinfl), e.g. number/quality of suggestions or complaints
6) Customer Cooperation Indicators (CCI), e.g. intention to or expertise for cooperation.
Classification of the 170+ CPIs into a "big CRM success chain" (¨Figure 42; p. 60).
Fuzzy classification and fuzzy Classification Management Tools (fCMT), like the fuzzy portfolio
analysis or ABC analysis and scoring methods, are general instruments to analyse and classify customer performance indicators. In addition to these methods, customer contribution
margin accounting and the calculation of customer equity or of Customer Lifetime Value
(CLV) are possible tools to measure customer performance and customer attractiveness.
By responding to central questions of each tool (see Figure 88) and defining adequate Key
Customer Performance Indicators (KCPI), companies can enhance their customer perform-
ance measurement in order to improve the quality and profitability of customer relationships. In
addition, customer performance measurement enables fuzzy customer segmentation, which
is defined as the fuzzy classification of the company’s current customers into similar, fuzzy
segments, using different customer performance indicators.
- 112 -
Customer Performance Measurement (CPM) and management
C2)
Fuzzy scoring or
RFM method
Customer contribution margin analysis
(Section 3.4)
Section 3.5)
(Subsection 5.1.14)
Cumulative turnover/profit
C1)
C6)
C4)
Indicator X
C2)
C3)
Which performance
indicators are considered in fuzzy portfolios?
What are the membership degrees of one/all
customers to each class?
How can the portfolios
be optimised?
C4)
C7)
C3)
Fre-
Cum. share of customers
Who are the customers
with the highest/lowest
turnover or profit?
How many customers
generate how much of
the turnover or profit?
How are A-, B- or Ccustomers managed?
C1)
quency
Which indicators are
considered in the scoring
model & how weighted?
Which customers purchase most recently, freqent and at a high value?
Which incentives are
offered to improve RFM?
Calculation of
customer equity and
Customer Lifetime
Value (CLV)
(Subsection 5.1.16)
(Rt − Et )
t
t =0 (1 + i )
n
CLV = ∑
How can customer
contribution margins
I-IV can be improved?
Which (non-/)monetary
indicators are taken
into account to model
and calculate CLV or
customer equity?
Measurement level Examples of indicators
r Customer
results
Customer Investment Indicators (CII)
Customer Relationship, Recommendation, Information, and Cooperation
Indicators (CRI, CInfI, CReI, CCR)
Category
C8)
B-customers
A-customers C-customers
C5)
Monetary value
(Section 3.2)
Indicator Y
Fuzzy customer
ABC-analysis
Recency
Fuzzy customer
portfolio analysis
(Section 4.3)
Customer Performance Indicator for
Revenues and Profitability (CPIP)
Customer Performance Indicators (CPIs)
Central questions Fuzzy customer analysis
(= analysis, planning, implementation and control)
q Customer
behaviour
pBehavioural
intentions
o Customer
attitude
n Customer
investments
Turnover (I 31), cash flow (I 30), contribution margins I-IV (I 70 - I 73),
customer gross or net profit (I 75,I 76), customer equity, CLV (I 79,I 80),
# of new customers (I 4), # of customers (I 7), market share (I 43)
Repurchases (recency, frequency, rhythm; I 20-I 28), cross-/up-selling
(I 50-I 53), share of wallet, penetration (I 39,I 38), payment behaviour (I
66), recommendations (I 156), complaints (I 164), duration of CR (I 147)
Repurchase intentions (I 55), cross-buying intentions (I 54), intention to
recommend (I 151), intention to switch (I 141), customer loyalty
(I 134), intention to dialog (I 157), intention to cooperate (I 171)
Perceived product (I 118), service (I 120) and relationship quality (I 149),
or price-performance ratio (I 122), image (I 115), customer value (I 123 I 125), customer satisfaction (I 126), commitment (I 131), trust (I 133)
Marketing costs (I 101), total customer costs (I 102), acquisition, retention and recovery costs (I 87-I 92) administration (I 93), transaction (I 94),
contacts (I 100), (after) sales (I 95), logistic (I 96), service (I 97) costs
Figure 88: Tools and Indicators for Customer Performance Measurement
Two different approaches can be used to define fuzzy segments. Any class of a one-, two- or
multidimensional fuzzy classification can be defined as a fuzzy customer segment. Another
way is to define fuzzy clusters according to the patterns of the data, using fuzzy algorithms.
The definition of fuzzy segments has important outcomes. Firstly, customers can partly belong
to different segments at the same time. If customer strategies are assigned to fuzzy segments,
customers have to be managed according to several, may be contradictory strategies at same
time. This forces customer managers to combine, align and personalise customer strategies.
However, to avoid contradictions, basic requirements should be well defined. For instance, the
company defines the basic requirement that a customer has to be profitable in the long term or
that his creditworthiness has to exceed a certain level.
- 113 -
Further points about fuzzy customer segmentation are summarised in Table 23.
How can customers be segmented fuzzily? (¨Section 5.1)
Fuzzy customer segmentation can be realised by one-, two- or multidimensional fuzzy classification using fuzzy classification management tools (¨RQ 2) and CPIs or KCPIs (¨RQ 5).
Customer segmentation and the management and profitability of customer relationships can be
improved using portfolio analysis with important indicators (¨Subsections 5.1.5 to 5.1.16):
6
- fuzzy classified customer orientation and customer value portfolios
- fuzzy classified customer satisfaction portfolios
- fuzzy classified customer loyalty and retention portfolios
- fuzzy classified customer repurchases, add-on selling and share of wallet portfolios
- fuzzy classified customer turnover, contribution margins and profit portfolios
- fuzzy classified customer equity and Customer Lifetime Value (CLV) portfolios.
This thesis suggests to classify and segment all customer performance indicators fuzzily in
order to avoid misclassifications and to improve the quality of customer evaluations.
Customer segmentation often is a strategic task in firms: banks and other financial institutions,
for instance, have to segment ‘creditworthy’ loan applicants from ‘not creditworthy’ ones. By
defining a four-level hierarchy of creditworthiness with different practice-relevant criteria in
Section 6.2, a fuzzy credit scoring approach was applied to the rating of a loan applicant’s
personal, material and overall creditworthiness.
In addition, a multidimensional fuzzy credit rating approach allows to calculate individual
interest rates (risk premiums) according to the customer’s values of the attributes ‘creditworthiness’, ‘amount of credit’ and ‘credit period’. Table 24 summarises further points.
What are the benefits of the fuzzy classification approach in credit rating? (¨Section 6.2)
The decomposition principle applied to a fuzzy credit scoring model enables a fair and exact
credit rating of loan applicants on different levels of a hierarchy of creditworthiness.
A three-dimensional fuzzy classification model allows to calculate individual interest rate
according to the values of the attributes ‘creditworthiness’, ‘amount of credit’ and ‘credit period’.
The decision to allow a credit is sharp, but contractual terms/conditions can be adapted fuzzily.
7 Conclusion: fuzzy classification enables
- a fair, exact, differentiated and sensitive rating of loan applicants according to the default risk
- the reduction of misclassifications (that a loan applicant receives no credit although he is
creditworthy, or that a loan applicant receives a credit, although he is not creditworthy)
- an improvement of the risk structure of the whole credit portfolio and
- the mass customisation of products and services in banking.
- 114 -
7.2
Critical Remarks
As summarised in this chapter, the application of fuzzy classification has many advantages.
However, fuzzy classification is also confronted with certain problems:
Sharp classification usually is clear, simple, straightforward and understandable for every-
one. In contrast, fuzzy classification is more complicated and not as easy to understand, to
communicate and to implement as sharp classification. If a classified customer belong simultaneously to different classes and strategies, this can be confusing, indeed.
How can it be explained to customer Brown that he was fuzzy classified into different
classes at same time and therefore receives a personal discount of 7.4% or has to pay an
interest rate of 8.59%? Since many people do not like being classified or labelled as something and may not understand or accept this, one strategy could be not to communicate
customer classifications. However, fuzzy classification may has to be communicated in a
subtle, indirect way, writing, for example: “Dear Mr. Brown, our company thanks you for your
trust and loyalty and offers you a personal discount of 7% for your next purchase.”
The communication of the fuzzy classification concept to employees can be difficult as well.
However, to successfully realise fuzzy customer segmentation, all relevant people have to
understand and support the idea and the implementation of fuzzy classification.
The benefit of a classification always depends on the context. It can be inefficient to clas-
sify objects, if the monetary or non-monetary, direct or indirect costs of classification are
too high in comparison to the benefit of the classification. Possible classification costs are:
- time required to collect, gather, handle, store, administrate and classify (customer) data
- staff (e.g. CIO, CCO, customer managers, data architect, employees of CRM or IT)
- ICT, technical infrastructure; hardware, information systems, CRM software, etc.
If an entrepreneur and the employees of a small enterprise personally know very well their
customers and their different value for the company, (fuzzy) classification with a data mining
tool is not necessary. However, with an increasing number of anonymous customers in medium or large enterprises, such implicit customer evaluation and segmentation is often not
possible anymore. This means that the benefits of CRM and of fuzzy customer classification
or segmentation for information management is higher with a large number of customers.
It is theoretical and empirical difficult to weight attributes or criteria optimally, for instance to
define the right degree of gamma or the number of points to each class of a scoring model.
Many basic decisions in everyday and business life have to be made in a sharp manner.
For example: a relationship is continued, or it is not. A contract is signed, or it is not; an order is accepted, or it is not. However, such sharp decisions do not exclude that for instance
the terms or the conditions of a contract or of an order still can be adapted in a fuzzy way.
- 115 -
Considering customer performance measurement, the following points have to be commented:
Much intangible, but important customer information cannot be handled by Customer Per-
formance Measurement (CPM) or by a system (CPMS). In the words of Albert Einstein:
“Sometimes what counts can’t be counted, and what can be counted doesn’t count.”
Consequently, the measurability of customers, or of marketing in general, is a problem and
challenge of marketing controlling. An empirical study of the market research organisation
IHA-GfK and the Swiss Marketing Association (GfM) confirms this problem (see Figure 89).
56
Measurability of marketing
Basis of information and data on marketing
37
Know-how in the range of marketing
19
Lack of ressources for marketing controlling
18
Indifferent
No challenge
19
26
46
23
Cross-functional support of marketing controlling
Big challenge
44
28
Engagement of marketing and sales management
for marketing controlling
12
32
33
44
36
45
37
45
0%
100%
Source: [Reinecke and Herzog 2006, p. 87]
Figure 89: The Main Challenges of Marketing Controlling in Practice
In addition, the basis of information and data on marketing and customers is challenge as
well, as shown in Figure 89.
However, both the measurability of marketing or CRM and the basis of information can be
improved by clearly defining and operationalising (customer) performance indicators, as
proposed in this thesis.
It is methodically difficult to determine cause-and-effect relations between the indicators in
models of performance measurement (see e.g. [Malina and Selto 2004]). Consequently,
causalities as proposed in the "big CRM success chain" have to be analysed critically.
The exact profit or equity of a customer cannot be determined, because not all customer
costs, revenues or benefits can be assigned clearly to a single customer or to a marketing
decision, process or action.
CPM(S) is only one instrument of CRM, customer and marketing controlling and should
not be considered or implemented in an isolated manner.
CPM and customer performance indicators are not an end in itself: they have to support
profitably marketing and business or corporate strategies.
- 116 -
7.3
Outlook
Many questions about fuzzy classification still are to be answered. Following open-ended
questions are only few ideas for further research on fuzzy classification in the field of CRM.
How could a customer Data Warehouse (DWH) with fuzzy classes be used for Customer
Performance Measurement (CPM)? How has the architecture of a DWH to be designed?
How can an open source DWH be adapted and implemented in an analytical CRM system?
What has to be considered from the information technology, and what from the business
management point of view? What are the advantages and problems of such a Customer
Performance Measurement System (CPMS) or of a DWH for information management?
How do CPMS or DWH architectures in business practice look like?
How could a hierarchical multidimensional fuzzy classification be combined with a scor-
ing model to evaluate customers in business practice? How do performance indicators have
to be aggregated and weighted (with points or the y-operator), to receive a valid valuation
method to classify fuzzily, for instance, customer attractiveness or customer equity?
How could sharp and fuzzy classification be combined in order to improve managerial de-
cisions? How can basic requirements, which have to be fulfilled sharply (e.g. a customer
has to be profitable in the long-term), be optimally defined within a fuzzy classification?
How does a fuzzy credit rating model for business customers or enterprises could look like?
Which rating criteria, figures, ratios and measures of companies have to be considered in a
hierarchical fuzzy classification of credit rating? What is the benefit of a fuzzy rating of securities, stocks and bonds in the fields of finance and investment?
Unfortunately, this master thesis could not realise empirical studies about the business ap-
plications for fuzzy classification. Future empirical research and case studies may focus on
the implementations of the fCQL toolkit in firms to demonstrate the discussed advantages of
fuzzy portfolio analysis, fuzzy ABC analysis and fuzzy customer segmentation or evaluation.
Since little theoretical and empirical research has been done on market- or customer-oriented
performance measurement, the following questions raised:
How many and which indicators are used for CPM in business practice? Which indicators
are important in companies of different sizes and in different industries? How and why can
customer performance indicators support CRM or marketing controlling in daily business?
What are Key Customer Performance Indicators (KCPIs; "indicators that matter") of suc-
cessful companies in general or in different industries, and why do they matter?
How have customer performance indicators to be defined, operationalised, implemented,
analysed and controlled specifically? Empirical research and case studies of companies
may answer these questions and provide valuable information in oder to improve CRM.
- 117 -
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- 132 -
Appendix
Appendix 1: Excel File for the Calculation of the Membership Degrees
The absolute and normalised (relative) membership degrees of the classified objects to each
class of a fuzzy classification with two attributes (Screenshot 1) or three attributes (Screenshot
2) can be calculated with the Microsoft® Excel file “GammaOperator”. The file is available under:
www.dzumstein.ch/pdf/GammaOperator.xls (or on the CD-ROM enclosed on the back)
Screenshot 1 of GammaOperator for the Calculation of Smith’s Membership Degrees in Table 3
Enter Grade: investment (in Section 3.2),
personalised account,
interest rate, …
Enter value of the
classified element
to attribute 1 Enter value of the
(vertical y-axis) classified element
to attribute 2
(horizontal x-axis)
Enter the value
of gamma (γ) of
the γ-operator
(see page 12)
Result: absolute
Membership
Degrees (MD)
M(Oi│Ck)
Result: normalised Membership
Degrees (MD)
Mnorm(Oi│Ck)
Result: investment, personalised account or
interest rate, …
Screenshot 2 of GammaOperator for the Calculation of Smith’s Membership Degrees in Table 4
Enter value of the
classified element
to attribute 1
(vertical y-axis)
Enter value of the
classified element
to attribute 2
(horizontal x-axis)
Enter value of the
classified element
to attribute 3
(z-axis)
- 133 -
Results: e.g.
calculation of
RFM points (in
Section 3.5)
Appendix
Appendix 2: Checklist for Performing – Fuzzy Strengths/Weaknesses Analysis
1
μ high performance
μ low performance
0
Minor
Major
Major
Minor
weakness weakness Neutral strengths strengths
Marketing
1) Company reputation
2) Market share
3) Customer satisfaction
4) Customer retention
5) Product quality
6) Service quality
7) Pricing effectiveness
8) Distribution effectiveness
9) Promotion effectiveness
10) Sales force effectiveness
11) Innovation effectiveness
12) Geographical covarage
1
0
μ high importance
μ low importance
Very
low
Low
Performance
Medium
High
Very
High
Importance

Finance
13) Cost or availability of capital
14) Cash flow
15) Financial stability
Manufacturing
16)
17)
18)
19)
20)
21)
Facilities
Economics of scale
Capacity
Able, dedicated workforce
Ability to produce on time
Technical manufactering skill
Organisation
22)
23)
24)
25)
Visionary, capable leadership
Dedicated employees
Entrepreneurial orientation
Flexible or responsive
Source: adapted from [Kotler and Keller 2005, p. 55]
134
Appendix
Appendix 3: Value Factors from the Customer Perspective
Value Factor
Literature Reference
Price, monetary sacrifices
[Zeithaml 1988], [Parasuraman et al. 1988, 1994], [Monroe 1990],
;
[Claycomb and Franckwick 1997], [Rust et al. 2000]
Cost reduction,
decreased relationship costs
References, image, status, prestige
Relationship investments
(time, ressources)
Time
Effort, search effort
Energy
Psychological costs, physic costs
Indirect costs
Service provider’s reliability
Service provider’s responsiveness,
quick response time
Accuracy
Flexibility
Efficency
Service delivery
Methodology used
Problem identification
Assurance
Empathy
Seller’s / Service provider’s
competence, core competencies
Service provider’s network (firm/personal)
Service provider’s innovation, creativity
Service provider’s knowledge strategy
Service provider’s expertise, knowledge
Service provider’s experience
Safety
Credibility
Security
Trust
Stability
Continuity
Goals, goal compatibility
Bonds (structural, social, economic)
Brand
fC
[Wilson and Jantrania 1996]
;
[Claycomb and Franckwick 1997]
;
[Wilson and Jantrania 1996], [Claycomb and Franckwick 1997]
;
[Parasuraman et al. 1988, 1994], [Rust et al. 2000]
[Parasuraman et al. 1994], [Claycomb and Franckwick 1997]
[Parasuraman et al. 1988, 1994]
[Grönroos 1998], [Claycomb and Franckwick 1997]
[Grönroos 1992]
;
;
;
;
;
;
[Parasuraman et al. 1988, 1994], [Maister 1993]
;
[Grönroos 1992]
[Grönroos 1992]
[Grönroos 1992]
[Zeithaml et al. 1990], [Rust et al. 2000]
[Patterson and Spreng 1997]
[Patterson and Spreng 1997]
;
;
;
;
;
;
;
;
[Patterson and Spreng 1997], [Wilson and Jantrania 1996]
;
[Patterson and Spreng 1997], [Hansen et al. 1999]
[Maister 1993]
[Hansen et al. 1999]
[Maister 1993]
[Maister 1993]
[Ravald and Grönroos 1996]
[Ravald and Grönroos 1996], [Wilson and Jantrania 1996]
[Grönroos 1992]
[Grönroos 1992]
[Rust et al. 2000]
;
;
;
;
;
;
;
;
;
;
;
;
;
;
Source: adapted from [Leino 2004, pp. 45f]
:: Fuzzy classification not adequate ;: Fuzzy classification with discrete or ;: continuous membership functions possible
135
Appendix
Appendix 4: 170+ Customer Performance Indicators
Legend measuring unit: # = Number/count, € = in euro, % = Percentage/share, Ø = Average (per customer), I = Index,
∑
R = Rating, t = time/period, * = Aggregate value (at enterprise level), * = Indicator at a customer level and at enterprise level
Indicators for: customer investment; customer attitude; customer behavioural intentions; customer behaviour; customer results
Indicators in bold: important indicators often discussed in literature on marketing or accounting
I#
Indicator
Definition or operationalisation Unit Purpose
fc
1) Customer Performance Indicators for Revenue and Profitability (CPIP)
I1
I2
I3
I4
I5
I6
I7
I8
I9
I 10
I 11
I 12
I 13
I 14
I 15
I 16
I 17
I 18
I 19
I 20
I 21
I 22
I 23
I 24
I 25
I 26
I 27
I 28
I 29
I 30
I 31
Number of interested non-buyers, pro- #,%, Indicator for market potential and
∑
;
spective customers for the company
* the company’s potential growth
Number of new customers relative to
Indicator for customer acquisition &
%,*
Conversion rate
;
prospective customers
marketing/communication success
Number of customers, who are gained #,%, Indicator competitive position and
Poached customers from competitors of from the competitors in a period
∑
;
t, * for relative market share (growth)
Number of new customers in a period, #,%, Indicator for customer acquisition &
Number of new customers
∑
;
nominal or relative to custom. portfolio t, * marketing success; future revenue
Number of new customer in a period #,%, Benchmark for competitive position
Relative number of new customers
∑
in comparison with main competitors
t, * & for customer acquisition success ;
Number of first-time customers of a
#,%, Over time sales should rely not on
Number of first-time buyers
∑
;
product (nominal/relative to cu. base) t, * trial but on repeat buyers
Number of customers who bought
#,%, Indicator for loyalty and retention,
Number of customers
∑
from the company in a defined period t, * market share, competitive position ;
Number of repeat buyers of a product #,%, Indicator for customer satisfaction,
Number of repeat buyers
∑
(nominal/relative to customer base)
t, * loyalty/retention, stability of a sales ;
Number of regual buyers (patrons) of #,%, Indicator for loyalty/retention & for
Number of regular buyers
∑
a product (nominal/relative to base)
t, * the stability of sales and revenues ;
Number of migrated or lost customers #,%, Indicator for customer dissatisfacNumber of migrated customers
∑
(nominal or relative to customer base) t, * tion, disloyalty or switching barriers ;
Number or rate of recovered custo#,%, Indicator for customer recovery or
Number of recovered customers
∑
;
mers (nominal or relative to cu. base)
* churn management success
Number of very profitable recovered
#,%, Indicator for customer recovery
Number of profitable recovered cu.
∑
;
customers (nominal or relative)
€,t, * success and efficiency
Number of customer’s bookings or re- #,%, Leading-indicator for customer
Number of bookings or reservations servation by the company in a period €,t,* turnover, contr. margins and profits ;
Number of customer’s requests,
#,%, Indicator for customer’s interest for
Number of requests
tenders or inquiries in a period
€,t,* a product and purchase intention ;
Share or number of responses to
#,%, Indicator for customer orientation
Perfect response
customer requests in a defined period €,t,* and the capacity to serve customer ;
Number of company’s offers, bids or #,%, Indicator for company’s marketing
Number of offers
;
quotations to a customer in a period
€,t,* and sales effort
Number of customer’s orders or ap#,%, Leading-indicator for customer
Number of orders
pointments by the company in a period €,t,* turnover, contr. margins and profits ;
Number of correct, complete and
#,%, Indicator for the company’s capacPerfect orders
;
punctual deliveries to a customers
€,t,* ity to serve customers
Number of customer’s orders (I 17)
Indicator for customers satisfaction
%,*
Order quote (hit rate)
relative to number of requests (I 14)
with the offer & offering efficiency ;
Purchased products/services (nom#,€,Ø, Indicator for marketing and sales
Purchased products or services
;
nal/relative) of a customer in a period %,t,* success and actual revenues
Customer’s purchased volume of a
#,€, Indicator for purchase behaviour,
Purchased volume
product or service per purchase/total Ø,%,t marketing success, actual revenue ;
Customer purchases of a product com- #,€, Indicator for relative intensity of
Heavy usage index
pared a Ø customer in the category
Ø,I,* consumption (purchase behaviour) ;
Average order value of a customer
€,Ø, Indicator for customer purchase
Average order value
per purchase in a defined period
t,* behaviour; turnover, potential, CLV ;
Dates (dd.mm.yyyy) of a customer’s
Indicator for customer purchase
Ø,t,*
Purchase dates
:
purchases of company’s products
behaviour
Rhythm or regularities of customers
Indicator of customer usage and
Ø,t,*
Purchase rhythm
;
purchases in a defined period
purchase behaviour
Customer’s intensity, e.g. number, vol- I,R, Indicator for customer usage and
Purchase intensity
;
ume or value of purchases in a period t,* purchase behaviour
The length of time since a customer’s
Indicator for purchase behaviour &
t,Ø,*
Recency
;
last purchase (RFM method)
for number of active customers
Frequency of a customers purchases
Indicator for customer behaviour
t,Ø,*
Frequency
;
in defined period (RFM method)
for customer loyalty or retention
Monetary value of a customer’s total
€,Ø, Indicator for customer turnover,
Monetary value
;
purchases in a period (RFM method)
R,* profitability and CLV
Operiational (free) cash flow received
Indicator for customer payment
€,t,*
Cash flow
;
from a customer in a defined period
behaviour
Customer’s total turnover (here synon- €,%, Indicator for company’s ability to
Customer turnover (or sales)
;
ymously: sales/revenues) in a period Ø,t,* fulfil customer needs, create value
Number of prospective customers
136
Appendix
I 33
I 34
I 35
I 36
I 37
I 38
I 39
I 40
I 41
I 42
I 43
I 44
I 45
I 46
I 47
I 48
I 49
I 50
I 51
I 52
I 53
I 54
I 55
I 56
I 57
I 58
I 59
I 60
I 61
I 62
I 63
I 64
I 65
I 66
I 67
I 68
Amount of turnover of a first-time
€,Ø, Indicator for customer potential
;
buyer (of a new customer) in a period %,t,* and attractiveness
Amount of turnover of a regular
€,Ø, Indicator for customer potential,
Turnover of repeat/regular buyers
;
customers in a defined period
%,t,* attractiveness and profitability
Total, cumulative turnover (or sales)
€,Ø, Indicator for loyalty and customer
Cumulative turnover (or sales)
of a customer since his first purchase %,t,* lifetime value or customer equity ;
Customers sales growth (nominal or
%, Indicator for customer potential
Growth of turnover (or sales)
;
relative to total sales growth)
Ø,* and attractiveness
Sales premiums or promotional ince- #,€,Ø, Customer investment indicator to
Sales premiums
;
tives given to cu. for buying a product %,t,* influence purchase behaviour
Discounts, reductions or gifts given to #,€,Ø, Customer investment indicator to
Customer or new customer discounts (new) customers dependent on sales %,t,* acquire new customers
;
Share of demand of all products a
Indicator for commitment (I 131) &
%,*
Customer penetration
customer buys from the company
customer (add-on selling) potential ;
Share of customer’s fulfilment by the #,€,Ø, Indicator for commitment (I 131)
Share of wallet
;
company rel. to demand of a product
%,* and (repurchase) potential
Share of a new customer’s fulfilment #,€,Ø Indicator for usage behaviour,
Share of wallet of new customers
;
of demand relative to his total demand ,%,* customer turnover and potential
Customer’s share of wallet relative to
%, Benchmark for the company’s
Relative share of wallet
;
company’s main competitor(s)
€,* competitive position
Share of customer’s volume at the
#,€, Indicator for the dependency of the
Market share of customer
;
total (revenue/unit) market share
%,* company on a customer
Sales revenue or unit sales as a
#,€, Indicator for market strength and
Revenue/unit market share
∑
;
percentage of market sales revenue
%, * competitive position
Company’s market share divided by
#,€, Indicator for market strength and
∑
;
largest competitor’s market share
%, * competitive position
Growth of revenue/unit (I 43) or re#,€,Ø, Indicator for marketing success
Growth of market share
∑
;
lative market share (I 44) in a period %,t, * and industry attractiveness
Nominal or relative growth of the total #,€,Ø, Indicator for industry attractiveness
Market growth
∑
;
market demand of a product/category %,t, * and market potential
Describes the total demand of all
#,€,Ø, Indicator for customer need and
Market demand
∑
;
consumers for a product in a period
%,t, * wishes (industry attractiveness)
Purchasers of a product category as
Indicator for (product/service) ca∑
%, *
Market penetration
;
a percentage of total population
tegory acceptance by consumers
Market share of a product in comparIndicator for the brand/product
∑
%, *
Brand/product penetration
;
son to total market share of a product
acceptance by consumers
Number of additional but less ex#,€,Ø, Indicator for customer satisfaction,
Down-selling (rate)
;
pensive products sold to a customer
%,t,* loyalty or retention, and turnover
Number of customer’s purchases of
#,€,Ø, Indicator for customer satisfaction,
Cross-selling (rate)
;
different products from the company
%,t,* loyalty or retention, and turnover
Probability of selling additional, other %,€, Indicator for customer’s potential,
Cross-selling potential
;
products to an existing customer
R,t,* future turnover and profits
Number of additional, upgrade, more #,€,Ø, Indicator for customer satisfaction,
Up-selling (rate)
;
expensive products sold to a customer %,t,* loyalty or retention, and turnover
Expressed intention of customer to
#,€,Ø Leading-indicator for customer’s
Cross-buying intention
;
purchase different products
,%,t,* future turnover and profits
Expressed intention of a prospect or
Leading-indicator for customer
%,*
Repurchase intentions
;
customer to (re)purchase a product
satisfaction, loyalty and retention
Number of customer’s repurchases
€,%, Indicator for customer value, satisRepurchases (or repurchase rate)
;
(or repurchase rate) of a product
Ø,t,* faction, loyalty and/or retention
Probability of intention of existing
Indicator for customer repurchases
%,*
Probability of repurchases
customers to repurchase a product
(satisfaction, loyalty or retention) ;
Customer purchased product mix
Indicator for customer needs,
Customers product mix at company by the company or at the competitor #,€,* wishes and purchase behaviour
:
Customer’s average number/volume
#,€, Indicator for customer needs,
Average product portfolio of customer of purchases of products in a period
;
Ø,t,* wishes and purchase behaviour
Customers sensitivity to notice and
Ø,I, Indicator for probability to switch or
Price sensitivity
;
react to changes in prices
€,R,* repurchase and for loyalty
The additional charge a satisfied cust- €, Indicator for product quality, cusPrice premium
omer pays for a product or service
%,* tomer value, satisfaction & loyalty ;
Method or medium, how a customer
Indicator for purchase or payment
-,*
Method of payment
pays, e.g. cash, credit card, cash card
behaviour and for creditworthiness :
Number of days, weeks or months of #,Ø, Indicator for customer creditworPunctuality of payment
;
a customer’s delay in payment
t,* thiness and solvency
A customer’s total number and sum
#,€, Indicator for customer creditworNumber or sum of outstanding bills
;
of outstanding bills or accounts
Ø,t,* thiness and solvency
Amount or share of customer’s or
#,€,% Indicator for customer creditworAmount or share of bad debt losses new customer's with bad dept losses Ø,t,* thiness and solvency
;
Customer’s payment history or
Ø,t, Indicator for customer creditworPayment history
;
punctuality of payment in a period
I,R,* thiness and solvency
A new customer’s payment history
Ø,R, Indicator for customer creditworPayment history of new customers
;
or punctuality of payment in a period
t,* thiness, solvency
Customer’s creditworthiness or
Ø,I, Indicator for the ability, intention
Creditworthiness
credit rating; customer solvency
R,* and financial capability to pay bills ;
Turnover of first-time buyers
Add-on-selling
I 32
137
Appendix
I 69
I 70
I 71
I 72
I 73
I 74
I 75
I 76
I 77
I 78
I 79
I 80
I 81
I 82
I 83
I 84
I 85
I 86
Customer’s total turnover (or sales)
€,% Indicator for customer turnover, fix
minus total variable costs in period
Ø,t,* costs and profit
Customer’s turnover (net sales) minus €,% Indicator for customer turnover
Customer contribution margin I
costs of goods/services sold in period Ø,t,* and profit
Contribution margin I (I 70) minus
€,% Indicator for customer turnover,
Customer contribution margin II
marketing costs (I 101)
Ø,t,* marketing costs and profit
Contribution margin II (I 71) minus
Customer contribution margin III
customer-driven sales costs (I 95)
Ø,t,* marketing costs and profit
Margin III (I 72) minus customer driven €,% Indicator for customer turnover,
Customer contribution margin IV
transport & service costs (I 96, I 97)
Ø,t,* transport/sales costs and profit
Contribution margin (I-IV) of a new
Contribution margin of new customer customer in a defined period
Ø,t,* customer lifetime value, potential
Customer’s total turnover (I 31) minus €,Ø, Indicator for customer and comCustomer gross profit
total costs (I 102) in a period
t,* pany performance and success
Gross profit (I 75) minus taxes, int€,Ø, Indicator for customer and comCustomer net profit
erests, depreciation & other expenses t,* pany performance and success
Realised (or expected) positive or
€,Ø, Leading indicator for potential and
Growth of customer profit
negative growth of gross or net profit
t,* company performance or success
Difference between total turnover and €,%,Ø Indicator for customer and comCustomer profitability
costs associates with a CR in a period t,R,* pany performance and success
The total of the discounted lifetime
€,%,Ø Indicator for customer and comCustomer equity
values of all customers of a company t,R,* pany performance and success
The present, discounted value of all €,%,Ø Indicator for customer and comCustomer Lifetime Value (CLV)
cash flows over the length of the CR
t,R,* pany performance and success
The present value of all cash flows
€,%,Ø Indicator for prospect potential and
Prospect Lifetime Value (PLV)
over the length of the prospect’s CR
t,R,* future customer lifetime value
The degree of overall attractiveness
Indicator for customer & company
I,R,*
of a customer for a company
performance (for segmentation)
The customer’s future potential, e.g.
€,I, Indicator for future customer and
Customer potential
for sales, contribution or profit
R,t,* company performance or success
A new customer’s future potential, e.g. €,I,Ø, Indicator for future customer and
Potential of new customers
volume, sales, contribution or profit
The change of customer’s potential
Development of customer’s potential in a defined past or future period
The change of customer’s potential
Development of customer’s industry in a defined past or future period
Gross margin (contribution margin)
;
;
;
;
;
;
;
;
;
;
;
;
;
;
;
;
;
;
2) Customer Investment Indicators (CII)
I 87
Acquisition costs
I 88
Acquisition efficiency
I 89
Retention costs
I 90
Retention efficiency
I 91
Recovery costs
I 92
Recovery efficiency
I 93
Administration costs
I 94
Transaction costs
I 95
Sales costs
I 96
Logistic costs
I 97
Service costs
I 98
After sales costs
I 99
Communication costs
I 100 Contact costs
I 101 Marketing costs
I 102 Total customer’s costs
I 103 Return on Sales (ROS)
I 104 Return on Customer (ROC; ROCI)
All costs incurred to acquire a new
customer (e.g. advertising, marketing)
Ratio of customer acquisition costs
(I 87) to turnover of first buyer (I 32)
All costs incurred to bind a customer
(e.g. retention programs, offers)
Ratio of customer acquisition costs
(I 89) to sales of customers (I 34)
All costs incurred to recover a customer (e.g. recovery actions, offers)
Ratio of customer recovery costs (I
91) to sales of recovered customers
Net costs of administration (wages of
administrative staff, materials, etc.)
All costs incurred in making an
exchange of products or services
All costs incurred to sale a product
or service to a customer
All costs associated with logistics (e.g.
order, inventory, handling & delivery)
All costs associated with customer
service (e.g. call center, consulting)
All costs incurred after the sale (e.g.
warranty, helpdesk, complaints, etc.)
All costs associated with customer
communication (e.g. ad, mail, phone)
All costs incurred to contact a customer (e.g. phone calls, travel expenses)
Total expenditures on marketing
activities in a defined period
All customer-related costs in a
defined period
ROS is calculated by dividing net
profits by sales (I 31: I 76)
ROC is calculated by dividing cu. net
profits by total costs (I 76: I 102)
138
€,%,
Ø,t,*
I,R,
∑
*
€,%,
Ø,t,*
I,R,*
€,%,
Ø,*
I,R,*
€,%,
Ø,t,*
€,%,
Ø,*
€,%,
t,*
€,%,
Ø,t,*
€,%,
Ø,t,*
€,%,
Ø,t,*
€,%,
Ø,t,*
€,%,
Ø,t,*
€,%,
Ø,t
€,%,
Ø,t,*
%,
∑
R, *
%,
R,*
Indicator for the company’s effort
;
to acquire or gain new customers
Indicator for the ability to acquire
;
efficiently new customers.
Indicator for the company’s effort
;
to bind and obtain customers
Indicator for the ability to bind
;
efficiently current customers.
Indicator for the company’s effort to
recover or reactivate old customers ;
Indicator for the ability to recover
;
efficiently migrated customers.
Indicator for customer orientation
and for customer efficiency (I 108) ;
Indicator for selling effort/efficiency
& customer contribution margin III ;
Indicator for selling effort/efficiency
& customer contribution margin III ;
Indicator of supply chain mmgt., &
for customer contribution margin IV ;
& customer contribution margin IV ;
& customer contribution margin IV ;
Indicator for the company’s market;
ing effort and efficiency
and for customer efficiency (I 108) ;
Indicator for the marketing effort &
;
customer contribution margin II
Indicator for customer profitability
;
and customer efficiency
Indicator for the company’s ability
;
to generate profits from sales
;
to generate profits from customers
Appendix
I 105 Return on Relationship (ROR)
I 106 Return on Customer Satisfaction
I 107 Return on Customer Retention
I 108 Customer efficiency
I 109 Return on Marketing (ROM; ROMI)
I 110 Return on Investment (ROI)
ROR is calculated by dividing cu. net
profits by total costs (I 76: I 102)
Net profits of customer satisfaction
divided by cu. satisfaction mmgt costs
Net profits of customer retention
divided by cu. retention costs ()
Customer contribution margin
relative to capacity shortage/costs
Total revenues of marketing divided
by total marketing costs
Total revenues divided by total costs
or investment
%,
R,*
%,
R,*
%,
R,*
%,
R,*
%,
R,*
%,
∑
R, *
Indicator for company’s ability to
generate profits from relationships
Indicator for the company’s ability/
effectiveness to satisfy customers
& effectiveness to bind customers
The company’s ability to optimally
serve customers at low costs
Indicator for marketing efficiency
and marketing success
Indicator for efficiency and success
of investments
;
;
;
;
;
;
3) Customer Relationship Indicators (CRI)
Share of target customers which is
%,€,Ø Indicator for advertising effects
;
aware of a brand (recall/recognition) I,R,* and communication success
Percentage of population/prospectives %,Ø, Indicator for advertising effects
Ad awareness
;
that is aware of a brand’s advertising I,R,* and communication success
Percentage of customers who demoIndicator of the familiarity with a
R,*
Brand/product knowledge
strate product knowledge or beliefs
product beyond name recognition ;
First brand comes to customers mind
Indicator for the salience of a
R,*
Top of mind
;
asking about a product of a category
product or service
Indicates, how a company is
Indicator for customer satisfaction,
R,*
Corporate image
perceived by a (prospective) customer
repurchases and recommendation ;
Indicates, how a company’s product is
R,*
Product or service image
perceived by a (prospective) customer
repurchases and recommendation ;
The a company’s focus on meeting
Indicator for a company’s ability to
R,*
;
the needs and wants of it customers
identify market opportunities
A customer’s rating of the perceived
Indicator for customer expectations
R,*
Perceived product quality
quality of a given product.
or satisfaction and product quality ;
A customer’s rating of a product comBenchmark for customer expectaR,*
Relative perceived product quality
pared to other products in the market
tions or satisfaction, product quality ;
Customers perceived quality of a
Indicator for customer expectations
R,*
Perceived service quality
provided service
or satisfaction and service quality ;
Customers perceived cheapness or
Indicator for the company’s price
R,*
Perceived cheapness/expensiveness expensiveness of a offer
competitiveness (and satisfaction) ;
Customer perceived price-perform%,I, Indicator for the company’s price
Perceived price-performance ratio ance ratio or value for money.
R,* competitiveness (and satisfaction) ;
The (subjective) customer rating
Indicator for created utility & (basic
R,*
Perceived value or utility
about the perceived value of product
/added) value of a marketing offer ;
The total value that a customer
Indicator for the created utility and
R,*
Customer value
;
receives from a offer
value of a marketing offer
Degree of fulfilment of customers’ ex- Ø,I, Indicator for customer needs and
Fulfilment of customers’ expectations pectations/requirements to a product R,* expectations, satisfaction & loyalty ;
The overall customer satisfaction with Ø,I, Indicator for customer value and
the offer of a company
R,* likelihood of repurchases (loyalty) ;
Customer satisfaction compared with Ø,I, Benchmark of the company’s
Relative customer satisfaction
;
competitors/national satisfaction index R,* ability to satisfy customers
The degree of customer satisfaction
Ø,I, Indicator for product quality and
Customer satisfaction with product
with a certain product or product
R,* likelihood of repurchases (loyalty) ;
The degree of customer satisfaction Ø,I, Indicator for service quality and
Customer satisfaction with service
;
with a service
R,* likelihood of repurchases (loyalty)
Activation level in purchase situations
Indicator for relationship quality &
R,*
Customer involvement
;
or emotional proximity to a product
likelihood of repurchases (loyalty)
A customer’s internal intention to
Indicator for likelihood of repurchR,*
Customer commitment
;
continue a valuable relationship
ases (loyalty) & recommendations
Feelings of affection of a customer for
Indicator for likelihood of repurchR,*
Customer attachment
;
the company and its products/services
ases (loyalty) & recommendations
Degree of customers trust or confiIndicator for relationship quality &
R,*
Customer trust
;
dence in the company and products
likelihood of repurchases (loyalty)
Committed customer who repurchase Ø,I, Indicator for satisfaction, attachCustomer loyalty
;
by the company and recommend it
R,* ment , retention & future revenues
Customer’s perceived dependency
Indicator for the company’s switchI,R,*
Customer perceived dependence
;
on the company or on its products
ing barriers and customer retention
Company’s financial, informational or
Indicator for number of customers
I,R
Dependency on a customer
;
cooperative dependency on customer
and market share of customer
The number of customers retained
%,Ø, Indicator for satisfaction, loyalty or
Customer retention (retention rate) (relative to the customer base)
;
I,R,* ability of the firm to bind customers
Retention rate in consideration of nonIndicator for satisfaction, loyalty or
I,R,*
Adjusted customer retention rate
influence able migration (e.g. deaths)
ability of the firm to bind customers ;
Retention rate (I 137) weighted e.g.
Indicator for the ability of the firm
I,R,*
Weighted customer retention rate
;
by customers turnover or profit
to bind profitable customers
Customers basic willingness to switch
I,R,*
Willingness to switch
from the company to a competitor
loyalty, retention and lifetime value ;
I 111 Awareness (brand awareness)
I 112
I 113
I 114
I 115
I 116
I 117
I 118
I 119
I 120
I 121
I 122
I 123
I 124
I 125
I 126
I 127
I 128
I 129
I 130
I 131
I 132
I 133
I 134
I 135
I 136
I 137
I 138
I 139
I 140
139
Appendix
Customers intention to switch over
from the company to a competitor
Customer’s future probability of
Switching probability
switching to a competitor
Period, after which the half of all
Customer half-life
acquired customers have left again
Number of migrated customers
Churn rate (migration rate)
relative to customer base
Height of the company’s different
Switching costs (switching barriers) switching barriers (or switching costs)
Height of technical/contractual switchTechnical/contractual switching costs ing barriers (others barriers see p. 74)
Length of time (weeks, months) of a
Duration of customer relationship company’s relationship with customer
Intensity or strength of the company’s
Intensity of customer relationship
relationship with a customer
Customer’s perceived (subjective)
Perceived relationship quality
relationship quality with the company
Cust. lifetime cycle: entrance, growth,
Position in customer lifetime cycle
maturation, saturation and dissolution
I 141 Intention to switch
I,R,*
I 142
%,*
I 143
I 144
I 145
I 146
I 147
I 148
I 149
I 150
I,R,
∑
*
%,I,
∑
R, *
I,R,*
I,R,*
Ø,t,
R,*
R,*
I,R,*
R,*
Indicator for number of migrated
customer and loyalty or retention ;
Indicator for customer loyalty and
retention (ability to bind customers) ;
Indicator for customer retention &
;
customer lifetime value or equity
Indicator for customer retention &
;
customer lifetime value or equity
Indicator for relationship quality,
and customer attachment & loyalty ;
Indicator for service/product quality,
satisfaction, loyalty and retention ;
Indicator for cross-/up-selling
potential & customer lifetime value ;
4) Customer Recommendation Indicators (CReI)
I 151 Intention to recommend
I 152 Number of recommendations
I 153 Recommendation intensity
I 154 Recommendation frequency
I 155 Potential reference recipients
I 156 Role as opinion leader
Customers’ intention or plan to recommend products to friends, relatives,...
Number of successful recommendtions of a customer in a given period
Intensity of customer’s recommendation to friends, relatives,…
Frequency and rhythm of a customer
recommendation to friends, relatives…
Number and importance of potential
of a customer
Customer’s social, medial or public
position; influence to other consumers
R,*
#,
R,*
R,*
#,
R,*
#,
R,*
R,*
commitment and loyalty
loyalty and acquisition efficiency
Indicator for customers social
position and role as opinion leader
Indicator for product or corporate
image, brand/company awareness
;
;
;
;
;
;
5) Customer Information Indicators (CInfI)
Customers general intention/ willingIndicator for involvement, relationR,*
;
ness to have a dialog or conversation.
ship quality & response behaviour
Consulting intensity (in hours or €) of #,€, Indicator for customer orientation,
Consulting or helpdesk intensity
;
a consultant or employee in a period R,t,* relationship intensity and quality
Number of customer initiated contacts #,€, Indicator for different customer
Customer initiated contacts
to the company in a defined period
R,t,* behaviour (sales, satisfaction, etc.) ;
Number of customer initiated contacts #,€, Indicator for customer orientation
Company initiated customer contacts to the company in a defined period
;
R,t,* and intensity of relationship
Number of companies contacts with
#,€, Indicator for customer orientation
Contact intensity with new customers a new customers in a defined period R,t,* and intensity of relationship
;
Number of contacts with a customers #,€, Indicator for customer orientation
Contact intensity with customers
;
(regular buyer) in a defined period
R,t,* and intensity of relationship
Number, quality and importance of a
#,R, Indicator for customer satisfaction
Number/quality of suggestions
;
customer’s suggestions in a period
t,* and product expertise
Number and quality of customer’s
#,Ø, Indicator for product/service quality
Number/quality of complaints
complaints in a defined period
R,t,* and customer satisfaction/loyalty ;
Degree of customer’s satisfaction with R, Indicator for service quality and
Complaint satisfaction
company’s reaction to the complaint
Ø,* customer satisfaction and loyalty ;
Customer’s demands (e.g. numbers, #,€,Ø, Indicator for usage behaviour,
Demands of technical services
duration, costs) of technical services
R,t,* satisfaction and product expertise ;
Customer’s number/value of returned #,€,R, Indicator for product/service quality
Number of returns (return rate)
∑
or rejected products or services
t,* * and cust. expectations/satisfaction ;
Customers know-how or expertise
Indicator for usage behaviour,
R,*
Product expertise
;
concerning the company or products
experience and involvement
Percentage of customers respond to
%,I, Indicator for customer purchase beResponse rate or behaviour
a communication or marketing action
R,* haviour, value, purchase intention ;
Customer’s response probability to a
%, Indicator for response rate and
Response probability
;
future communication/marketing action R,* communication success
I 157 General intention to dialog
I 158
I 159
I 160
I 161
I 162
I 163
I 164
I 165
I 166
I 167
I 168
I 169
I 170
6) Customer Cooperation Indicators (CCI)
I 171 Intention to cooperate
I 172 Expertise for cooperation
I 173 Potential cooperation topics
Customer’s intention for a cooperLeading-indicator for attachment,
R,t,*
tion with the company (e.g. for R&D)
relationship quality and intensity
Customer’s know-how, knowledge, exIndicator for purchase behaviour,
R,*
pertise or information for cooperation
Possible cooperation topics, fields,
Indicator for purchase behaviour,
R,*
technologies, products/services, etc.
;
;
;
Source: partly adapted from [Reinecke 2004, pp.267ff], [Neckel and Knobloch 2005, pp.199], [Bauer et al. 2006, pp.106ff], [Farris et al. 2006, pp.12ff, 129f], [Davis 2006, pp.4ff]
140
Appendix
Appendix 5: Criteria for Fuzzy Market Segmentation
Demographic
Geographic
Variables
Geographic units
Climate
Regions
City or metro size
Population density
Employment
Age
Gender
Race
Nationality
Religion
Generation
Occupational group
Occupation
Family life cycle
Family size
Education
Social class
Psychographic
Income
Psychographic lifestyle
Personality
Sinus-Milieus®
Social status
Basic values
10 Milieus
Euro-Socio-Styles
®
Reality vs. mirage
Permanence/change
8 Styles
Behavioural
Behavioural occasions
Benefit
Buying behaviour,

Price sensibility
Service sensibility
Brand awareness
Demand for quality
Involvement
Typical breakdowns
fC
Nations, states, cantons, regions, cities, communes, neighbourhoods, streets
Northern, southern
South-north, west-east (e.g. German speaking part of Switzerland, Romandy, Ticino,
Grisons); mountains, midland
<5000, 15’000-20’000, 20’001-50’000, 50’000-100’000, 100’001-200’000, 200’001500’000, 500’000-1Mio., >1Mio, ...
Rural, suburban, urban; Resident persons per square kilometre: <10,0; 10,0-24,9; 2549.9; 50-99,9; 100-249,9; 245-499,9; 500-999,9; ≥1000)
Low, …, high (<250, 250-449, 450-549, 550-749; ≥750 employed persons/1000 habitants)
:
:
Under 2, 2-6, 6-11, 11-15, 15-20, 20-26, … , 65+
Male, female [no women/men, …, uni sexe, only women/men]
Black, White, Asian, Hispanic, Arabic, …
British, French, German, Swiss, Italian, Spanish, Turkish, Polish, Swedish, …
Catholic, Protestant, Jewish, Muslim, Hindu, Buddhist, other, none
Baby boomers; Generation Xers (68ers, 70ers, 80ers, 90ers), …
Worker, sales, clerical, officials & proprietors, professional & technical, managers,...
In education (school, university), full-time/part-time work, housewife, unemployed,…
[Young, single], [young, married, no children], [young, married, youngest child
under 6], [older, married, children], [older, married, adult children], [older, single], …
1, 2, 3, 4, 5 and more
Grade school or less, high school, … , college graduate (low vs. high educated)
Lower lowers, upper lowers, working, middle, upper middle, lower uppers, upper uppers
Household income, number of persons in the household with own income,
household income per capita, personal net income, disposable income, ...
;
;
;
;
;
;
:
:
:
:
:
:
:
;
;
;
;
;
;
Culture-oriented, sports-oriented, outdoor-oriented, etc.
Compulsive, gregarious, authoritarian, ambitious, etc.
Lower, middle, higher social status
Traditional (duty, order), modern (Individualisation, self-realisation, hedonism, pleasure)
and pro-active change (patchwork, virtual society, multi-options);
Frugal traditional, materialistic workers, traditional commoner, status oriented, well established, post-materialist, new middle class, escapists, experimentalists, modern performer
Being vs. possessions
Peace and personal security vs. living ones’ own emotions
Secure world, steady world, standing world, authentic world, new world, cosy tech world,
crafty world, magic world
Regular occasion, special occasion
Quality, service, economy or speed
Very low, …, very high price sensibility or consciousness
Very low, …, very high demand for service
Very low, …, very high brand awareness or brand identity
Very low, …, very high demand for quality
Very low, …, very high involvement
;
;
;
;
;
;
:
;
;
;
;
;
;
;
Types of buying decisions Processes, e.g.

Extensive
Limited
Routine
Impulsive
User status
Usage rate
Readiness stage
Attitude
Adoption time
Loyalty status
High price, rarely purchased, careful evaluation and choice
Medium price, moderate information demand
Low price, frequently purchased, few alternatives
Response to an intensive stimuli, no evaluation of alternatives
Non user, ex-user, potential user, first time user, regular user
Very light user, light user, medium user, heavy user, very heavy user
Unaware, aware, informed, interested, desirous, intending to buy
Hostile, very negative, negative, indifferent, positive, very positive, enthusiastic
Innovators, early adopters, early majority, late majority, laggards
None, very low, medium, strong, very strong, absolute
;
;
;
;
;
;
;
;
;
;
Source: following [Kotler et al. 2005, p. 399]
141
Appendix
Appendix 6: Attributes, Classes, Terms, Domains and Contexts of Credit Rating
Attribute (Aijkl) &
hierarchy level Li
1 2 3
4
Membership
functions (μ)
Classes
C1) creditworthy
C2) personally not
creditworthy
C3) materially not
creditworthy
Family situation (A311)
Employment (A312)
Personal creditworthiness (A21)
C1-1) personally
creditworthy
C1-2) job circumstances
not creditworthy
C1-3) family circumstances
not creditworthy
C1-4) personally not
creditworthy
μpers. creditworthy +
μmat. creditworthy
μpers. not creditworthy +
μmat. creditworthy
μpers. creditworthy +
μmat. not creditworthy
μpers. not creditworthy +
μmat. nicht kw
not creditworthy
creditworthy
μfamily c. creditworthy +
μjob c. creditworthy
μfamily c. creditworthy +
μjob c. not creditworthy
μfamily c. not creditworthy +
μjob c. creditworthy
μfamily c. not creditworthy +
μjob c. not creditworthy
Number of children (A4114)
Age of children (A4115)
Profession (A4121)
Qualification (A4122)
Employer (A4123)
Duration of employment(A4124)
Income (A321)
μearning c. creditworthy +
μexpenses c. creditworthy
μearning c. creditworthy +
μexpenses c.not creditworthy
μearning c. not creditworthy +
μexpenses c. creditworthy
μearning c. not creditworthy +
μexpenses c. not creditworthy
Outgoings (A322)
Rent, mortgage rate (A4221)
Alimonies (A4222)
Financial obligations (A4223)
Reason for credit (A4224)
other (A4225)
c
Legend: scores assigned to values of : continuous (
discrete: {very bad,
bad, insufficient}
cont.: [0,99]; (50100)
discrete: {sufficient,
good, excellent}
[0, 100]; (0,50)
Family status (A4113)
other (A4215)
cont.: [0,99]; (0,49.9)
[0, 49]; (0, 24.9)
personally
creditworthy
Civil status (A4112)
Account information (A4214)
Context
K(Aijkl)
s/d
Family situation
not creditworthy
Family situation
creditworthy
18, ..., 100
single, married, divorced,
separated, widowed
single, single mother,
concubinage, married
0, 1, 2, 4, >4
0, ... , 30 years
Employment not
creditworthy
Employment
creditworthy
[low, … , high qualified]
[labour contract, <1, >1,
>3 years]
-
Net income (A4211)
Property (A4212)
Loan securities (A4213)
continuous :
[0,200]; (0,100)
d
discrete :
{very bad,
bad, insufficient,
sufficient, good,
excellent}
personally
not creditworthy
Age (A4111)
C2-1) materially
creditworthy
C2-2) Expenses circumstances not creditwort.
C2-3) Earning capacity
not creditworthy
C2-4) materially not
creditworthy
Scores S(Aijkl)
[scores],(%)
c
other (A4125)
Material creditworthiness (A22)
Overall creditworthiness (A1)
C4) not creditworthy
Terms T(Aijkl)
[50, 100]; (25, 50)
s/d
[25, 50]; (12.5, 25)
d
[0, 10]; (0, 5)
s
[0, 10]; (0, 5)
d
[0, 10]; (0, 5)
s
[0, 10]; (0, 5)
d
[0, 10]; (0, 5)
s/d
Income not
creditworthy
Income
creditworthy
[0, ≥ 10'000]
[0, ≥ 1'000'000]
[very low, ... , very high]
[High, …, low negative;
low, ..., very high]
Outgoings not
creditworthy
Outgoings
creditworthy
[very low, … , very high]
[very high, ... , none]
[very high, ... , none]
[un-, ... , important]
d
[0, 24]; (0, 12.49)
[0, 50]; (0, 25)
[25, 50]; (12.5, 25)
d
[0, 10]; (0, 5)
d
[0, 10]; (0, 5)
d
[0, 10]; (0, 5)
s/d
[0, 10]; (0, 5)
s/d
[0, 10]; (0, 5)
Materially
not creditworthy
Materially
creditworthy
[0, 24]; (0, 12.49)
[0, 50]; (0, 25)
[0, 49]; (0, 24.9)
[0, 100]; (0,
s/d
50)
[50, 100]; (25, 50)
s/d
[0, 24]; (0, 12.49)
[0, 50]; (0, 25)
[25, 50]; (12.5, 25)
s/d
[0, 10]; (0, 5)
s/d
[0, 10]; (0, 5)
s/d
[0, 10]; (0, 5)
s/d
[0, 10]; (0, 5)
s/d
[0, 10]; (0, 5)
d
[0, 24]; (0, 12.49)
[0, 50]; (0, 25)
[25, 50]; (12.5, 25)
s/d
[0, 10]; (0, 5)
s/d
[0, 10]; (0, 5)
s/d
[0, 10]; (0, 5)
s/d
[0, 10]; (0, 5)
s/d
[0, 10]; (0, 5)
) or : discrete membership functions ( );
142
s/d
: both possible
Appendix
So eine Arbeit wird eigentlich nie fertig, man muss sie für fertig erklären,
wenn man nach Zeit und Umständen das Mögliche getan hat.
Johann Wolfgang von Goethe
143
Statement
FACULTE DES SCIENCES ECONOMIQUES ET SOCIALES /
WIRTSCHAFTS- UND SOZIALWISSENSCHAFTLICHE FAKULTÄT
DEKANAT
BD. DE PÉROLLES 90
1700 FRIBOURG
ERKLÄRUNG
Ich versichere, dass ich die vorstehende Arbeit selbständig angefertigt und
entsprechend den Grundsätzen wissenschaftlicher Ehrlichkeit abgefasst habe.
Es ist mir bekannt, dass andernfalls die Abteilung gemäss dem Abteilungsbeschluss vom 28.11.1984 das Recht hat, den auf Grund dieser Arbeit
verliehenen Titel zu entziehen.
Fribourg, den 7. März 2007
............................................
Darius Zumstein
- 144 -