IDEAS home Printed from https://ideas.repec.org/p/cep/cepdps/dp1296.html
   My bibliography  Save this paper

Consumer Search: Evidence from Path-tracking Data

Author

Listed:
  • Fabio Pinna
  • Stephan Seiler

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

  • Fabio Pinna & Stephan Seiler, 2014. "Consumer Search: Evidence from Path-tracking Data," CEP Discussion Papers dp1296, Centre for Economic Performance, LSE.
    • Handle: RePEc:cep:cepdps:dp1296
    as

    Download full text from publisher

    File URL: https://cep.lse.ac.uk/pubs/download/dp1296.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Nitin Mehta & Surendra Rajiv & Kannan Srinivasan, 2003. "Price Uncertainty and Consumer Search: A Structural Model of Consideration Set Formation," Marketing Science, INFORMS, vol. 22(1), pages 58-84, June.
    3. 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.
    4. Hoyer, Wayne D, 1984. "An Examination of Consumer Decision Making for a Common Repeat Purchase Product," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 11(3), pages 822-829, December.
    5. 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.
    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.
    7. 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.
    8. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

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

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Helmers, Christian & Krishnan, Pramila & Patnam, Manasa, 2019. "Attention and saliency on the internet: Evidence from an online recommendation system," Journal of Economic Behavior & Organization, Elsevier, vol. 161(C), pages 216-242.
    3. Tiago Pires, 2016. "Costly search and consideration sets in storable goods markets," Quantitative Marketing and Economics (QME), Springer, vol. 14(3), pages 157-193, September.
    4. Stephan Seiler & Fabio Pinna, 2017. "Estimating Search Benefits from Path-Tracking Data: Measurement and Determinants," Marketing Science, INFORMS, vol. 36(4), pages 565-589, July.
    5. repec:diw:diwwpp:dp1681 is not listed on IDEAS
    6. Navid Mojir & K. Sudhir, 2014. "Price Search Across Time and Across Stores," Cowles Foundation Discussion Papers 1942R, Cowles Foundation for Research in Economics, Yale University, revised Jul 2019.
    7. Anocha Aribarg & Thomas Otter & Daniel Zantedeschi & Greg M. Allenby & Taylor Bentley & David J. Curry & Marc Dotson & Ty Henderson & Elisabeth Honka & Rajeev Kohli & Kamel Jedidi & Stephan Seiler & X, 2018. "Advancing Non-compensatory Choice Models in Marketing," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 82-92, March.
    8. Avery Haviv, 2022. "Consumer Search, Price Promotions, and Counter-Cyclic Pricing," Marketing Science, INFORMS, vol. 41(2), pages 294-314, March.
    9. Navid Mojir & K. Sudhir, 2014. "A Model of Multi-pass Search: Price Search across Stores and Time," Cowles Foundation Discussion Papers 1942R2, Cowles Foundation for Research in Economics, Yale University, revised Feb 2020.
    10. Pires, Tiago, 2018. "Measuring the effects of search costs on equilibrium prices and profits," International Journal of Industrial Organization, Elsevier, vol. 60(C), pages 179-205.
    11. Raluca M. Ursu & Qingliang Wang & Pradeep K. Chintagunta, 2020. "Search Duration," Marketing Science, INFORMS, vol. 39(5), pages 849-871, September.
    12. Yuxin Chen & Song Yao, 2017. "Sequential Search with Refinement: Model and Application with Click-Stream Data," Management Science, INFORMS, vol. 63(12), pages 4345-4365, December.
    13. Martin Gaynor & Carol Propper & Stephan Seiler, 2016. "Free to Choose? Reform, Choice, and Consideration Sets in the English National Health Service," American Economic Review, American Economic Association, vol. 106(11), pages 3521-3557, November.
    14. Charles Murry & Yiyi Zhou, 2020. "Consumer Search and Automobile Dealer Colocation," Management Science, INFORMS, vol. 66(5), pages 1909-1934, May.
    15. José Luis Moraga-González & Zsolt Sándor & Matthijs R Wildenbeest, 2023. "Consumer Search and Prices in the Automobile Market," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(3), pages 1394-1440.
    16. Bart J. Bronnenberg & Jun B. Kim & Carl F. Mela, 2016. "Zooming In on Choice: How Do Consumers Search for Cameras Online?," Marketing Science, INFORMS, vol. 35(5), pages 693-712, September.
    17. Thomas Blake & Chris Nosko & Steven Tadelis, 2016. "Returns to Consumer Search: Evidence from eBay," NBER Working Papers 22302, National Bureau of Economic Research, Inc.
    18. Ilya Morozov & Stephan Seiler & Xiaojing Dong & Liwen Hou, 2021. "Estimation of Preference Heterogeneity in Markets with Costly Search," Marketing Science, INFORMS, vol. 40(5), pages 871-899, September.
    19. Dubois, Pierre & Perrone, Helena, 2015. "Price Dispersion and Informational Frictions: Evidence from Supermarket Purchases," TSE Working Papers 15-606, Toulouse School of Economics (TSE), revised Sep 2017.
    20. Matsumoto, Brett & Spence, Forrest, 2016. "Price beliefs and experience: Do consumers’ beliefs converge to empirical distributions with repeated purchases?," Journal of Economic Behavior & Organization, Elsevier, vol. 126(PA), pages 243-254.
    21. 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.

    More about this item

    Keywords

    Consumer search; in-store marketing; path data;
    All these keywords.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cep:cepdps:dp1296. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://cep.lse.ac.uk/_new/publications/discussion-papers/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.