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Präsentation - e

Auswirkungen von Big Data Analysen
auf das Online-Marketing
e-marketingday rheinland
- IHK Initiative Rheinland -
Wuppertal, 13. April 2016
Auswirkungen von Big Data Analysen auf das Online-Marketing
Heutige Unternehmen „verdursten“ in einem Meer
aus Daten
Bis zu 90 % der heutigen
Daten sind unstrukturiert
13.04.2016
Nur 20 % der Daten können
mit herkömmlichen Systemen
nutzbar gemacht werden
Arbeitsthese
Der immer mobilere Mensch soll
schnellere und bessere Entscheidungen
zu unstrukturierteren Problemstellungen treffen,
wobei ihm immer mehr Daten
in vielfältigerer Form zur Verfügung stehen.
13.04.2016
Herausforderungen von Big Data

Big Data – na und?

Big Data beschreibt keinen Nutzen

Big Data formuliert kein Problem

Beschreibung des Business Case = Herausforderung

TROTZ fachlich formulierter Frage:

zielführende Auswertung verschiedener Quellen aufwändig

bestehende IT-Umgebung nicht „Big-Data-fähig“
13.04.2016
Liest Du noch oder analysierst Du schon?
„Es scheint, also ob Sie alle
Daten hätten –
was ist also das Problem?!“
13.04.2016
Ihre Entscheidungsunterstützung soll sich
systematisch, strategisch weiterentwickeln können
Reifegrad
Cognitive
Computing
Predictive Analytics
Planung & Forecast
Reporting
descriptive
13.04.2016
predictive
prescriptive
Zukunftsorientierung
Die besondere Kombination von drei Eigenschaften
unterscheidet Watson von anderen Technologien
Verarbeitet natürliche Sprache und versteht Komplexität unstrukturierter Daten
Lernt mit jeder
Iteration und
Interaktion der
Anwender
13.04.2016
Wendet fortgeschrittene Analysemethoden an, um
Antworten logisch
zu gewichten und
auszuwerten
Google Search Approach
13.04.2016
Watson Jeopardy!
13.04.2016
A travel services company uses cloud-based social media
analytics to detect the desires of customers and drive more sales
13.04.2016
A fashion brand uses cloud-based social media
analytics to successfully expand into foreign markets
72% reduction
in time to market for new products
Pricing strategy
aligned to local market conditions
Differentiated brand
to established retailers in the market
Brand values
which resonate with the target
customers
Powered by
 Real time Social Media data
 IBM SMA 1.3 (DB2, Cognos)
 IBM SPSS Modeler
 IBM Psycholinguistics
 Next Best Action
13.04.2016
Business challenge: Although the fashion brand had a good understanding
of its home markets, it realised that the fashion trends, attitudes, values and
market structure in a foreign market were different. Social conversations
provide clues but how can the company reveal the underlying insights and
successfully expand into a new foreign market?
The socially aware solution: The fashion brand uses social media
conversions to understand consumer preferences and the market
landscape to build brand equity in a new market. And in the fast moving
world of fashion, real time social conversations provide the insights needed
by product designers to bring new wears to market quickly and boost profits.
By using advanced analytics and real time social media postings, the fashion
brand positioned itself for success in a new market. Trend alerts enable
product designers to bring new lines to market at speed. The outcome,
social intelligence driving real financial results.
A Telecom company uses cloud-based social media analytics to
proactively retain customers before they have decided to leave
30% reduction
in customer attrition rate
Increased revenues
 10% increase in Call
Centre Agent revenues
 25% increase in cross sell
and upsell opportunities
Higher Customer
Satisfaction
Rating improved from 1.5 to 2.7
on a five point scale
Powered by
 Real time Twitter feed and other
Social Media data
 IBM Big Match
13.04.2016
Business challenge: Customers continue to shift to mobile and social
channels in the way they converse about brands. Social channels are
often the first way to express grievances and doubts. In real time, negative
sentiments can quickly proliferate and influence existing and prospective
customers. How can we leverage these social channels to connect with
customers in a way that suits their preferences in order to build loyalty
and reduce attrition?
The socially aware solution: The Telco uses customer sentiment derived
from Twitter postings along with other social data and internal company
records to understand customer preferences and predict customers at risk
of attrition. Resource is being shifted from staffing call centres to social
media engagements in order to empower customers and respond to them
via their preferred communication medium.
A Health Insurance company uses cloud-based social media analytics to improve marketing effectiveness through one-to-one
engagement
25% reduction
in time to market for new products
Increased revenues
 10% increase in Seller revenues
 25% increase in cross sell and
upsell opportunities
20% reduction
in the cost of acquiring new customers
Powered by
 Real time Twitter feed and other
Social Media data
 IBM Big Match
 Life Event Detection
13.04.2016
Business challenge: Customer engagement is limited by what is captured
within the internal company data repositories. And yet customers are publicly
conversing about their products, services and market via social media. How
do we tap into this wealth of social insight in order to better engage with our
customers individually and increase market share?
The socially aware solution: The Health Insurance company now combines
enterprise customer data with insights from social conversations to enhance
how they engage with customers more individually, and feeds market
intelligence insights about the market, competitors and segment needs into
their key messaging and product development to increase market share.
Moderne Plattformen erlauben einen schnellen,
kostengünstigen, einfachen Einstieg
z.B. IBM BlueMix



Plattform as a Service
Benötigte Apps können schnell und kostengünstig per
drag+drop erstellt werden
Beinhaltet Watson-Angebote und mehr als 100 CloudServices
13.04.2016
Ohne Strategie geht es nicht



Was will ich erreichen?
Was will ich mit den gewonnenen Daten und
Informationen machen, wie will ich sie nutzen?
Bsp. Sentimentanalyse: will ich Positives verstärken oder
Negativem entgegenwirken?

Will ich individuell antworten, z.B. in Social Media?

Auf den richtigen Ton kommt es an
13.04.2016
Wo stehen die Firmen heute?
9%
21%
65%
5%
STEP 4:
Differentiating
STEP 3:
Competitive
STEP 2:
Foundational
STEP 1:
Ad-hoc
Manual, slow, error prone, cumbersome,
fragmented data quality concerns
13.04.2016
Automated, instant, accurate, seamless,
converged Data governance is in place
Sie „verdursten“ nicht mehr im Datenmeer
13.04.2016
START SMART
 SCALE FAST
13.04.2016
Haben Sie Fragen?
Stephan Multhaupt
Geschäftsführer
gmc² gerhards multhaupt consulting GmbH
Adenauerallee 136
D - 53113 Bonn
Main
Tel
Fax
Email
Website
+49 228 - 30 49 77 00
+49 228 - 30 49 77 10
+49 228 - 30 49 77 99
[email protected]
www.gmc2.de
13.04.2016

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