Sivaramakrishnan Siddarth

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.