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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
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    References listed on IDEAS

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    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.

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    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

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