We examine the profitability of personalized pricing policies that are derived using different specifications of demand in a typical retail setting with consumer-level panel data. We generate pricing policies from a variety of models, including Bayesian hierarchical choice models, regularized...

We propose a demand estimation method that allows researchers to estimate substitution patterns from unstructured image and text data. We first employ a series of machine learning models to measure product similarity from products' images and textual descriptions. We then estimate a nested logit...

We provide a detailed overview of the empirical implementation of the sequential search model proposed by Weitzman (1979). We discuss the assumptions underlying the model, the identifica-tion of search cost and preference parameters, the necessary normalizations of utility parameters,...

Researchers are increasingly able to observe consumers’ behavior prior to a purchase, such as their navigation through a store or website and the products they consider. Such pre-purchase (or search) data can be valuable to researchers in a variety of ways: as an additional source of...

We quantify the prevalence of undisclosed influencer posts on Twitter across a large set of brands based on a unique data set of over 100 million posts. We develop a novel method to detect undisclosed influencer posts and find that 96% of influencer posts are not disclosed as such. Despite...

We analyze structural state dependence in brand choice using variation from brand switching during stock-outs caused by hurricanes. We derive a simple test for structural state dependence based on the time-series of choice persistence for households affected by the stock-outs. Using data from...