Optimizing Click-through in Online Rankings for Partially Anonymous Consumers
AbstractWhile 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.
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Bibliographic InfoPaper provided by Indiana University, Kelley School of Business, Department of Business Economics and Public Policy in its series Working Papers with number 2012-04.
Date of creation: May 2012
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consumer search; hotel industry; popularity rankings; platform; collaborative fitering; click-through rfates; customization;
Find related papers by JEL classification:
- D43 - Microeconomics - - Market Structure and Pricing - - - Oligopoly and Other Forms of Market Imperfection
- D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
- L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-10-27 (All new papers)
- NEP-COM-2012-10-27 (Industrial Competition)
- NEP-MKT-2012-10-27 (Marketing)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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.
- Berman, Ron & Katona, Zsolt, 2010. "The Role of Search Engine Optimization in Search Rankings," MPRA Paper 20129, University Library of Munich, Germany.
- Michael R. Baye & Babur De los Santos & Matthijs R. Wildenbeest, 2012. "What's in a Name? Measuring Prominence, and its Impact on Organic Traffic from Search Engines," Working Papers 2012-09, Indiana University, Kelley School of Business, Department of Business Economics and Public Policy.
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