Does banner advertising affect browsing for brands? clickstream choice model says yes, for some

This paper investigates how exposure to Internet display advertising affects the subsequent choices users make of brand-specific pages to view within a website. Using individual-level clickstream data from a third-party automotive website, we tracked the web pages selected by users as they browsed the site and their exposures to premium placement display ads for different vehicle makes (e.g., Ford, Toyota). Pages on the site were classified into those that displayed information about a specific vehicle make (a “make page”) versus those that did not (a “non-make page”). For each “make-page” viewed, the specific automotive make selected (e.g., Ford, Toyota) was also recorded. We use these data to develop a model of users’ make-specific page choices as a function of prior banner ad exposure on the site. Consumer heterogeneity is captured using a Bayesian Mixture approach. We find that banner ads influence subsequent choices of which make-specific pages to view for ads, served during the current browsing session but not for ads served in previous sessions. The effect of banner ads is also segmented: users in one segment (54%) reacted positively, users in a second segment (46%) were not influenced. Using a standard continuous approach to heterogeneity, we would have concluded–incorrectly–that banner advertising has no effect on the subsequent selection of make-specific pages. For the positively reacting segment, we estimate that the elasticity of make-page choice with respect to banner ad exposure is just under 0.2. Users in this segment appear less focused in their site browsing behavior and tend to stay longer than users in the non-reacting segment.

[1]  Greg M. Allenby,et al.  The Effect of Media Advertising on Brand Consideration and Choice , 2011, Mark. Sci..

[2]  Peter E. Rossi,et al.  Bayesian analysis of the multinomial probit model , 2000 .

[3]  Rick L. Andrews,et al.  An Empirical Comparison of Logit Choice Models with Discrete versus Continuous Representations of Heterogeneity , 2002 .

[4]  M. Stephens.  Dealing with label switching in mixture models , 2000 .

[5]  Huberman,et al.  Strong regularities in world wide web surfing , 1998, Science.

[6]  Pradeep K. Chintagunta,et al.  The Effect of Banner Advertising on Internet Purchasing , 2006 .

[7]  Rachel Kennedy,et al.  The Total Long-Term Sales Effects of Advertising: Lessons from Single Source , 2009, Journal of Advertising Research.

[8]  R. Bucklin,et al.  Modeling Purchase Behavior at an E-Commerce Web Site: A Task-Completion Approach , 2004 .

[9]  John Geweke,et al.  Statistical inference in the multinomial multiperiod probit model , 1997 .

[10]  G. Celeux,et al.  Stochastic versions of the em algorithm: an experimental study in the mixture case , 1996 .

[11]  Greg M. Allenby,et al.  On the Heterogeneity of Demand , 1998 .

[12]  Sumit K. Majumdar,et al.  Wearout Effects of Different Advertising Themes: A Dynamic Bayesian Model of the Advertising-Sales Relationship , 2007 .

[13]  Prasad A. Naik,et al.  Planning Media Schedules in the Presence of Dynamic Advertising Quality , 1998 .

[14]  Peter E. Rossi,et al.  Do Switching Costs Make Markets Less Competitive? , 2008 .

[15]  Roberto S. Mariano,et al.  Simulation-based Inference in Econometrics , 2008 .

[16]  Claire Allison Stammerjohan,et al.  BANNER ADVERTISER-WEB SITE CONTEXT CONGRUITY AND COLOR EFFECTS ON ATTENTION AND ATTITUDES , 2005 .

[17]  William J. Havlena,et al.  Decay Effects in Online Advertising: Quantifying the Impact of Time Since Last Exposure on Branding Effectiveness , 2004, Journal of Advertising Research.

[18]  William D. Penny,et al.  A Dynamic Bayesian Model of Homeostatic Control , 2014, ICAIS.

[19]  Peter S. Fader,et al.  Dynamic Conversion Behavior at E-Commerce Sites , 2004, Manag. Sci..

[20]  Wendy W. Moe.  An Empirical Two-Stage Choice Model with Varying Decision Rules Applied to Internet Clickstream Data , 2006 .

[21]  Daniel A. Bell,et al.  The future of technology , 2001 .

[22]  Guy W. Mullarkey,et al.  Factors Affecting Online Advertising Recall: A Study of Students , 2003, Journal of Advertising Research.

[23]  Peter E. Rossi,et al.  NBER WORKING PAPER SERIES STATE DEPENDENCE AND ALTERNATIVE EXPLANATIONS FOR CONSUMER INERTIA , 2009 .

[24]  A J Griffin,et al.  The future of technology. , 1985, Dental lab management today.

[25]  Joel R. Rubinson,et al.  Empirical Evidence of TV Advertising Effectiveness , 2009, Journal of Advertising Research.

[26]  Dominique M. Hanssens,et al.  Market Response Models: Econometric and Time Series Analysis , 1989 .

[27]  Catarina Sismeiro,et al.  A Model of Web Site Browsing Behavior Estimated on Clickstream Data , 2003 .

[28]  P. Green,et al.  On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion) , 1997 .

[29]  P. Chatterjee,et al.  Modeling the Clickstream: Implications for Web-Based Advertising Efforts , 2003 .

[30]  Wendy W. Moe,et al.  The Influence of Goal‐Directed and Experiential Activities on Online Flow Experiences , 2003 .

[31]  Kannan Srinivasan,et al.  Modeling Online Browsing and Path Analysis Using Clickstream Data , 2004 .

[32]  C. Robert,et al.  Estimation of Finite Mixture Distributions Through Bayesian Sampling , 1994 .

[33]  Chang-Hoan Cho,et al.  WHY DO PEOPLE AVOID ADVERTISING ON THE INTERNET? , 2004 .

[34]  X. Drèze,et al.  Internet advertising: Is anybody watching? , 2003 .

[35]  Lisa R. Klein.  Evaluating the Potential of Interactive Media through a New Lens: Search versus Experience Goods , 1998 .

[36]  Avi Goldfarb.  Analyzing Website Choice Using Clickstream Data , 2002, The Economics of the Internet and E-commerce.

[37]  Peter S. Fader,et al.  On the Depth and Dynamics of Online Search Behavior , 2004, Manag. Sci..

[38]  Leonard M. Lodish,et al.  How T.V. Advertising Works: A Meta-Analysis of 389 Real World Split Cable T.V. Advertising Experiments , 1995 .

[39]  Wendy W. Moe.  A field experiment to assess the interruption effect of pop-up promotions , 2006 .

[40]  Gilles Celeux,et al.  Bayesian Inference for Mixture: The Label Switching Problem , 1998, COMPSTAT.