IDEAS home Printed from https://ideas.repec.org/p/iuk/wpaper/2012-04.html
   My bibliography  Save this paper

Optimizing Click-through in Online Rankings for Partially Anonymous Consumers

Author

Listed:
  • Babur De los Santos

    (Department of Business Economics and Public Policy, Indiana University Kelley School of Business)

  • Sergei Koulayev

    (Keystone Strategy)

Abstract

While considering differentiated products for purchasing decisions, it is costly for consumers to obtain the necessary information to weigh the various alternatives. The vast amount of information available online has revolutionized the way firms present consumers with product options. Presenting the best alternatives reduces search costs associated with a consumer finding the right product. Heterogeneity in consumers' preference for products with multiple attributes makes it challenging to present a relevant ranking, especially when important characteristics, such as price, differ between the formation of the underlying ranking and the consumer's search process. We use novel data on consumer click-stream behavior from a major web-based hotel comparison platform to estimate a random coefficient discrete choice model. We are then able to infer consumer preferences regarding a set of product attributes and propose an optimal ranking tailored to anonymous consumers with different price sensitivity. We are able to customize rankings by relating price sensitivity to their request parameters, such as the length of stay, number of guests, and day of the week of the stay. In contrast to a myopic popularity-based ranking, our model accounts for the rapidly changing prices that characterize the hotel industry, consumers' expected search strategies including result sorting and filtering, and consumer heterogeneity. The platform must determine which hotel ordering maximizes consumers' click-through rates (CTR) based on the information available to the platform at that time, its assessment of consumers' preferences, and the expected consumer type based on request parameters. We find that consumers' CTRs more than double when consumers are provided consumers with customized rankings that refl ect the price/quality trade-off inferred from the consumer's request parameters. We show that the optimal ranking results in consumers' welfare 173 percent greater on average than in the original ranking.

Suggested Citation

  • Babur De los Santos & Sergei Koulayev, 2012. "Optimizing Click-through in Online Rankings for Partially Anonymous Consumers," Working Papers 2012-04, Indiana University, Kelley School of Business, Department of Business Economics and Public Policy.
    • Handle: RePEc:iuk:wpaper:2012-04
    as

    Download full text from publisher

    File URL: http://kelley.iu.edu/riharbau/RePEc/iuk/wpaper/bepp2012-04-DelosSantos-Koulayev.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Berman, Ron & Katona, Zsolt, 2010. "The Role of Search Engine Optimization in Search Rankings," MPRA Paper 20129, University Library of Munich, Germany.
    2. Kenneth Hendricks & Alan Sorensen & Thomas Wiseman, 2012. "Observational Learning and Demand for Search Goods," American Economic Journal: Microeconomics, American Economic Association, vol. 4(1), pages 1-31, February.
    3. 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.
    4. Sangkil Moon & Gary J. Russell, 2008. "Predicting Product Purchase from Inferred Customer Similarity: An Autologistic Model Approach," Management Science, INFORMS, vol. 54(1), pages 71-82, January.
    5. Ho, Daniel E. & Imai, Kosuke, 2006. "Randomization Inference With Natural Experiments: An Analysis of Ballot Effects in the 2003 California Recall Election," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 888-900, September.
    6. Jun B. Kim & Paulo Albuquerque & Bart J. Bronnenberg, 2010. "Online Demand Under Limited Consumer Search," Marketing Science, INFORMS, vol. 29(6), pages 1001-1023, 11-12.
    7. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2012. "Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content," Marketing Science, INFORMS, vol. 31(3), pages 493-520, May.
    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. Baye, Michael R. & De los Santos, Babur & Wildenbeest, Matthijs R., 2016. "What’s in a name? Measuring prominence and its impact on organic traffic from search engines," Information Economics and Policy, Elsevier, vol. 34(C), pages 44-57.
    2. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2014. "Examining the Impact of Ranking on Consumer Behavior and Search Engine Revenue," Management Science, INFORMS, vol. 60(7), pages 1632-1654, July.
    3. Sergei Koulayev, 2014. "Search for differentiated products: identification and estimation," RAND Journal of Economics, RAND Corporation, vol. 45(3), pages 553-575, September.
    4. Fishman, Arthur & Lubensky, Dmitry, 2018. "Search prominence and return costs," International Journal of Industrial Organization, Elsevier, vol. 58(C), pages 136-161.

    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. Babur De los Santos & Sergei Koulayev, 2017. "Optimizing Click-Through in Online Rankings with Endogenous Search Refinement," Marketing Science, INFORMS, vol. 36(4), pages 542-564, July.
    2. 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.
    3. Daria Dzyabura & John R. Hauser, 2019. "Recommending Products When Consumers Learn Their Preference Weights," Marketing Science, INFORMS, vol. 38(3), pages 417-441, May.
    4. Hana Choi & Carl F. Mela, 2019. "Monetizing Online Marketplaces," Marketing Science, INFORMS, vol. 38(6), pages 948-972, November.
    5. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2019. "Modeling Consumer Footprints on Search Engines: An Interplay with Social Media," Management Science, INFORMS, vol. 65(3), pages 1363-1385, March.
    6. Anindya Ghose & Avi Goldfarb & Sang Pil Han, 2013. "How Is the Mobile Internet Different? Search Costs and Local Activities," Information Systems Research, INFORMS, vol. 24(3), pages 613-631, September.
    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. Raluca M. Ursu & Qingliang Wang & Pradeep K. Chintagunta, 2020. "Search Duration," Marketing Science, INFORMS, vol. 39(5), pages 849-871, September.
    9. Giovanni Compiani & Gregory Lewis & Sida Peng & Peichun Wang, 2024. "Online Search and Optimal Product Rankings: An Empirical Framework," Marketing Science, INFORMS, vol. 43(3), pages 615-636, May.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Jia Liu & Olivier Toubia, 2018. "A Semantic Approach for Estimating Consumer Content Preferences from Online Search Queries," Marketing Science, INFORMS, vol. 37(6), pages 930-952, November.
    15. Chris Gu & Yike Wang, 2022. "Consumer Online Search with Partially Revealed Information," Management Science, INFORMS, vol. 68(6), pages 4215-4235, June.
    16. Sergei Koulayev, 2014. "Search for differentiated products: identification and estimation," RAND Journal of Economics, RAND Corporation, vol. 45(3), pages 553-575, September.
    17. Avery Haviv, 2022. "Consumer Search, Price Promotions, and Counter-Cyclic Pricing," Marketing Science, INFORMS, vol. 41(2), pages 294-314, March.
    18. Gu, Chris & Wang, Yike, 2022. "Consumer online search with partially revealed information," LSE Research Online Documents on Economics 109871, London School of Economics and Political Science, LSE Library.
    19. 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.
    20. Elisabeth Honka & Pradeep Chintagunta, 2017. "Simultaneous or Sequential? Search Strategies in the U.S. Auto Insurance Industry," Marketing Science, INFORMS, vol. 36(1), pages 21-42, January.

    More about this item

    Keywords

    consumer search; hotel industry; popularity rankings; platform; collaborative fitering; click-through rfates; customization;
    All these keywords.

    JEL classification:

    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets

    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:iuk:wpaper:2012-04. 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: Rick Harbaugh (email available below). General contact details of provider: https://edirc.repec.org/data/dpiubus.html .

    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.