Inside the Customer: Modeling Cognition during Online Shopping

Online marketers want to present potential customers with the right information at the right time. Decisions about what information to present are typically made before the customer has visited a web site, using data such as purchase histories and logs of web pages visited (i.e., clickstream data). An alternative approach is to develop predictions about what information to present based on inferences made from cognitive models of the customer. This research presents one approach to collecting and analyzing data that could be used to construct such models. Two studies are presented on how differences in product type may impact customer cognition and browsing behavior. The results suggest that differences in product type may lead to differences in waiting time before making a purchase. Product type may also influence the types of information people consult before making a purchase.

[1]  P. G. Quester,et al.  Product Involvement in Consumer Wine Purchases: Its Demographic Determinants and Influence on Choice Attributes , 1996 .

[2]  James F. Allen.  Maintaining knowledge about temporal intervals , 1983, CACM.

[3]  Peter A. Todd,et al.  Consumer Reactions to Electronic Shopping on the World Wide Web , 1996, Int. J. Electron. Commer..

[4]  Charles Møller,et al.  Proceedings of the Tenth Americas Conference on Information Systems , 2004 .

[5]  Philip S. Yu,et al.  Efficient Data Mining for Path Traversal Patterns , 1998, IEEE Trans. Knowl. Data Eng..

[6]  G. Laurent,et al.  Measuring Consumer Involvement Profiles , 1985 .

[7]  Abraham Silberschatz,et al.  What Makes Patterns Interesting in Knowledge Discovery Systems , 1996, IEEE Trans. Knowl. Data Eng..

[8]  P. Kotler,et al.  Marketing management : analysis, planning, and control , 1973 .

[9]  T. Tyebjee.  Response Time, Conflict, and Involvement in Brand Choice , 1979 .

[10]  U. Dholakia.  A motivational process model of product involvement and consumer risk perception , 2001 .

[11]  Chuan-Hoo Tan.  Comparison-Shopping Websites: An Empirical Investigation on the Influence of Decision Aids and Information Load on Consumer Decision-Making Behavior , 2003, ICIS.

[12]  Susan Wiedenbeck,et al.  PATTERNS OF INFORMATION SEEKING ON THE WEB: A QUALITATIVE STUDY OF DOMAIN EXPERTISE AND WEB EXPERTISE , 2003 .

[13]  Jaideep Srivastava,et al.  Data Preparation for Mining World Wide Web Browsing Patterns , 1999, Knowledge and Information Systems.

[14]  Robert W. Reeder,et al.  Information scent as a driver of Web behavior graphs: results of a protocol analysis method for Web usability , 2001, CHI.

[15]  P. Kotler.  Marketing Management: Analysis, Planning, Implementation and Control , 1972 .

[16]  Peter H. Bloch,et al.  Correlates of Price Acceptability , 1988 .

[17]  G. A. Miller.  THE PSYCHOLOGICAL REVIEW THE MAGICAL NUMBER SEVEN, PLUS OR MINUS TWO: SOME LIMITS ON OUR CAPACITY FOR PROCESSING INFORMATION 1 , 1956 .

[18]  J. Zaichkowsky.  Measuring the Involvement Construct , 1985 .

[19]  Per E. Pedersen,et al.  Search mode and purchase intention in online shopping behaviour , 2005 .

[20]  Ajay K. Kohli,et al.  HOW DO THEY KNOW THEIR CUSTOMERS SO WELL , 2001 .

[21]  Oded Lowengart,et al.  Differential Effects of Product Category on Shoppers' Selection of Web-based Stores: A Probabilistic Modeling Approach , 2001, J. Electron. Commer. Res..

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

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

[24]  Kevin Lane Keller.  Conceptualizing, Measuring, and Managing Customer-Based Brand Equity , 1993 .

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