|
Article Excerpt
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.
Key words: choice models; nested logit; random coefficients; promotions; automobiles; hierarchical Bayes.
**********
Pricing is a critical component in the marketing-mix plans of automobile manufacturers. Because these companies tend to keep their manufacturer's suggested retail prices (MSRPs) and wholesale prices fixed throughout the year, they customize pricing to reflect supply and demand by using incentives, which, in the US market, represent approximately $45 billion per year. Several conditions make price-customization decisions critical in this industry:
(1) Variations in capacity utilization have immediate and substantial effects on profitability (The Economist 2004). In addition to the high fixed costs of plant and equipment, union contracts place severe restrictions on plant closures or shift reductions. Idle unionized workers must be paid approximately 95 percent of their wages.
(2) Legacy costs (e.g., retirement benefits) constrain management's ability to reduce output.
(3) The growth of non-US auto manufacturers (e.g., Toyota and Honda) has increased industry capacity.
(4) The long new-car design and production cycle (typically more than five years) and a heavily regulated distribution system limit the capability to respond to weakening demand.
(5) The numerous tools available to customize auto pricing (e.g., cash incentives to consumers or dealers, reduced interest-rate financing, and reduced lease rates) and their respective elasticities make the task of identifying effective and efficient pricing programs daunting.
Healthy profits and cash flows depend on bringing to market cars and trucks that consumers want--at prices that return reasonable margins (Pauwels et al. 2004). However, a marketing executive who is facing a softening demand cannot wait five years for a new product line and must develop pricing or promotion programs to keep sales volume and capacity utilization at profitable levels. That executive must determine a mix of incentives for many products and regional markets, and, in addition, must evaluate the conflicting information provided by regional managers, each pushing for a greater share of the promotional budget.
Marketing research organizations, e.g., IRI and Nielsen, have implemented models to assist consumer package goods firms in making price-promotion decisions (Abraham and Lodish 1987, 1993; Wittink et al. 1988; Sinha et al. 2005). Silva-Risso et al. (1999) developed a simulated annealing procedure to extend those models to the optimization of promotion calendars. Researchers have analyzed and discussed these models and their implementations (Bucklin and Gupta 1999, Leeflang and Wittink 2000, Hanssens et al. 2005). In contrast, relatively little work has characterized price and promotion responsiveness in durable goods markets, particularly automobiles. Colombo and Morrison (1989) use a switching matrix to understand cross-competitive effects in the automobile market; however, their analysis does not include any elements of the marketing mix. Thompson and Noordewier (1992) include dummy variables in a time-series model to estimate the effects of incentive programs. Unfortunately, their specification does not allow decision makers to plan future promotions, derive insights on how characteristics of each promotion program have driven the results, or understand the effects of competition. More recently, Berry et al. (1995, 2004), Sudhir (2001), and Train and Winston (2007) quantify consumer response to automobile pricing, but they limit their analyses to MSRPs and do not address the multiple instruments that are used for price customization. Bruce et al. (2006) examine the logic of offering consumer rebates in a context in which consumers face a constraint in their "ability-to-pay" for a durable product (automobiles) considering the second-hand market as an alternative.
This paper describes a model and decision-support system that the Marketing Science Group of the Power Information Network division of J. D. Power and Associates (JDPA) developed to provide automobile manufacturers with a tool to improve the effectiveness and efficiency of their pricing decisions, and Chrysler's pioneering implementation of this tool. This research led to several findings that, to the best of our knowledge, had not been previously documented:
(1) Automobile consumers, in addition to being heterogeneous in their brand preferences, are also heterogeneous in their preferences for transaction types, e.g., acquisition types (purchasing or leasing), promotion types (e.g., cash discount or reduced interest rates), and financing terms.
(2) Consumers are heterogeneous in their relative sensitivity to various pricing instruments--not only on their overall price sensitivity. Some consumers are more responsive to a cash discount, others to a reduced interest rate. Hence, price discounts of the same magnitude may lead to different effects, based on the instruments used and the idiosyncratic price sensitivities of the target consumers.
(3) When a manufacturer must offer a blanket pricing program to a...
|
