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Website Morphing

  • John R. Hauser


    (MIT Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142)

  • Glen L. Urban


    (MIT Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142)

  • Guilherme Liberali


    (MIT Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, and Universidade do Vale do Rio dos Sinos, Sao Leopoldo, RS 90450 Brazil)

  • Michael Braun


    (MIT Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142)

Virtual advisors often increase sales for those customers who find such online advice to be convenient and helpful. However, other customers take a more active role in their purchase decisions and prefer more detailed data. In general, we expect that websites are more preferred and increase sales if their characteristics (e.g., more detailed data) match customers' cognitive styles (e.g., more analytic). “Morphing” involves automatically matching the basic “look and feel” of a website, not just the content, to cognitive styles. We infer cognitive styles from clickstream data with Bayesian updating. We then balance exploration (learning how morphing affects purchase probabilities) with exploitation (maximizing short-term sales) by solving a dynamic program (partially observable Markov decision process). The solution is made feasible in real time with expected Gittins indices. We apply the Bayesian updating and dynamic programming to an experimental BT Group (formerly British Telecom) website using data from 835 priming respondents. If we had perfect information on cognitive styles, the optimal “morph” assignments would increase purchase intentions by 21%. When cognitive styles are partially observable, dynamic programming does almost as well—purchase intentions can increase by almost 20%. If implemented system-wide, such increases represent approximately $80 million in additional revenue.

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Article provided by INFORMS in its journal Marketing Science.

Volume (Year): 28 (2009)
Issue (Month): 2 (03-04)
Pages: 202-223

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Handle: RePEc:inm:ormksc:v:28:y:2009:i:2:p:202-223
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  1. Oded Netzer & James M. Lattin & V. Srinivasan, 2008. "A Hidden Markov Model of Customer Relationship Dynamics," Marketing Science, INFORMS, vol. 27(2), pages 185-204, 03-04.
  2. Shane Frederick, 2005. "Cognitive Reflection and Decision Making," Journal of Economic Perspectives, American Economic Association, vol. 19(4), pages 25-42, Fall.
  3. Manohar U. Kalwani & Alvin J. Silk, 1982. "On the Reliability and Predictive Validity of Purchase Intention Measures," Marketing Science, INFORMS, vol. 1(3), pages 243-286.
  4. Christopher W. Allinson, 1996. "The Cognitive Style Index: A Measure of Intuition-Analysis For Organizational Research," Journal of Management Studies, Wiley Blackwell, vol. 33(1), pages 119-135, 01.
  5. Steenkamp, Jan-Benedict E M & Baumgartner, Hans, 1998. " Assessing Measurement Invariance in Cross-National Consumer Research," Journal of Consumer Research, Oxford University Press, vol. 25(1), pages 78-90, June.
  6. John Liechty & Rik Pieters & Michel Wedel, 2003. "Global and local covert visual attention: Evidence from a bayesian hidden markov model," Psychometrika, Springer;The Psychometric Society, vol. 68(4), pages 519-541, December.
  7. Alan L. Montgomery & Shibo Li & Kannan Srinivasan & John C. Liechty, 2004. "Modeling Online Browsing and Path Analysis Using Clickstream Data," Marketing Science, INFORMS, vol. 23(4), pages 579-595, November.
  8. John R. Hauser & Olivier Toubia, 2005. "The Impact of Utility Balance and Endogeneity in Conjoint Analysis," Marketing Science, INFORMS, vol. 24(3), pages 498-507, August.
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