Cross-channel effect of informational websites

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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.

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Cross-channel effect of informational websites

  1. 1. Determining the Cross-Channel Effect of Informational Web Sites Enschede, November 2006 Marije L. Teerling
  2. 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. 3. Dissertation: Multi-channel effects in retailing STORE
  4. 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. 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
  6. 6. 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
  7. 7. 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?
  8. 8. 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
  9. 9. 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
  10. 10. 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?
  11. 11. 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
  12. 12. 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
  13. 13. 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
  14. 14. 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
  15. 15. 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
  16. 16. 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
  17. 17. 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
  18. 18. 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.
  19. 19. 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%
  20. 20. 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)
  21. 21. 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
  22. 22. 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’
  23. 23. 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
  24. 24. 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!
  25. 25. 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
  26. 26. Multichannel consumer behavior Online  offline: Offline  online:
  27. 27. 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?
  28. 28. 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)
  29. 29. 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)
  30. 30. Methodology VAR - model
  31. 31. 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
  32. 32. 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.
  33. 33. 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
  34. 34. 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!
  35. 35. 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

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