IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v40y2021i3p428-458.html
   My bibliography  Save this article

A Dynamic Model of Optimal Retargeting

Author

Listed:
  • J. Miguel Villas-Boas

    (Haas School of Business, University of California, Berkeley, California 94720)

  • Yunfei (Jesse) Yao

    (Haas School of Business, University of California, Berkeley, California 94720)

Abstract

A consumer searching for information on a product may be indicative that the consumer has some interest in that product but is still undecided about whether to purchase it. Some of this consumer search for information is not observable to firms, but some may be observable. Once a firm observes a consumer searching for information on its product, the firm may then want to try to provide further information about the product to that consumer, a phenomenon that has been known in electronic commerce as retargeting. Firms may not observe all activities by a consumer in searching for information, may not be able to observe the information gained by consumers, and may not be able to observe whether a consumer stopped searching for information. A consumer could stop searching either because he received information of poor fit with the product, because he bought the product (which may be unobservable to the firm), or because he exogenously lost interest in the product. This paper presents a dynamic model with these features characterizing the optimal advertising retargeting strategy by the firm. We find that a forward-looking firm can advertise more or less than a myopic firm to gain more information about whether the consumer is searching for information, advertising more if the effect of advertising is relatively high. We characterize how the optimal advertising retargeting strategy is affected by the ability of the firm to observe when the consumer purchases the product, when the firm is better able to observe the consumer search behavior, and by the informativeness of the signal received by the consumer. We find that better tracking of consumer search behavior could be beneficial for consumers, because it may reduce the length of time when a consumer receives retargeting, but that it also enlarges the region of firm’s beliefs where retargeting is optimal. Finally, we also find that the value of retargeting is highest for an intermediate value of the likelihood of the consumer receiving an informative signal and that retargeting may allow the firm to charge higher prices if consumers are forward-looking.

Suggested Citation

  • J. Miguel Villas-Boas & Yunfei (Jesse) Yao, 2021. "A Dynamic Model of Optimal Retargeting," Marketing Science, INFORMS, vol. 40(3), pages 428-458, May.
  • Handle: RePEc:inm:ormksc:v:40:y:2021:i:3:p:428-458
    DOI: 10.1287/mksc.2020.1267
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.2020.1267
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mksc.2020.1267?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Drew Fudenberg & Philipp Strack & Tomasz Strzalecki, 2018. "Speed, Accuracy, and the Optimal Timing of Choices," American Economic Review, American Economic Association, vol. 108(12), pages 3651-3684, December.
    2. Dmitri Kuksov, 2004. "Buyer Search Costs and Endogenous Product Design," Marketing Science, INFORMS, vol. 23(4), pages 490-499, May.
    3. Fernando Branco & Monic Sun & J. Miguel Villas-Boas, 2012. "Optimal Search for Product Information," Management Science, INFORMS, vol. 58(11), pages 2037-2056, November.
    4. T. Tony Ke & Zuo-Jun Max Shen & J. Miguel Villas-Boas, 2016. "Search for Information on Multiple Products," Management Science, INFORMS, vol. 62(12), pages 3576-3603, December.
    5. Qiaowei Shen & J. Miguel Villas-Boas, 2018. "Behavior-Based Advertising," Management Science, INFORMS, vol. 64(5), pages 2047-2064, May.
    6. Juanjuan Zhang, 2011. "The Perils of Behavior-Based Personalization," Marketing Science, INFORMS, vol. 30(1), pages 170-186, 01-02.
    7. Drew Fudenberg & Jean Tirole, 2000. "Customer Poaching and Brand Switching," RAND Journal of Economics, The RAND Corporation, vol. 31(4), pages 634-657, Winter.
    8. Ke, T. Tony & Villas-Boas, J. Miguel, 2019. "Optimal learning before choice," Journal of Economic Theory, Elsevier, vol. 180(C), pages 383-437.
    9. Zibin Xu & Anthony Dukes, 2019. "Product Line Design Under Preference Uncertainty Using Aggregate Consumer Data," Marketing Science, INFORMS, vol. 38(4), pages 669-689, July.
    10. Jiwoong Shin & K. Sudhir, 2010. "A Customer Management Dilemma: When Is It Profitable to Reward One's Own Customers?," Marketing Science, INFORMS, vol. 29(4), pages 671-689, 07-08.
    11. Ganesh Iyer & David Soberman & J. Miguel Villas-Boas, 2005. "The Targeting of Advertising," Marketing Science, INFORMS, vol. 24(3), pages 461-476, May.
    12. J. Miguel Villas-Boas, 2004. "Price Cycles in Markets with Customer Recognition," RAND Journal of Economics, The RAND Corporation, vol. 35(3), pages 486-501, Autumn.
    13. Yuxin Chen & Xinxin Li & Monic Sun, 2017. "Competitive Mobile Geo Targeting," Marketing Science, INFORMS, vol. 36(5), pages 666-682, September.
    14. J. Miguel Villas-Boas, 1999. "Dynamic Competition with Customer Recognition," RAND Journal of Economics, The RAND Corporation, vol. 30(4), pages 604-631, Winter.
    15. Z. Eddie Ning, 2021. "List Price and Discount in a Stochastic Selling Process," Marketing Science, INFORMS, vol. 40(2), pages 366-387, March.
    16. Dirk Bergemann & Juuso Välimäki, 2006. "Dynamic Pricing of New Experience Goods," Journal of Political Economy, University of Chicago Press, vol. 114(4), pages 713-743, August.
    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. Matveenko, Andrei & Starkov, Egor, 2023. "Sparking curiosity or tipping the scales? Targeted advertising with consumer learning," Journal of Economic Behavior & Organization, Elsevier, vol. 213(C), pages 172-192.

    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. Qiaowei Shen & J. Miguel Villas-Boas, 2018. "Behavior-Based Advertising," Management Science, INFORMS, vol. 64(5), pages 2047-2064, May.
    2. Bernard Caillaud & Romain De Nijs, 2014. "Strategic Loyalty Reward in Dynamic Price Discrimination," Marketing Science, INFORMS, vol. 33(5), pages 725-742, September.
    3. Flavio Pino, 2022. "The microeconomics of data – a survey," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 49(3), pages 635-665, September.
    4. Z. Eddie Ning & J. Miguel Villas-Boas, 2023. "Browse or Experience," Marketing Science, INFORMS, vol. 42(2), pages 336-359, March.
    5. Bita Hajihashemi & Amin Sayedi & Jeffrey D. Shulman, 2022. "The Perils of Personalized Pricing with Network Effects," Marketing Science, INFORMS, vol. 41(3), pages 477-500, May.
    6. Upender Subramanian & Jagmohan S. Raju & Z. John Zhang, 2014. "The Strategic Value of High-Cost Customers," Management Science, INFORMS, vol. 60(2), pages 494-507, February.
    7. Ki-Eun Rhee & Raphael Thomadsen, 2017. "Behavior-Based Pricing in Vertically Differentiated Industries," Management Science, INFORMS, vol. 63(8), pages 2729-2740, August.
    8. Xuan Wang & Chi To Ng, 2020. "New retail versus traditional retail in e-commerce: channel establishment, price competition, and consumer recognition," Annals of Operations Research, Springer, vol. 291(1), pages 921-937, August.
    9. Jiwoong Shin & K. Sudhir & Dae-Hee Yoon, 2012. "When to "Fire" Customers: Customer Cost-Based Pricing," Management Science, INFORMS, vol. 58(5), pages 932-947, May.
    10. Arieh Gavious & Ella Segev, 2017. "Price Discrimination Based on Buyers’ Purchase History," Dynamic Games and Applications, Springer, vol. 7(2), pages 229-265, June.
    11. Pedro M. Gardete & Yakov Bart, 2018. "Tailored Cheap Talk: The Effects of Privacy Policy on Ad Content and Market Outcomes," Marketing Science, INFORMS, vol. 37(5), pages 733-752, September.
    12. Bing Jing, 2017. "Behavior-Based Pricing, Production Efficiency, and Quality Differentiation," Management Science, INFORMS, vol. 63(7), pages 2365-2376, July.
    13. Chongwoo Choe & Stephen King & Noriaki Matsushima, 2018. "Pricing with Cookies: Behavior-Based Price Discrimination and Spatial Competition," Management Science, INFORMS, vol. 64(12), pages 5669-5687, December.
    14. Bing Jing, 2016. "Customer Recognition in Experience vs. Inspection Good Markets," Management Science, INFORMS, vol. 62(1), pages 216-224, January.
    15. Muzaffer Buyruk & Ertan Güner, 2022. "Personalization in airline revenue management: an overview and future outlook," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(2), pages 129-139, April.
    16. Wang, Yu & Li, Minqiang & Feng, Haiyang & Feng, Nan, 2023. "Which is better for competing firms with quality increasing: behavior-based price discrimination or uniform pricing?," Omega, Elsevier, vol. 118(C).
    17. Krista J. Li & Sanjay Jain, 2016. "Behavior-Based Pricing: An Analysis of the Impact of Peer-Induced Fairness," Management Science, INFORMS, vol. 62(9), pages 2705-2721, September.
    18. Bernard Caillaud & Romain de Nijs, 2011. "Strategic loyalty reward in dynamic price Discrimination," Working Papers halshs-00622291, HAL.
    19. Miettinen, Topi & Stenbacka, Rune, 2018. "Strategic short-termism: Implications for the management and acquisition of customer relationships," Journal of Economic Behavior & Organization, Elsevier, vol. 153(C), pages 200-222.
    20. Bernard Caillaud & Romain de Nijs, 2011. "Strategic loyalty reward in dynamic price Discrimination," PSE Working Papers halshs-00622291, HAL.

    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:inm:ormksc:v:40:y:2021:i:3:p:428-458. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.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.