Markov chain Monte Carlo and models of consideration set and parameter heterogeneity

In this paper the authors propose an integrated consideration set-brand choice model that is capable of accounting for the heterogeneity in consideration set and in the parameters of the brand choice model. The model is estimated by an approximation free Markov chain Monte Carlo sampling procedure and is applied to a scanner panel data. The main findings are: ignoring consideration set heterogeneity understates the impact of marketing mix and overstates the impact of preferences and past purchase feedback even when heterogeneity in parameters is modeled; the estimate of consideration set heterogeneity is robust to the inclusion of parameter heterogeneity; when consideration set heterogeneity is included the parameter heterogeneity takes on considerably less importance; the promotional response of households depends on their consideration set even if the underlying choice parameters are identical.