Pricing is a critical component in the marketing-mix plans of automobile manufacturers. Because they tend to keep their manufacturer's suggested retail prices (MSRPs) and wholesale prices fixed throughout the model year, they customize pricing to reflect supply and demand by using incentives; in the US market, they represent approximately $45 billion per year. In addition, variations in capacity utilization have immediate and substantial effects on profitability. This, together with legacy costs and inflexible labor contracts, makes the effectiveness and efficiency of price-customization decisions particularly vital for the industry.
Chrysler, a pioneer in using science in its pricing decisions, engaged J. D. Power and Associates (JDPA) to implement an incentive planning model. The approach used is based on a random-effects multinomial nested logit model of product (vehicle model), acquisition (cash, finance, lease), and program-type (e.g., consumer cash rebates, reduced interest-rate financing, cash/reduced interest-rate combinations, lease-support) selection. The model uses sales transaction data that are collected daily from approximately 10,000 dealerships. It uses a hierarchical Bayes modeling structure to capture response heterogeneity at the local market level. This specification allows users to apply the model to pricing decisions at the local, regional, and national market levels.
Based on implementing this model, Chrysler learned that, for any given price level, the pricing structure (e.g., a combination of retail price, interest rates, or rebates) is important. The set of the most efficient pricing structures for each price level constitutes an efficient frontier; efficient pricing structures vary across products, price levels, and markets. The system provides three alternative approaches to identify efficient (and effective) pricing programs: (a) what-if-scenario simulations, (b) a batch scenario generator that allows users to identify and examine the profit-share/volume efficient frontier, and (c) an optimizer that, given an objective and a set of constraints, allows users to search for incentive programs rapidly. The Chrysler Corporate Economics Office estimates that Chrysler's annual savings from implementing the model are approximately $500 million.
