Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data

By Susan Athey, David M. Blei, Rob Donnelly, Francisco J. R. Ruiz, Tobias Schmidt. 2018

70 citations recorded by the source corpus.

This paper analyzes consumer choices over lunchtime restaurants using data from a sample of several thousand anonymous mobile phone users in the San Francisco Bay Area. The data is used to identify users' approximate typical morning location, as well as their choices of lunchtime restaurants. We build a model where restaurants have latent characteristics (whose distribution may depend on restaurant observables, such as star ratings, food category, and price range), each user has preferences for these latent characteristics, and these preferences are heterogeneous across users. Similarly, each item has latent characteristics that describe users' willingness to travel to the restaurant, and each user has individual-specific preferences for those latent characteristics. Thus, both users' willingness to travel and their base utility for each restaurant vary across user-restaurant pairs. We use a Bayesian approach to estimation. To make the estimation computationally feasible, we rely on variational inference to approximate the posterior distribution, as well as stochastic gradient descent as a computational approach. Our model performs better than more standard competing models such as multinomial logit and nested logit models, in part due to the personalization of the estimates. We analyze how consumers re-allocate their demand after a restaurant closes to nearby restaurants versus more distant restaurants with similar characteristics, and we compare our predictions to actual outcomes. Finally, we show how the model can be used to analyze counterfactual questions such as what type of restaurant would attract the most consumers in a given location.

Subfields: Management Science and Operations Research, Marketing, Transportation

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Citations in the corpus

Counterfactual inference for consumer choice across many product categories (2019, methodology): “It also extends the Time Travel Factorization Model (TTFM) introduced in Athey et al. (2018). A core difference is that while the TTFM model predicted choice of restaurant conditional on the choice to go out to eat, whereas in this paper we wish to predict the unconditional purchase probabilities fo”
Income Growth and the Distributional Effects of Urban Spatial Sorting (2019, unknown): “5 with respect to travel time from home, and Athey et al. (2018) find a distance elasticity of -1.”