Search and Learning at a Daily Deals Website

Abstract

We formulate a dynamic model of search and Dirichlet learning to explain consumer behavior at a daily deal website.

The Smart Green Nudge: Reducing Product Returns through Enriched Digital Footprints & Causal Machine Learning

“…The increasing availability of large amounts of data has recently spawned the development of machine learning systems that predict individuals' responses to certain interventions. For example, machine learning predict individuals' price sensitivity (Arevalillo 2021), forecast consumer behavior (Hu et al 2019, Xia et al 2019, and personalize product recommendations (Anitha andKalaiarasu 2021, Ramzan et al 2019). To do so, trained machine learning models typically leverage data to produce highly accurate, individual-level predictions about a certain variable of interest.…”

The Importance of Price Beliefs in Consumer Search

“…Previous literature assumes either that consumers have rational expectations and do not learn about the price distribution (Honka 2014; Honka and Chintagunta 2017; Mehta, Rajiv, and Srinivasan 2003; Zwick et al 2003) or that consumers update their beliefs, which can explain revisits to previously searched information (Bronnenberg, Kim, and Mela 2016; Dang, Ursu, and Chintagunta 2020). The empirical literature allowing for learning assumes that consumers engage in Bayesian updating with either normally distributed priors (Chick and Frazier 2012; Ursu, Wang, and Chintagunta 2020; Zhang, Ursu, and Erdem 2020), Dirichlet priors (Hu, Dang, and Chintagunta 2019; Koulayev 2013; Wu 2017), or Dirichlet process priors (De los Santos, Hortacsu, and Wildenbeest 2017; Häubl, Dellaert, and Donkers 2010).…”

“…While previous research assumes that consumers have an informative prior (De los Santos, Hortacsu, and Wildenbeest 2017; Koulayev 2013), such that the expected value of the realizations from the process that generates prior beliefs corresponds to the true distribution of prices, more recent research attempts to estimate prior beliefs from observational data (Hu, Dang, and Chintagunta 2019; Ursu, Wang, and Chintagunta 2020; Zhang, Ursu, and Erdem 2020). Hu, Dang, and Chintagunta (2019) use relative variation in clicking and purchase to estimate Dirichlet prior beliefs that are homogeneous across consumers. By contrast, Ursu, Wang, and Chintagunta (2020) and Zhang, Ursu, and Erdem (2020) estimate heterogeneous prior uncertainty either as latent segments or as a function of the prior consumer experience using eye-tracking data, respectively.…”

The history of academic research in marketing and its implications for the future

“…For example, Xia et al (2019) developed a machine learning model for estimating shopping patterns from very large scanner datasets. Hu et al (2019) applied a deep learning model to develop a quality measure in their study of daily deals. Timoshenko and Hauser (2019) used a neural network to develop an efficient procedure for identifying customer needs.…”