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Consumer search: evidence from path-tracking data

Author

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  • Pinna, Fabio
  • Seiler, Stephan

Abstract

We estimate the effect of consumer search on the price of the purchased product in a physical store environment. We implement the analysis using a unique data set obtained from radio frequency identification tags, which are attached to supermarket shopping carts. This technology allows us to record consumers' purchases as well as the time they spent in front of the shelf when contemplating which product to buy, giving us a direct measure of search effort. Controlling for a host of confounding factors, we estimate that an additional minute spent searching lowers price paid by $2.10 which represents 8 percent of average trip-level expenditure.

Suggested Citation

  • Pinna, Fabio & Seiler, Stephan, 2014. "Consumer search: evidence from path-tracking data," LSE Research Online Documents on Economics 60447, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:60447
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    File URL: http://eprints.lse.ac.uk/60447/
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    References listed on IDEAS

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    1. Hoyer, Wayne D, 1984. "An Examination of Consumer Decision Making for a Common Repeat Purchase Product," Journal of Consumer Research, Oxford University Press, vol. 11(3), pages 822-829, December.
    2. Babur De Los Santos & Ali Hortacsu & Matthijs R. Wildenbeest, 2012. "Testing Models of Consumer Search Using Data on Web Browsing and Purchasing Behavior," American Economic Review, American Economic Association, vol. 102(6), pages 2955-2980, October.
    3. Stephan Seiler, 2013. "The impact of search costs on consumer behavior: A dynamic approach," Quantitative Marketing and Economics (QME), Springer, vol. 11(2), pages 155-203, June.
    4. Draganska, Michaela & Klapper, Daniel, 2010. "Choice Set Heterogeneity and the Role of Advertising: An Analysis with Micro and Macro Data," Research Papers 2063, Stanford University, Graduate School of Business.
    5. Elisabeth Honka, 2014. "Quantifying search and switching costs in the US auto insurance industry," RAND Journal of Economics, RAND Corporation, vol. 45(4), pages 847-884, December.
    6. Sam K. Hui & Peter S. Fader & Eric T. Bradlow, 2009. "Path Data in Marketing: An Integrative Framework and Prospectus for Model Building," Marketing Science, INFORMS, vol. 28(2), pages 320-335, 03-04.
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    Cited by:

    1. Petrikaitė, Vaiva, 2016. "Collusion with costly consumer search," International Journal of Industrial Organization, Elsevier, vol. 44(C), pages 1-10.

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    More about this item

    Keywords

    Consumer search; in-store marketing; path data;

    JEL classification:

    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • L15 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Information and Product Quality

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