Inferring Consideration Sets from Sales Transaction Data

Understanding consumer preferences is critical when optimizing prices and planning in retail operations, and when matching supply and demand in online platforms. In pursuing such objective, the identification of the consideration set of the consumers (i.e., the set of products really accounted for by consumers prior to making a choice) is indeed a fundamental input. In this paper we propose a methodology to identify consideration sets from sales transactions data in a data driven way. We assume that customers are boundedly rational and make their purchases in a two-stage process. First, they sample their consideration set and then purchase the most preferred item therein. Our contribution to the literature is two-fold. Theoretically, we address the problem of identifiability of consider-then-choose models from data. Since calibrating this class of choice models is a hard problem, we propose a framework to effectively estimate them and infer consideration sets. The methodology to model the consideration set formation is founded on machine learning techniques that can account for nonlinear-in-parameter utilities in a tractable way. Then we apply the proposed methodology to retail store data and data obtained from a car-sharing platform, and observe that accounting for consideration sets can boost the predictive performance in comparison with classical choice-based demand benchmarks. Our findings suggest that consider-then-choose models tend to be rather robust to the degree of ambiguity in the consideration set definition, and their relative importance in prediction tasks increases with this noise. Moreover, we show that the consider-then-choose type of choice models can provide important managerial insights about the consideration set formation