Retrieving Unobserved Consideration Sets from Household Panel Data

Abstract

We propose a new model to describe consideration, consisting of a multivariate probit model component for consideration and a multinomial probit model component for choice, given consideration. The approach allows one to analyze stated consideration set data, revealed consideration set (choice) data or both, while at the same time it allows for unobserved dependence in consideration among brands. In addition, the model accommodates different effects of the marketing mix on consideration and choice, an error process that is correlated over time, and unobserved consumer heterogeneity in both processes.We attempt to establish the validity of existing practice to infer consideration sets from observed choices in panel data. To this end, we collect data in an on-line choice experiment involving interactive supermarket shelves and post-choice questionnaires to measure the choice protocol and stated consideration levels. We show with these experimental data that underlying consideration sets can be reliably retrieved from choice data alone.Next, we estimate the model on IRI panel data. We have two main results. First, compared with the single-stage multinomial probit model, promotion effects are larger when they are included in the consideration stage of the two-stage model. Second, we find that consideration of brands does not covary greatly across brands once we account for observed effects.

Evaluation Set Size and Purchase: Evidence from a Product Search Engine

“…Wu and Rangaswamy (2003) focus on how reducing "fuzziness" 6 alters consideration set sizes in an online grocer setting; however, the products customers consider or evaluate are imputed (not observed), and the links between the antecedents of consideration or evaluation set size and fuzziness are not examined. van Nierop et al (2010) propose and estimate a model that captures unobserved consideration from discrete choice data. Kuksov and Villas-Boas (2010) investigate how search or evaluation costs may lead consumers not to search and not to choose if too many or too few alternatives are offered.…”

“…Kuksov and Villas-Boas (2010) investigate how search or evaluation costs may lead consumers not to search and not to choose if too many or too few alternatives are offered. Yet, neither van Nierop et al (2010) nor Kuksov and Villas-Boas (2010) investigate behavioral causes of ESS. Shi and Zhang (2014) research how experience with decision aids impacts online purchase behavior over time, but do not look at antecedents or consequences of evaluation sets.…”

Inference of Consumer Consideration Sets

“…A small literature circumvents this problem by collecting direct information on consideration sets. This is typically done via questionnaires in which participants state which products they considered, for example for hypothetical purchases in a virtual supermarket (Van Nierop et al, 2010), or coupled with actual purchase decisions, e.g. in the German coffee market (Draganska and Klapper, 2011) or in the U.S. auto insurance industry (Honka, 2014).…”

Stochastic Choice and Consideration Sets

“…5 See, for example, van Nierop, Bronnenberg, Paap, Wedel, and Franses (2010). 6 For example, our own "shortlisting" method (Manzini and Mariotti (2007)).…”