Gourab Mukherjee

We analyze consumer adoption of hybrid cars using automobile transaction data from the Sacramento market during the first half of $2007$, a critical period in the lifecycle of hybrid technology. Modeling demand for durable goods such as automobiles is made more difficult by the absence of repeat purchase data and pooling information across similar consumers is one way to address this data scarcity. We propose a new multinomial spatial probit model that connects different consumers using multiple weighted networks, which are based on different similarity structures. Unlike in the traditional multinomial spatial probit, different subsets of the parameter vector, i.e., the preference and marketing coefficients can be correlated in their own unique ways.

Parameter estimation is carried out via a novel Monte-Carlo Expectation-Maximization (MCEM) based approach, which enables the model to be used with a significantly greater number of consumers and choice alternatives than is possible with the standard model. The approach substitutes the computationally expensive E-step in the classical EM algorithm by an efficient Gibbs sampling-based evaluation and, additionally, implements the M-step using a fast back-fitting method that iteratively fits weighted regressions based on the associated similarity matrix for each coefficient subset. We establish the convergence properties of the proposed MCEM algorithm, present computational perspectives on the scalability of the proposed method, and provide a distributed computing-based implementation that yields parameter estimates and their standard errors.

We apply the model to sales data for compact cars from the Sacramento market and find that the best fitting version of our model is one in which the intercepts are based on the geographical closeness between consumers and the slope coefficients on the similarity of their previously owned vehicles. We summarize the cross-price elasticity matrices to produce clout and vulnerability measures for each vehicle and to produce competitive maps of the product category. We show how the multiple network weights explain the changes in price sensitivity of consumers across geographic locations, captures the variation in brand preferences among consumers and together deliver more accurate estimates of a consumer's hybrid purchase probability. Finally, we demonstrate how an automobile manufacturer can leverage the estimated heterogeneous spatial contiguity effects to improve the effectiveness of targeted promotions that are designed to accelerate the consumer adoption of the Toyota hybrid.

We propose and estimate a spatial autoregressive multinomial probit model in which consumers’ product preferences are correlated based upon their close they are to each other. Our proposed model uses a Bayesian structural uncertainty approach to combine multiple sources of such contiguity information and also incorporates consumer response heterogeneity. The model is applied to the unique problem of improving the efficacy of promotional programs that offer targeted conquesting and loyalty discounts to consumers, which is common in the auto industry but unstudied in the marketing literature.

Model calibration on automobile transaction data from the Los Angeles market confirms that previous purchases made by consumers are predictive of the future purchases of other consumers. Targeted discounts derived from the proposed model for conquesting and loyalty promotional programs substantially increase manufacturer profits. We demonstrate that the extant method of using a linear combination of the individual weight matrices provides an inferior fit and lower incremental profits than the proposed Bayesian structural uncertainty approach to information assimilation.