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Personalized Ranking at a Mobile App Distribution Platform

Author

Listed:
  • Shengjun Mao

    (Faculty of Business and Economics, The University of Hong Kong, Hong Kong)

  • Sanjeev Dewan

    (Paul Merage School of Business, University of California– Irvine, Irvine, California 92697)

  • Yi-Jen (Ian) Ho

    (Smeal College of Business, Pennsylvania State University, University Park, Pennsylvania 16802)

Abstract

The ease of customer data collection has enabled the widespread personalization of content and services in digital platforms. We examine personalization in a hitherto unaddressed context: that of mobile app distribution. Specifically, we develop a comprehensive framework for the personalized ranking of app impressions, leveraging revealed preferences embedded in consumer clickstream data. To improve platform revenues, the framework jointly accounts for consumer utility and cost per action (CPA) margin, which is the revenue earned by the platform per app installation. To this end, we specify a structural model of click and installation choices, jointly estimated as a function of a comprehensive set of numerical (screen rank, quality, and popularity) and textual (titles, descriptions, and reviews) covariates. Our novel data set is at the granular user-impression level and uniquely includes app CPA margins paid to the platform. We conduct a series of policy experiments to quantify the value of personalization. Specifically, we show that a personalized hybrid margin and utility margin ranking scheme outperforms other personalized methods, including those based on utilities alone or a combination of utilities and margins. Overall, our analysis demonstrates how platforms can leverage routine consumer clickstream data to personalize the ranking of app impressions, thereby more effectively monetizing mobile app distribution.

Suggested Citation

  • Shengjun Mao & Sanjeev Dewan & Yi-Jen (Ian) Ho, 2023. "Personalized Ranking at a Mobile App Distribution Platform," Information Systems Research, INFORMS, vol. 34(3), pages 811-827, September.
  • Handle: RePEc:inm:orisre:v:34:y:2023:i:3:p:811-827
    DOI: 10.1287/isre.2022.1156
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    References listed on IDEAS

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    1. Raluca M. Ursu, 2018. "The Power of Rankings: Quantifying the Effect of Rankings on Online Consumer Search and Purchase Decisions," Marketing Science, INFORMS, vol. 37(4), pages 530-552, August.
    2. Avi Goldfarb & Catherine Tucker, 2011. "Online Display Advertising: Targeting and Obtrusiveness," Marketing Science, INFORMS, vol. 30(3), pages 389-404, 05-06.
    3. Avi Goldfarb & Catherine Tucker, 2011. "Rejoinder--Implications of "Online Display Advertising: Targeting and Obtrusiveness"," Marketing Science, INFORMS, vol. 30(3), pages 413-415, 05-06.
    4. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
    5. 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.
    6. Khim-Yong Goh & Cheng-Suang Heng & Zhijie Lin, 2013. "Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content," Information Systems Research, INFORMS, vol. 24(1), pages 88-107, March.
    7. McFadden, Daniel, 1974. "The measurement of urban travel demand," Journal of Public Economics, Elsevier, vol. 3(4), pages 303-328, November.
    8. Peter E. Rossi & Robert E. McCulloch & Greg M. Allenby, 1996. "The Value of Purchase History Data in Target Marketing," Marketing Science, INFORMS, vol. 15(4), pages 321-340.
    9. 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.
    10. Anindya Ghose & Sang Pil Han, 2014. "Estimating Demand for Mobile Applications in the New Economy," Management Science, INFORMS, vol. 60(6), pages 1470-1488, June.
    11. Hema Yoganarasimhan, 2020. "Search Personalization Using Machine Learning," Management Science, INFORMS, vol. 66(3), pages 1045-1070, March.
    12. Klapdor, Sebastian & Anderl, Eva M. & von Wangenheim, Florian & Schumann, Jan H., 2014. "Finding the Right Words: The Influence of Keyword Characteristics on Performance of Paid Search Campaigns," Journal of Interactive Marketing, Elsevier, vol. 28(4), pages 285-301.
    13. Erik Brynjolfsson & Astrid Dick & Michael Smith, 2010. "A nearly perfect market?," Quantitative Marketing and Economics (QME), Springer, vol. 8(1), pages 1-33, March.
    14. Hana Choi & Carl F. Mela, 2019. "Monetizing Online Marketplaces," Marketing Science, INFORMS, vol. 38(6), pages 948-972, November.
    15. Song Yao & Carl F. Mela, 2011. "A Dynamic Model of Sponsored Search Advertising," Marketing Science, INFORMS, vol. 30(3), pages 447-468, 05-06.
    16. Anindya Ghose & Sha Yang, 2009. "An Empirical Analysis of Search Engine Advertising: Sponsored Search in Electronic Markets," Management Science, INFORMS, vol. 55(10), pages 1605-1622, October.
    17. Neeraj Arora & Ty Henderson, 2007. "Embedded Premium Promotion: Why It Works and How to Make It More Effective," Marketing Science, INFORMS, vol. 26(4), pages 514-531, 07-08.
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