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Offering Online Recommendations with Minimum Customer Input Through Conjoint-Based Decision Aids

Author

Listed:
  • Arnaud De Bruyn

    (Department of Marketing, ESSEC Business School, 95000 Cergy, France)

  • John C. Liechty

    (Smeal College of Business, The Pennsylvania State University, University Park, Pennsylvania 16802)

  • Eelko K. R. E. Huizingh

    (Department of Business Development, University of Groningen, 9700 AV Groningen, The Netherlands)

  • Gary L. Lilien

    (Smeal College of Business, The Pennsylvania State University, University Park, Pennsylvania 16802)

Abstract

In their purchase decisions, online customers seek to improve decision quality while limiting search efforts. In practice, many merchants have understood the importance of helping customers in the decision-making process and provide online decision aids to their visitors. In this paper, we show how preference models which are common in conjoint analysis can be leveraged to design a questionnaire-based decision aid that elicits customers' preferences based on simple demographics, product usage, and self-reported preference questions. Such a system can offer relevant recommendations quickly and with minimal customer input. We compare three algorithms—cluster classification, Bayesian treed regression, and stepwise componential regression—to develop an optimal sequence of questions and predict online visitors' preferences. In an empirical study, stepwise componential regression, relying on many fewer and easier-to-answer questions, achieved predictive accuracy equivalent to a traditional conjoint approach.

Suggested Citation

  • Arnaud De Bruyn & John C. Liechty & Eelko K. R. E. Huizingh & Gary L. Lilien, 2008. "Offering Online Recommendations with Minimum Customer Input Through Conjoint-Based Decision Aids," Marketing Science, INFORMS, vol. 27(3), pages 443-460, 05-06.
    • Handle: RePEc:inm:ormksc:v:27:y:2008:i:3:p:443-460
    DOI: 10.1287/mksc.1070.0306
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    References listed on IDEAS

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    1. Kohsuke Ogawa, 1987. "An Approach to Simultaneous Estimation and Segmentation in Conjoint Analysis," Marketing Science, INFORMS, vol. 6(1), pages 66-81.
    2. Olivier Toubia & Duncan I. Simester & John R. Hauser & Ely Dahan, 2003. "Fast Polyhedral Adaptive Conjoint Estimation," Marketing Science, INFORMS, vol. 22(3), pages 273-303.
    3. John G. Lynch , Jr. & Dan Ariely, 2000. "Wine Online: Search Costs Affect Competition on Price, Quality, and Distribution," Marketing Science, INFORMS, vol. 19(1), pages 83-103, April.
    4. Chuan He & Yuxin Chen, 2006. "Research Note—Managing e-Marketplace: A Strategic Analysis of Nonprice Advertising," Marketing Science, INFORMS, vol. 25(2), pages 175-187, 03-04.
    5. Stephen J. Hoch & David A. Schkade, 1996. "A Psychological Approach to Decision Support Systems," Management Science, INFORMS, vol. 42(1), pages 51-64, January.
    6. Shugan, Steven M, 1980. "The Cost of Thinking," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 7(2), pages 99-111, Se.
    7. Wayne DeSarbo & Richard Oliver & Arvind Rangaswamy, 1989. "A simulated annealing methodology for clusterwise linear regression," Psychometrika, Springer;The Psychometric Society, vol. 54(4), pages 707-736, September.
    8. Diehl, Kristin & Kornish, Laura J & Lynch, John G, Jr, 2003. "Smart Agents: When Lower Search Costs for Quality Information Increase Price Sensitivity," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 30(1), pages 56-71, June.
    9. Gerald Häubl & Valerie Trifts, 2000. "Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids," Marketing Science, INFORMS, vol. 19(1), pages 4-21, May.
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    Cited by:

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    2. James Agarwal & Wayne DeSarbo & Naresh K. Malhotra & Vithala Rao, 2015. "An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 2(1), pages 19-40, March.
    3. Eleanor McDonnell Feit & Mark A. Beltramo & Fred M. Feinberg, 2010. "Reality Check: Combining Choice Experiments with Market Data to Estimate the Importance of Product Attributes," Management Science, INFORMS, vol. 56(5), pages 785-800, May.
    4. Murray, Kyle B. & Häubl, Gerald, 2009. "Personalization without Interrogation: Towards more Effective Interactions between Consumers and Feature-Based Recommendation Agents," Journal of Interactive Marketing, Elsevier, vol. 23(2), pages 138-146.
    5. Jella Pfeiffer & Thies Pfeiffer & Martin Meißner & Elisa Weiß, 2020. "Eye-Tracking-Based Classification of Information Search Behavior Using Machine Learning: Evidence from Experiments in Physical Shops and Virtual Reality Shopping Environments," Information Systems Research, INFORMS, vol. 31(3), pages 675-691, September.
    6. Oded Netzer & Olivier Toubia & Eric Bradlow & Ely Dahan & Theodoros Evgeniou & Fred Feinberg & Eleanor Feit & Sam Hui & Joseph Johnson & John Liechty & James Orlin & Vithala Rao, 2008. "Beyond conjoint analysis: Advances in preference measurement," Marketing Letters, Springer, vol. 19(3), pages 337-354, December.
    7. Lurie, Nicholas H. & Wen, Na, 2014. "Simple Decision Aids and Consumer Decision Making," Journal of Retailing, Elsevier, vol. 90(4), pages 511-523.
    8. Daria Dzyabura & John R. Hauser, 2019. "Recommending Products When Consumers Learn Their Preference Weights," Marketing Science, INFORMS, vol. 38(3), pages 417-441, May.
    9. Ronny Baierl, 2018. "Understanding Entrepreneurial Team Decisions: Measuring Team Members’ Influences With The Metricized Limit Conjoint Analysis," SAGE Open, , vol. 8(2), pages 21582440187, May.
    10. Jella Pfeiffer & Michael Scholz, 2013. "A Low-Effort Recommendation System with High Accuracy," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 5(6), pages 397-408, December.
    11. Aksoy, Lerzan & Cooil, Bruce & Lurie, Nicholas H., 2011. "Decision Quality Measures in Recommendation Agents Research," Journal of Interactive Marketing, Elsevier, vol. 25(2), pages 110-122.
    12. Xingyue (Luna) Zhang & James A. Dearden & Yuliang Yao, 2022. "Let them stay or let them go? Online retailer pricing strategy for managing stockouts," Production and Operations Management, Production and Operations Management Society, vol. 31(11), pages 4173-4190, November.
    13. Scholz, Michael & Dorner, Verena & Schryen, Guido & Benlian, Alexander, 2017. "A configuration-based recommender system for supporting e-commerce decisions," European Journal of Operational Research, Elsevier, vol. 259(1), pages 205-215.
    14. Rossella Berni & Nedka Dechkova Nikiforova & Patrizia Pinelli, 2024. "An Optimal Design through a Compound Criterion for Integrating Extra Preference Information in a Choice Experiment: A Case Study on Moka Ground Coffee," Stats, MDPI, vol. 7(2), pages 1-16, June.

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