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Search and Learning at a Daily Deals Website

Author

Listed:
  • Mantian (Mandy) Hu

    (CUHK Business School, The Chinese University of Hong Kong, Hong Kong)

  • Chu (Ivy) Dang

    (CUHK Business School, The Chinese University of Hong Kong, Hong Kong)

  • Pradeep K. Chintagunta

    (Booth School of Business, University of Chicago, Chicago, Illinois 60637)

Abstract

We study consumers’ purchase behavior on daily deal websites (e.g., Groupon promotions) using individual clickstream data on the browsing history of new subscribers to Groupon between January and March 2011. We observe two patterns in the data. First, the probability that a given consumer clicks on a merchant in the emailed newsletter declines over time, which seems to be consistent with the notion of consumer “fatigue”—a phenomenon highlighted by the popular press. Second, the probability that the consumer makes a purchase conditional on clicking increases over time, which seems contrary to the notion of “fatigue.” To reconcile these two observations, we propose a model that rationalizes these patterns and then use it to provide insights for companies in the daily deal industry. When consumers first subscribe to a daily deal website, they are unlikely to be fully informed about the quality of the deals offered on that site. The daily newsletter provides only the price and some limited information about that day’s featured deal. To learn more about quality, consumers need to click on the emailed newsletter; this takes them to the deal’s website, where they invest time and effort to learn about the deal’s quality. Such a search for information is costly. Furthermore, consumers do not know about the quality of deals they may receive in the future. Given the cost of searching and the uncertainty about the quality of future deals, consumers are more likely to search early on (i.e., click on the newsletter) in their tenure. As they learn about the distribution of the quality of deals on Groupon, they require less searching, resulting in a decline in clicks over time. As learning accumulates, consumers are better at recognizing the position of a deal in the quality distribution of Groupon deals and are therefore more likely to purchase the clicked deals. This results in an increase in the conditional probability of purchasing. We formulate a dynamic model of search and Dirichlet learning based on the above characterization of consumer behavior. We show that the model is able to replicate patterns in the data. Next, we estimate the parameters of the model and provide insights for managers of daily deal websites based on our findings and policy simulations.

Suggested Citation

  • Mantian (Mandy) Hu & Chu (Ivy) Dang & Pradeep K. Chintagunta, 2019. "Search and Learning at a Daily Deals Website," Marketing Science, INFORMS, vol. 38(4), pages 609-642, July.
    • Handle: RePEc:inm:ormksc:v:38:y:2019:i:4:p:609-642
    DOI: 10.1287/mksc.2019.1156
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    2. Yao Tang & Xu Guan, 2022. "Seller Organization and Percentage Fee Design in the Daily Deal Market," Information Systems Research, INFORMS, vol. 33(4), pages 1287-1302, December.
    3. Tang, Yao & Chen, Rachel R. & Guan, Xu, 2021. "Daily-deal market with consumer retention: Price discrimination or quality differentiation," Omega, Elsevier, vol. 102(C).
    4. Xiangyu Gao & Stefanus Jasin & Sajjad Najafi & Huanan Zhang, 2022. "Joint Learning and Optimization for Multi-Product Pricing (and Ranking) Under a General Cascade Click Model," Management Science, INFORMS, vol. 68(10), pages 7362-7382, October.
    5. Carlson, Keith & Kopalle, Praveen K. & Riddell, Allen & Rockmore, Daniel & Vana, Prasad, 2023. "Complementing human effort in online reviews: A deep learning approach to automatic content generation and review synthesis," International Journal of Research in Marketing, Elsevier, vol. 40(1), pages 54-74.
    6. Yao Tang, 2023. "A product strategy for daily deal campaigns utilizing demand expansion and consumer leakage," Electronic Commerce Research, Springer, vol. 23(3), pages 1861-1883, September.
    7. Ludovica Cesareo & Claudia Townsend & Eugene Pavlov, 2023. "Hideous but worth it: Distinctive ugliness as a signal of luxury," Journal of the Academy of Marketing Science, Springer, vol. 51(3), pages 636-657, May.
    8. Venkatesh Shankar & Sohil Parsana, 2022. "An overview and empirical comparison of natural language processing (NLP) models and an introduction to and empirical application of autoencoder models in marketing," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1324-1350, November.
    9. Henrika Langen & Martin Huber, 2022. "How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign," Papers 2204.10820, arXiv.org, revised Jun 2022.
    10. Xia, Feihong & Chatterjee, Rabikar & Venkatesh, R., 2022. "Clinching the deal: An empirical study of the drivers of diffusion of daily deals," Journal of Business Research, Elsevier, vol. 149(C), pages 824-832.
    11. von Zahn, Moritz & Bauer, Kevin & Mihale-Wilson, Cristina & Jagow, Johanna & Speicher, Max & Hinz, Oliver, 2022. "The smart green nudge: Reducing product returns through enriched digital footprints & causal machine learning," SAFE Working Paper Series 363, Leibniz Institute for Financial Research SAFE, revised 2022.
    12. Chalil, Tengku Munawar & Dahana, Wirawan Dony & Baumann, Chris, 2020. "How do search ads induce and accelerate conversion? The moderating role of transaction experience and organizational type," Journal of Business Research, Elsevier, vol. 116(C), pages 324-336.
    13. Jorge Mejia & Anandasivam Gopal & Michael Trusov, 2020. "Deal or No Deal? Online Deals, Retailer Heterogeneity, and Brand Evaluations in a Competitive Environment," Information Systems Research, INFORMS, vol. 31(4), pages 1087-1106, December.

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