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Enriching Scanner Panel Models with Choice Experiments

  • Joffre Swait

    ()

    (Advanis Inc., 12 W. University Ave. #205, Gainesville, Florida 32601, and University of Alberta)

  • Rick L. Andrews

    ()

    (Ourso College of Business Adminstration, Louisiana State University, Baton Rouge, Louisiana 70803)

This research examines the methods, viability, and benefits of pooling scanner panel choice data with compatible preference data from designed choice experiments. The fact that different choice data sources have diverse strengths and weaknesses suggests it might be possible to pool multiple sources to achieve improved models, due to offsetting advantages and disadvantages. For example, new attributes and attribute levels not included in the scanner panel data can be introduced via the choice experiment, while the scanner panel data captures preference dynamics, which is, at best, difficult with experimental data. Our application, involving liquid laundry detergent, establishes the feasibility and desirability of doing such augmentations of scanner panel data: The joint scanner panel/choice experiment model has significantly better prediction performance on a holdout data set than does a pure scanner panel model. Thus, we extend the concept of choice into another domain and demonstrate that data enrichment can add significantly to one's understanding of preferences reflected in scanner panel data.

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File URL: http://dx.doi.org/10.1287/mksc.22.4.442.24910
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Article provided by INFORMS in its journal Marketing Science.

Volume (Year): 22 (2003)
Issue (Month): 4 (September)
Pages: 442-460

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Handle: RePEc:inm:ormksc:v:22:y:2003:i:4:p:442-460
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  1. Swait, Joffre & Adamowicz, Wiktor, 2001. "Choice Environment, Market Complexity, and Consumer Behavior: A Theoretical and Empirical Approach for Incorporating Decision Complexity into Models of Consumer Choice," Organizational Behavior and Human Decision Processes, Elsevier, vol. 86(2), pages 141-167, November.
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  4. Randolph E. Bucklin & Sunil Gupta, 1999. "Commercial Use of UPC Scanner Data: Industry and Academic Perspectives," Marketing Science, INFORMS, vol. 18(3), pages 247-273.
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  9. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
  10. Burke, Raymond R, et al, 1992. " Comparing Dynamic Consumer Choice in Real and Computer-Simulated Environments," Journal of Consumer Research, Oxford University Press, vol. 19(1), pages 71-82, June.
  11. Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
  12. Keane, Michael, 1997. "Current Issues in Discrete Choice Modeling," MPRA Paper 52515, University Library of Munich, Germany.
  13. Peter M. Guadagni & John D. C. Little, 1983. "A Logit Model of Brand Choice Calibrated on Scanner Data," Marketing Science, INFORMS, vol. 2(3), pages 203-238.
  14. Swait, Joffre & Bernardino, Adriana, 2000. "Distinguishing taste variation from error structure in discrete choice data," Transportation Research Part B: Methodological, Elsevier, vol. 34(1), pages 1-15, January.
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