In my dissertation research I focused on determining the effectiveness of informational web sites on individual customer buying behavior. I show that this effectiveness, given the multichannel environment, is not necessarily positive and that customers have a lot to benefit from the information offered online.

1. Determining the
Cross-Channel Effect of
Informational Web Sites
Enschede, November 2006
Marije L. Teerling

2. Research interests (so far)
• Effects of the multi-channel environment, informational web
sites
– Dissertation
– Overview paper in JSR
• Effects of web site customization & personalization
– Theoretical paper
– How does web site customization effect customer
behavior
• The impact of social networks on customer behavior
– How do our networks influence our shopping behavior
• Dashboard marketing
– Measurement and use of key marketing metrics

3. Dissertation:
Multi-channel effects
in retailing
STORE

4. Defining multichannel consumer
behavior
– Channel = customer contact point or medium through which
the firm and the customer interact (Neslin et al., 2006).
– Multichannel customer behavior = customers’ choice and use
of multiple channels during the decision-making process.
– Multichannelling = firms reaching customers using a mix of
channel formats, (e.g., stores, web sites, direct mail and
kiosks) (Montoya-Weiss et al., 2003).
– Cross-channel effects = the effects of a change in behavior in
channel a on behavior in channel b.

5. Multichannel consumer behavior
Some effects on customer behavior
– Marketing efforts can migrate customers (Ansari et al.,
2006)
– Customer become multichannelers after the addition of
the Internet channel (Dholakia et al., 2005)
– Transactional Internet channel may increase or decrease
customer buying behavior (Kushwaha and Shankar, 2005;
Gensler et al., 2006)
Only 0.4% of 1300 studies published over a period of 7 years look
at informational web sites

7. Why informational web sites
• Informational sites educate or inform the consumer about
products or services offered by the company (e.g. Morton et
al., 2001).
• 70% all web sites only contain information (Carroll, 2002).
• Web influenced 20% of in-store sales (Forrester Research,
2005).
• Web-to-store shoppers spend 70% more (Dieringer research
group, 2004).
• Consumers prefer offline buying given
– Type of product
– Situational influences
– Consumer anxieties

8. Research Questions
Effects from an informational web site
Study 1: Complementary attitudes
• How do online attitudes influence offline attitudes and
behavior?
• What are the effects of moderators on these relationships?
Study 2: The effect on individual offline behavior
• What are the effects on the number of offline shopping trips?
• What are the effects on the amount spent in different product
categories?
Study 3: Marketing dynamics and feedback loops
• What sequential cross-channel effects take place?
• What are the cross-channel effects of marketing efforts?

9. Expected effects of
an informational web site
Decreases offline behavior Increases offline behavior
– Improved efficiency – Synergy effects between
– Bargaining power the channels
– Reduced impulse buying –Better choices
– Reduced switching costs –Sense of smart shopper
–Little human contact – Marketing effects
– Web usage decreases –More exposure
loyalty – Improved brand/product
awareness
– Increased loyalty
–Enhanced service

10. Study 2:
The Impact of an
Informational Website on
Offline Consumer Behavior
With:
Erjen van Nierop
Peter Leeflang
Eelko Huizingh
University of Groningen
The Netherlands

11. Objective
To determine the effect of using an informational web site on the
offline behavior of individual consumers.
Research questions:
• Web site use change overall offline store purchases?
decomposition
• Web site use  change offline shopping trips
• Web site use change product categories purchases?

12. Empirical setting
• Large Dutch retail organization
– 58 offline department stores
– Main departments: female & male fashion, accessories,
children’s products, interior design, sports equipment and
apparel.
• Informational web site – introduced in March 2001
– Pages related to the main departments of the store
– Products and information related to the departments
– Customized features such as address book, e-card, gift
planner

13. Empirical setting
• Data
Individual customer level (n=8,615)
– Offline purchases for over 2.5 years <January 2000 to
May 2002>
• 10 months before web site use introduction
• 21 months after web site use introduction
– Six product categories
– Online behavior for well more than a year <March 2001 to
May 2002>
– Daily data aggregated to monthly level
– Survey held in May 2001 and May 2002
– Background variables via web site and Acxiom
– Linkage through popular national joint loyalty program id –
all data is collected through the id number

14. Methodology Total
number of
Total amount of shopping
money spent by trips of Multivariate
individual i during individual i type II Tobit
month t during model
month t
M itc
M it Vit *
c Vit Total amount of
Poisson
model money spent by
individual i per
trip in category c
during month t

15. Methodology
Number of shopping trips ( Vit ): Poisson
v it
e it
it
Pr Vit v it
v it !
where:
ln it i X it it

16. Methodology
Average amount per category: Tobit-2
Stage 1 Stage 2
* *
Z ict = decision to buy Yict = amount spent per category
* *
Zitc 1 if Zitc 0 Yitc if Zitc 1
* Yitc
Zitc 0 if Zitc 0 0 otherwise
where: where:
* *
Zitc c ic Hitc itc lnYitc c ic Gitc itc

17. Explanatory variables
• Online behavior
Web visits, number of online pages
• Promotional activities
Promotional activity – 3 types
• Spatial
Distance to closest store
• Socio demographics
% hh generally loyal, catalog buyers, life stage,
household size, gender
• Past behavior

18. Comparison of site visitors and non-site
visitors over time for shopping trips
3.5
3
2.5
Store visits
2 Site users
1.5 Non-site users
1
0.5
0
10
13
16
19
22
25
28
1
4
7
Periods

19. Results shopping trips
Estimated Std. Error Partial Partial
Parameter Effects Effects
2001 2002
Intercepta 0.625 0.057 -- --
Holiday promotion 0.192 0.044 0.383 0.341
General promotion 0.133 0.041 0.265 0.236
Fashion promotion 0.075 0.043 0.150 0.133
Dummy 2001 -0.134 0.035 -- --
Dummy 2002 -0.250 0.046 -- --
Site visitsa -0.456 0.070 -0.909 -0.809
Distance to closest outlet -0.013 0.005 -0.026 -0.023
Lagged shopping trips 0.072 0.009 0.144 0.128
Variance intercept 0.093 0.015 -- --
Variance site visits 0.508 0.074 0.570 0.508
a Bold parameters are significant at 95% confidence.
b Indicates the variable is estimated at the individual level.

20. Results amount spent per category
Children's
Ladies Fashion Men's Fashion Products
Yes/no Money Yes/no Money Yes/no Money
Category-specific effects
Intercept -0.379 0.469 -1.087 -0.612 -0.840 -0.178
Web visits -0.428 -0.901 -0.208 -0.429 -0.350 -0.851
Pooled effects
Year dummy 2001 -0.008 0.040
Year dummy 2002 -0.075 -0.016
Distance to closest store -0.007 -0.014
Last purchase occasion spending 0.001 -0.004
Web site pages -0.004 -0.017
Holiday promotions 0.059 0.165
Fashion promotions 0.039 0.144
General promotions 0.215 0.517
Yes/No hitrate 71% 85% 82%

21. Specific Findings
Store trips Money spent per category
Negative effects: Negative effects:
• Web visits • Web visits
• Distance to closest store • Distance to closest store
Positive effects: Positive effects:
• Promotions • Promotions
–Holiday –General (both stages)
–General –Holiday & fashion (stage
• Past behavior 2)

22. Post-hoc comparison
% customers with positive effects of
web site visits
21% of customers make more trips due to visiting the web site
Estimation Average: 5
Sample Samples
Yes/no Money Yes/no Money
Ladies 0 14 0 12
Men’s 9 16 2 8
Children 1 11 0 10
Accessories 0 10 0 10
Living 0 9 1 11
Sports 1 11 0 7
Average 2 12 0 10

23. Main findings
• For most customers, using the informational web site
decreases offline buying behavior
• For the minority of customers, using the informational site
increases offline buying behavior
• These customer:
– live closer to the store
– buy more items
– spent more money
– visit the store more often
– view more web pages
• Hence, seem to be the company’s ‘top customers’

24. Possible causes
• Efficient decision-making processes
– Use of an informational web site in a goal-directed
manner  searching for the best alternative
• Reduced impulse buying
– Informational web sites do not allow transactions 
increased processing resources  decisions based on
cognition instead of impulse
• Reduced switching costs
– Competition click away, less psychological bonds 
informational web site has disadvantage of not being able
to instantaneously buy the product

25. In conclusion
• The majority of customers spent less money!
– More online visits = less offline buying
• The company’s ‘top’ customers increase their spending
Limitations/future research
• The bottom line
• Competitors’ effects
• Other marketing instruments
• Empirical case study
Implementation of an informational web site should be
considered with great care!

26. Study 3:
Web-to-Store Shopping:
The Marketing Dynamics and Feedback
Loops between Online Information and
Offline Buying
With
Koen Pauwels, Dartmouth College
Peter Leeflang, University of Groningen
Eelko Huizingh, University of Groningen

27. Multichannel consumer behavior
Online  offline:
Offline  online:

28. Objective
• To gain insight into multichannel consumer behavior in an
informational web site / department store setting.
• Research questions:
– How do the components of online search and offline buying
behavior influence each other?
– How do marketing efforts influence both offline buying and online
search behavior?
– How do consumer characteristics influence multichannel
consumer behavior?

29. Decomposing offline purchase behavior
Amount per Number of
article shopping trips
purchased in per customer in
period t period t
(money) (trips)
M t Pt Trt
offline behaviort * * * Ct
Pt Trt Ct
Number of Total number of
products customers in
purchased per period t
visit in period t (customers)
(products)

30. Decomposing online search behavior
Amount of time Number of
per page in online visits per
period t visitor in period
(time) t
(visits)
Tit Pa t Vst
online behaviort * * *Vrs t
Pat Vst Vrs t
Number of Total number of
pages viewed online visitors in
per visit in period t
period t (visitors)
(pages)

31. Methodology
VAR - model

32. Modeling approach
• Vector Autoregressive Model – reduced form
K
Yt iYt i Xt t
i 1
• Reasons for using VAR
All possible relationships included
Effect past behavior on current
What if scenario’s
• Immediate versus cumulative effects

33. Findings
Aggregate level
• Small positive (long term) effect of online search on offline
buying
• Small negatieve (long term) effect of offline buying on online
search
Effects consumer characteristics
• Online search has a positive (long term) effect on offline
buying in case of sensory products, low online flow
experience and low frequency of web site visits
• Offline buying has a positive (long term) effect on online
search in case of low online flow and low frequency of web
site visits.

34. Simulation introduction web site
IRF site introduction
Immediate effect
0.500
0.400
0.300
Permanent effect
effect on money
0.200
0.100
0.000
-0.100
0
2
4
6
8
10
12
14
17
19
21
23
25
-0.200
-0.300 Post-intro dip
week

35. Some main conclusions
(total dissertation)
• Customers appreciate access to and use of multiple
channels
– Positive effects on an attitudinal level
• Majority of customers shop less often due to using the
informational web site
– Customers become more rational, improve their decision-
making processes
• Individual heterogeneity matters!
• Top customers benefit!
• Channel integration matters
• Effects on the short term vary from the long term effects!

36. Contribution
Providing insights into:
• The effects of using informational web sites on customer
behavior
• The customer’s sequential process of search and behavior
• Specific effects of the introduction of an informational web
site for different customer segments and product categories
• Customer free-riding behavior
• A methodology that can be used to measure these effects