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Enriching Scanner Panel Models with Choice Experiments

Author

Listed:
  • 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)

Abstract

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.

Suggested Citation

  • Joffre Swait & Rick L. Andrews, 2003. "Enriching Scanner Panel Models with Choice Experiments," Marketing Science, INFORMS, vol. 22(4), pages 442-460, September.
  • Handle: RePEc:inm:ormksc:v:22:y:2003:i:4:p:442-460
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    File URL: http://dx.doi.org/10.1287/mksc.22.4.442.24910
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    References listed on IDEAS

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    Cited by:

    1. G. Verstraeten & D. Van Den Poel, 2006. "Using Predicted Outcome Stratified Sampling to Reduce the Variability in Predictive Performance of a One-Shot Train-and-Test Split for Individual Customer Predictions," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 06/360, Ghent University, Faculty of Economics and Business Administration.
    2. Hinz, Oliver & Schulze, Christian & Takac, Carsten, 2014. "New product adoption in social networks: Why direction matters," Journal of Business Research, Elsevier, vol. 67(1), pages 2836-2844.
    3. 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.
    4. 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.
    5. Koen Pauwels, 2004. "How Dynamic Consumer Response, Competitor Response, Company Support, and Company Inertia Shape Long-Term Marketing Effectiveness," Marketing Science, INFORMS, vol. 23(4), pages 596-610, June.
    6. Michael P. Keane & Susan Thorp, 2016. "Complex Decision Making: The Roles of Cognitive Limitations, Cognitive Decline and Ageing," Economics Papers 2016-W10, Economics Group, Nuffield College, University of Oxford.
    7. Jordan Louviere & Joffre Swait, 2010. "—Discussion of “Alleviating the Constant Stochastic Variance Assumption in Decision Research: Theory, Measurement, and Experimental Test”," Marketing Science, INFORMS, vol. 29(1), pages 18-22, 01-02.
    8. Els Breugelmans & Katia Campo & Els Gijsbrechts, 2007. "Shelf sequence and proximity effects on online grocery choices," Marketing Letters, Springer, vol. 18(1), pages 117-133, June.
    9. repec:eee:hapoch:v1_661 is not listed on IDEAS
    10. Louviere, Jordan J. & Islam, Towhidul, 2008. "A comparison of importance weights and willingness-to-pay measures derived from choice-based conjoint, constant sum scales and best-worst scaling," Journal of Business Research, Elsevier, vol. 61(9), pages 903-911, September.
    11. Girish Punj, 2006. "—Structural Modeling in Marketing: Some Future Possibilities," Marketing Science, INFORMS, vol. 25(6), pages 622-624, 11-12.
    12. David Hansen, 2004. "Assessing Industrial Buyer Preferences: Using the Swait-Louviere Test to Test the Key Informant Assumption," Marketing Letters, Springer, vol. 15(4), pages 223-236, December.
    13. J. Burez & D. Van Den Poel, 2008. "Handling class imbalance in customer churn prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/517, Ghent University, Faculty of Economics and Business Administration.
    14. Michael P. Keane & Nada Wasi, 2013. "The Structure of Consumer Taste Heterogeneity in Revealed vs. Stated Preference Data," Economics Papers 2013-W10, Economics Group, Nuffield College, University of Oxford.
    15. Steven M. Shugan, 2004. "Endogeneity in Marketing Decision Models," Marketing Science, INFORMS, vol. 23(1), pages 1-3.
    16. Marcel Fritz & Christian Schlereth & Stefan Figge, 2011. "Empirical Evaluation of Fair Use Flat Rate Strategies for Mobile Internet," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 3(5), pages 269-277, October.
    17. 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.
    18. Steven M. Shugan, 2003. "Editorial: Journal Rankings: Save the Outlets for Your Research," Marketing Science, INFORMS, vol. 22(4), pages 437-441.
    19. Christian Schlereth & Christine Eckert & Bernd Skiera, 2012. "Using discrete choice experiments to estimate willingness-to-pay intervals," Marketing Letters, Springer, vol. 23(3), pages 761-776, September.
    20. Breugelmans Els & Campo Katia & Gijsbrechts Els, 2005. "Shelf Sequence and Proximity Effects on Online Grocery Choices," Research Memorandum 052, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).

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