Shadowing of Grocery Shoppers and Appropriate Classification

Transcrição

AG DANK München, 22. November 2014
23.12.2014
Shadowing of Grocery Shoppers
and Appropriate
Classification schemes
Udo Wagner
University of Vienna
Herbsttagung der Arbeitsgruppe
Datenanalyse und nummerische Klassifikation
22. November 2014
Agenda
• Introduction
• Conceptual Considerations
• Design of Empirical Study
• Data Analysis
• Conclusions
Wagner, U., Ebster, C., Eske, U. und Weitzl, W. (2014):
„The Influence of Shopping Carts on Customer Behavior in Grocery Store“,
in: Marketing, ZFP – Journal of Research and Management , 36(3), S. 165-175.
Udo Wagner
Udo Wagner, Universität Wien
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23.12.2014
Introduction
• Cell phone tracking by US retailers to better compete with online retailers
(controversial issue)
• In-store observations are used for many decades
analyze shopper orientation (Gröppel-Klein, Bartmann, 2009)
identify predominant paths shoppers take inside the store (Larson, Bradlow, Fader, 2005)
compare shoppers’ level of exposure to in-store visuals (Sorensen, 2003,2009)
capture in-store interactions between parents and their children (Ebster, Wagner, Neumüller,
2009)
measure customer traffic in different parts of a store (Newman, Yu, Oulton, 2002)
analyze the effects of clockwise or anti-clockwise walking patterns in a store (Gröppel-Klein,
Bartmann, 2008)
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Devices used for recording instore movement
• GPS (cell phone tracking)
• RFID
• Automatic tracking of (specially equipped) carts (Path-Tracker®)
• Video observation
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• Larson, Bradlow and Fader (2004)
- Path-Tracker® = electronic observation / tracking system
- Typical shopper movements are recorded and analyzed
- Prototypical shopping paths
- „k-medoids clustering algorithm“:
Clustering of customers according to time spent in the store
• low (2 – 10 Minutes)
• medium (10 – 17 Minutes)
• high (more than 17 Minutes)
→
→
→
2 medoids
4 medoids
8 medoids
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Customers‘ interaction with shopping
carts
RQ1: What is the effect of parking shopping carts on the
relationship between in-store movement and
purchase behavior?
RQ2: Does store design moderate these relationships?
Grocery stores (utilitarian goods),
exploratory study,
unobtrusive (human) observation procedure
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Conceptual considerations
Shopping carts facilitate shopping
+ temporarily store products before purchasing
+ deposit personal belongings
+ allow to touch merchandise
Shopping carts might hinder shopping
- restricted movement in the store
- reduced walking speed
- reduced flexibility of walking direction
Shopping cart behavior might be moderated by
• time pressure of customers
• number of shoppers present in the store
• perceived crowding
• fear of pickpockets
• environmental characteristics of store
• time of the day, season
• familiarity with the store
Udo Wagner
Effects of crowding (many shoppers are
present) can operate in opposite directions:
+ individual space becomes limited 
parking carts helps regain mobility
leaving the cart unattended might prove
risky (especially when personal
belongings are stored in the cart)
parked carts of other shoppers might be
perceived negatively
(butt-brush effect)
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parking carts behavior
store
design
(a‘)
(b‘)
(c‘)
(c)
(b)
in-store movement behavior
purchase behavior
(a)
Simplified model:
1. Parking shopping carts is considered as a mediating construct that
influences the relationship between in-store movement and purchase
behavior
2. Store design is considered as a moderator
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store
design
(a‘)
(b‘)
(c‘)
(c)
(b)
purchase behavior
(a)
RQ1: What is the effect of parking shopping carts on the relationship
between in-store movement and purchase behavior?
(a) Patrons who spend more time in a store will purchase more often
(b) Patrons who spend more time in a store are more likely to park their
carts more often
(c) Patrons who park their carts more often will purchase more often
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store
design
(a‘)
(b‘)
(c‘)
(c)
(b)
purchase behavior
(a)
(a’) The relationship between the time required for shopping and the number of
purchases is less well established for a modern, conveniently designed, store
(i.e., shopping experience is more pleasant in a modern store)
(b’) The relationship between the time required for shopping and the frequency of
parking the carts is stronger for a modern, conveniently designed, store (i.e.,
perceived crowding is less established in a modern store, thus customers are
more willing to park their carts)
(c’) The relationship between the frequency of parking the carts and the number
of purchases is stronger for a modern, conveniently designed, store (i.e., a
modern design facilitates in-store navigation, customers feel more comfortable
and tend to make more unplanned purchases)
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Design of Empirical Study:
Disguised observation method
Two observers followed selected shoppers covertly and
recorded their shopping paths on a tablet computer with a
shadowing tool (plotting individual shopping paths on digitized
shopping floor plan in real time)
Demonstrationsvideo
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Floor plan of traditional store
•
•
•
•
•
two large supermarkets
belong to the same
grocery retail chain
carry about the same
assortment of products
have approximately the
same size of total sales
located in different parts
of the same city
Meat &
sausages
Check-out area
Long aisles, rectangular
arranged shelves
Frozen food & dairy
products
Fruits &
vegetables
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Floor plan of modern store
•
•
•
three weeks observation
period
five well trained
observers / interviewers
randomized selection of
(single) shoppers
Frozen food & dairy
products
Check-out area
landmarks, spacious aisles,
shelves arranged in freeflowing patterns, lower
heights of the shelves
Meat &
sausages
Fruits &
vegetables
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Design of Empirical Study
Collected data
197 shoppers in the traditional store
209 in the modern store
By means of observation:
• in-store shopping path data (with and without carts)
• purchases (with and without carts)
• frequency, places and duration of parked carts
• walking speed
• shopping duration
• calendar date, time of day
• handbags carried in the cart
• gender, age
By means of communication (only for some of persons of the sample):
• weekly shopping behavior,
• perceived store atmospherics
• functionality of cart
(only 7% indicated that they noticed someone was following them through the store)
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Data analysis: descriptives
Open checkout cashiers
Type of store
Women
<40
40‐60
>60
range
mode
traditional
65%
34%
42%
24%
3‐9
5
modern
66%
39%
39%
22%
2‐10
4
Shops are fairly similar in terms of customer gender and age,
average time spent in the store, using carts for carrying personal
belonging (66%)
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Data analysis: descriptives
Parking shopping carts
n
Average number of purchases
Time spent shopping
without
at least once
on average
with
total
shopping cart
traditional
197
19 min
54%
1.64
8.51
1.13
7.38
modern
209
21 min
74%
2.82
10.72
2.39
8.33
z, t‐Test
‐2.07
‐4.19
‐4.21
‐4.04
‐4.18
‐2.01
p‐level
.04
<.01
<.01
<.01
<.01
.04
Different variance of time spent in the store,
different patterns of parking carts,
different number of products purchases (in total and with/without cart)
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Data analysis: measurement
store
design
(a‘)
(b‘)
(c‘)
(c)
(b)
purchase behavior
(a)
STt: in-store movement behavior
surplus of time customer spent in the store (given personal demographics
and demand)
CPt: cart parking behavior
frequency of parking carts during a shopping trip
PPt: purchase behavior
number of products purchased
DEt: store design
binary (traditional / modern)
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Data analysis: research questions
DEt
store
design
CPt
(a‘)
(b‘)
(c‘)
(c)
(b)
STt
purchase behavior
(a)
CPt  α1 
PPt
 γ1  STt  error1t
PPt  2  2  CPt   2  STt  error2t
with:
1,2 , 2 ,1, 2 parameters
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CPt  α1 
PPt   2   2  CPt   2  STt  error2t
with :
1 ,  2 ,  2 ,  1 ,  2
Traditional store
Modern store
n=197
n=209
parameters
Equation 1: Surplus of time ( ˆ1 )  Frequency of parking carts *
R2=.11
ˆ1
p
ˆ1
p
.33
<.01
.40
<.01
F=24.17
<.01
F=38.86
<.01
R2=.16
Equation 2: ˆ2 ), frequency of parking carts ( ) 
̂ 2
Surplus of time ( Number of purchases *
R2=.45
*
ˆ2
p
̂ 2
p
.59
<.01
.17
<.01
F=79.29
<.01
R2=.44
ˆ2
p
̂ 2
p
.49
<.01
.30
<.01
F=81.41
<.01
Only standardized regression coefficients are reported.
All parameter estimates are plausible and statistically significant,
fit of the model is satisfactory
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CPt  α1 
with :
1 ,  2 ,  2 ,  1 ,  2
parameters
RQ1: What is the effect of parking shopping carts on the relationship between
in-store movement and purchase behavior?
Mediation test on
(Sobel 1982; Iacobucci 2012):
traditional
modern
zMediation
 2.64 p  .01 zMediation
 3.99 p  .01
 Frequency of parking shopping carts partially mediates the relationship
between surplus of time and number of purchases
√
(a’) The relationship between the time required for shopping and the number of purchases is
less well established for a modern, conveniently designed, store
√
(b’) The relationship between the time required for shopping and the frequency of parking the
carts is stronger for a modern, conveniently designed, store
√
(c’) The relationship between the frequency of parking the carts and the number of purchases
is stronger for a modern, conveniently designed, store
√
By utilizing the dummy variable DEt we estimate our model for both types of stores
simultaneously and find a significant moderator effect in all three cases
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Data analysis: effect of personal
belongings carried in the cart – RQ1
Traditional store
Only customers without belongings kept in carts are considered
n=70
R2=.02
n=69
p
ˆ1
p
.14
.24
.59
<.01
F=1.41
.24
F=35.84
<.01
R2=.35
R2=.45
2
p
̂ 2
p
.64
<.01
.12
.19
F=27.32
<.01
R2=.44
ˆ2
p
̂ 2
p
.51
<.01
.22
.06
F=26.01
<.01
Mediation test, with
Only customers with belongings kept in carts are considered
n=127
n=140
Equation 1: Surplus of time ( ˆ )  Frequency of parking carts *
ˆ1
R2=.15
1
ˆ1
p
.39
<.01
F=21.87
<.01
R2=.10
p
.32
<.01
F=15.55
<.01
Equation 2: Surplus of time ( ˆ ), frequency of parking carts ( ) 
̂ Number of purchases *
R2=.45
*
2
ˆ2
p
.56
<.01
F=50.06
<.01
parameters
Mediation test, without
Equation 2: Surplus of time (ˆ2), frequency of parking carts ( ) 
̂ Number of purchases *
ˆ2
with :
All parameter estimates are
plausible and most of them
statistically significant,
fit of the model is satisfactory
(with but one exception)
Equation 1: ˆ1)  Frequency of parking carts *
Surplus of time ( ˆ1
1 ,  2 ,  2 ,  1 ,  2
Modern store
CPt  α1 
̂ 2
.21
2
p
ˆ2
p
̂ 2
p
<.01
.50
<.01
.32
<.01
F=55.20
<.01
R2=.45
 Frequency of parking shopping
carts partially mediates the
relationship between surplus of
time and number of purchases
even when potential influence of
belongings kept in carts is
considered
√
Only standardized regression coefficients are reported.
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Modern store
Traditional store
without
with
Surplus of time ( ) 
Frequency of parking carts
without
with
Surplus of time ( ) 
Number of purchases
Udo Wagner
without
with
Frequency of parking carts ( ) 
Number of purchases
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CPt  α1 
√
with :
1 ,  2 ,  2 ,  1 ,  2
(a’)
parameters
is dominant and larger for
the traditional store
for the modern store, no
influence of belongings kept in
cart
(b’)
for both conditions
(c’)
for “without” condition
Interaction effects between store
design and belongings kept in cart:
behavior appears more similar in
the two stores if belongings are in
cart (due to fear of pickpockets?)
(a’) The relationship between the time required for shopping and the number of purchases is
less well established for a modern, conveniently designed, store
(b’) The relationship between the time required for shopping and the frequency of parking the
carts is stronger for a modern, conveniently designed, store
(c’) The relationship between the frequency of parking the carts and the number of purchases
is stronger for a modern, conveniently designed, store
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Data analysis: Typical patterns of
shopping paths – traditional store
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shopping paths – modern store
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shopping paths, both stores
Time spent in the store
Patterns observed
<5 minutes
goal‐oriented movement; targeted areas visited
5–10 minutes
movement along main aisles; targeted areas visited
10–25 minutes
movement along main aisles with short side trips to secondary aisles
25–45 minutes
movement along main aisles, some secondary aisles are walked through completely, center of the store is visited
>45 minutes
strolling through the whole sales area, longer visits in the center of the store
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Data analysis: typical shopping paths
Modern store
Traditional store
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Data analysis: typical shopping paths
many
few
Modern store
Traditional store
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Data analysis: walking speed
Traditional store
Modern store
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Data analysis: frequently used typical
shopping paths without carts
Modern store
Traditional store
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Data analysis: locations for parking carts
Traditional store
Modern store
31
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Data analysis: duration for parking carts
Traditional store
Modern store
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Data analysis: total number of purchases
Traditional store
Modern store
33
Udo Wagner
Data analysis: number of purchases
without cart
Traditional store
Modern store
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Data analysis: findings on individual
sales departments (rather similar across stores)
Sales department
Frequency of patrons’ visits
Frequency of parked Duration of parking carts
carts
Frequency of purchases
Time spent by patrons
Patrons’ walking speed
Fruits & vegetables
high
high
Frozen food & dairy products
long
many
long
slow
high
high
short
many
long
slow
Meat & sausages
high
high
long
many
medium
moderate
Vinery
low
low
short
few
long
slow
Pet food
low
low
short
few
long
slow
New product offers
low
low
short
few
long
slow
Kitchenware
low
low *
short
few
long
slow
Cosmetics
low
low
short
few
long
slow
Textiles
low
low
short
few
long
slow
Checkout area
high
low
short
few
short
fast
* Typically, customers walk around in this area without their shopping cart.
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Data analysis: general findings
• Patterns do not depend substantially on type of store
• High frequency of fruits & vegetables area (decompression zone) an frozen
food & dairy products area
• Shoppers move along aisles located near the walls of the store (race tracks)
• Shoppers walk anti-clockwise
• Many zones are frequently skipped (especially in the traditional store)
• Areas in the middle commonly remain untouched (however, higher profit
margins product are offered there)
• Selection of aisles frequently visited depends on the total time spent in the store
• Duration of parked carts is rather short (distribution is highly skewed) with
exceptions (meat & sausages, fruits & vegetables)
• Customers park their carts in spacious regions and stay in their proximity
• Walking speed becomes faster along main aisles and near the checkout, slower
in zones with a low frequency of visits
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Conclusions
• Considerable heterogeneity across customers with respect to use of carts
• Frequency of carts parked partially mediates the relationship between time spent
in the store and the number of purchases
• Design of store has a moderating effect
Managerial implications
• Provide in-door space for temporary parking of carts
• Design of the store impacts cart parking behavior, parking carts might induce
spontaneous purchases  provide incentives for parking carts in particular in the
center of the store
• Retail strategies should be adapted account for differences within various sales
departments
Theoretical implications
• Tracking shopping carts only might lead to considerable measurement problems
• Proposed measurement tool requires further testing
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Limitations and further research
•
•
•
•
•
•
Results are probably not generalizable
No causal relationships have been established
Familiarity with the store has not been adequately considered
Preparing for shopping (e.g., by means of a shopping list) has not been considered
Reasons for parking carts have not been identified
Crowding effects have only been roughly considered
•
•
•
•
Investigate other types of supermarkets, shopping aids (e.g., baskets)
Investigate effects of cash deposits for using a cart
Investigate size of shopping carts
….
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23.12.2014
Quellen
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Berekoven, Ludwig (1995). Erfolgreiches Einzelhandelsmarketing: Grundlagen und Entscheidungshilfen.
C. H Beck’sche Verlagsbuchhandlung, München, 2.überarbeitete Auflage.
Berghaus, Nadine (2005). Eye-Tracking im stationären Einzelhandel: Eine empirische Anaylse der
Wahrnehmung von Kunden am Point of Purchase. JOSEF EUL VERLAG GmbH, Köln, 1. Auflage
Chandon, Pierre; Wesley J. Hutchinson and Scott H. Young. Unseen is Unsold: Assessing Visual Equity
with Commercial Eye-Tracking Data. Marketing Science (2002), Perception Research Services, Inc., pp.
1 – 54.
Hui, Sam K., Eric T. Bradlow and Peter S. Fader. Testing Behavioral Hypotheses Using an Integrated
Model of Grocery Store Shopping Path and Purchase Behavior. Journal
Kourennyi, Dimtri D. (2011). Customer Tracking through security camera video processing. Case
Western Reserve University, Department of Electrical Engineering and Computer Service, pp. 1 – 32.
Larson, Jeffrey S.; Eric T. Bradlow and Peter S. Fader. A Exploratory Look at Supermarket Shopping
Paths. International Journal of Research in Marketing (2004), Vol 22 (4), pp. 395 – 414.
Newman, Andrew J.; Daniel K. C. Yu and David P. Oulton. New insights into retail space and format
planning from customer-tracking data. Journal of Retailing and Consumer Services (2002), Vol. 9, pp.
253 – 258.
Sorensen, Herb. The Science of Shopping. Marketing Research (2003), pp. 31 – 35.
Sorensen, Herb. (2009). Inside the mind of the shopper: The Science of Retailing. Pearson Education,
Inc., New Jersey.
Underhill, Paco. (2009). Why we buy: The Science of Shopping. Simon & Schuster, Inc., New York.
Braaß, Christa. Total Store Research: Mit Kundenlaufstudien POS-Konzepte optimieren http://www.gdpgroup.com/de/nl_200802.php
Hillenbrand, Tom. Smart-Chip-Debatte: „Selbstzerstörung inklusive“. Der Spiegel (03.09.2003)
http://www.spiegel.de/wirtschaft/0,1518,262763,00.html
P.O.S. kompakt 4.2008. Kundenlauf und Kundenverhalten: Wissen, wo’s langgeht. http://www.poskompakt.net/media/Ausgabe/heft042008/Shoppermetrics.pdf
Winter, Stefanie (2000). Shadowing http://imihome.imi.uni-karlsruhe.de/nshadowing_b.html
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Shadowing of Grocery Shoppers and Appropriate Classification

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