“Groupon Fatigue: Search & Learning in a Daily Deals Site” by Dr. Mantian Hu
Marketing Seminar
Speaker:
Dr. Mantian Hu
Assistant Professor
Chinese University of Hong Kong
Abstract:
Daily deal “fatigue” refers to a phenomenon in which consumers get tired of deals from daily deal sites (e.g., Groupon) Such fatigue, if it exists, poses a challenge to the daily deal industry. We study purchase behavior of daily deals with individual clickstream data on browsing sessions of consumers who newly subscribe to Groupon between January and March 2011. The data reveal two patterns. First, consistent with the notion of “fatigue,” the probability of a consumer clicking on a merchant in the emailed newsletter declines over time. Second, the probability that the consumer makes a purchase conditional on clicking increases over time. Our objective is to propose a model that rationalizes these patterns and to then provide insights for companies to deal with “fatigue”.
When consumers first subscribe to a daily deal site, they are unlikely to be fully informed about the quality of the deals offered on the site. The daily newsletter provides price and some limited information about that day’s featured deals. To learn more about quality, consumers need to click on the newsletter, go to the deal’s website and invest time and effort to learn about quality. Such a search for information is costly and only provides a noisy signal of quality to consumers. Further, consumers do not know the future deals they may receive. Given the uncertainty about quality and the cost of searching, consumers are more likely to search early on (i.e., click on the newsletter). As they learn about deal quality, they need to search less; resulting in clicks declining over time. As learning accumulates, consumers are better at recognizing deals of higher quality to click on and they are more likely to click in order to purchase rather than to learn about quality. This results in an increase in the conditional probability of purchasing. We formulate a dynamic model of search and learning based on the above characterization of consumer behavior. We show that the model is able to replicate the patterns in the data. Next, we estimate the parameters of the model and provide managerial insights to daily deal websites based on our findings and policy simulations.
