Raghuram Iyengar, Kamel Jedidi, and Rajeev Kohli
Executive Summary
The use of conjoint analysis to study the effect of price changes on market demand dates back to at least the 1970s. Since then, marketing researchers have developed various pricing models using conjoint analysis. Mahajan, Green and Goldberg (1982) describe a method for estimating self- and cross-elasticities using conjoint analysis. Kohli and Mahajan (1991) introduce an approach for optimal pricing using conjoint data. Jedidi and Zhang (2002) further develop this method to allow for the effect of new product introduction on category-level demand. Jedidi, Jagpal, and Manchanda (2003) describe a method for estimating consumer reservation prices for product bundles.
Each method assumes that a product is sold at a single price and that a consumer cannot upgrade or add features to a product by paying an additional fee. Another assumption common to these methods is that consumer usage rates do not depend on price. These assumptions are approximately, if not perfectly, satisfied for some products—for example, durable goods such as washing machines and refrigerators. However, there are also categories of products and services in which one or more of these assumptions is not appropriate. For example, some services charge not one price but two prices and charge additional fees for add-on features. Examples are car rentals, some HMO plans, Xerox copying services, memberships to health clubs, and telephone services. Some of these services charge an additional variable fee. For example, institutional users pay a per-page charge for copies on a Xerox machine, and members of an HMO pay a deductible for each visit to a doctor. Other services, such as cellular phone services and museum and health club memberships, charge a fixed fee and allow “free” use up to a certain level, beyond which consumers must pay a usage-based unit rate. This induces a two-way dependence of price and consumption; namely, the price a provider charges influences consumption, whereas the price a consumer pays depends on his or her usage level. Some services allow customers to purchase optional features, such as rollover minutes for cell phone services and extra life insurance for car rentals and air travel. Other services, such HMOs, do not allow service enhancements but offer alternative plans with bundles of add-on features.
In this article, the authors propose a method using conjoint analysis for multipart pricing. The method reflects the two-way dependence between prices and consumption and incorporates the uncertainty consumers have about their use of a service. The proposed method estimates both choice probabilities and usage levels for each individual as functions of the product features and the different price components. The authors then use these estimates to evaluate the expected revenues and profits of alternative plans and pricing schemes. The method is illustrated using data from a conjoint study of cellular phone services. The authors compare the results with those obtained from using several competing models. They use the proposed procedure to identify the optimal set of features in a base plan and the pricing of optional features for a provider of cellular phone services.
Biography
Raghuram Iyengar is Assistant Professor of Marketing at the Wharton School at the University of Pennsylvania. He has an undergraduate degree in Engineering from I.I.T. Kanpur, India, and a PhD in Marketing from Columbia University. His substantive research interests include pricing, diffusion of innovations, social networks. His methodological interests lie in Bayesian methods. His research has been published in Journal of Marketing Research, Quantitative and Marketing Economics, and Psychometrika.
Kamel Jedidi is Professor of Marketing and Chair of the Marketing Division in the Graduate School of Business at Columbia University. He holds a bachelor’s degree in Economics from the Faculté des Sciences Economiques de Tunis, Tunisia, and a master’s degree and PhD in Marketing from the Wharton School at the University of Pennsylvania. He has published more than 30 articles in leading marketing and statistics journals, the most recent of which have appeared in Journal of Marketing Research, Marketing Science, Management Science, the International Journal of Research in Marketing, and Psychometrika. His substantive research interests include pricing, product design and positioning, diffusion of innovations, market segmentation, and the long-term impact of advertising and promotions. His methodological interests lie in multidimensional scaling, classification, structural equation modeling, and Bayesian and finite-mixture models. He was awarded the 1998 International Journal of Research in Marketing Best-Article Award and the Marketing Science Institute 2000 Best-Paper Award.
Rajeev Kohli is Professor of Marketing in the Marketing Division of the Graduate School of Business, Columbia University. He is interested in the use of mathematical models to study consumer preference and choice and their application to marketing problems in new product design, pricing, and resource allocation. He teaches courses on new product development and marketing management to MBA AND Executive MBA students and a course on mathematical models in marketing to doctoral students. He also teaches courses on new product development and innovation to executives in the United States, Europe, and Asia. His publications have appeared in leading journals in marketing, management science, and mathematics, including Journal of Marketing Research, Marketing Science, Management Science, Psychometrika, Mathematical Programming, SIAM Journal on Discrete Mathematics, and Discrete Applied Mathematics. He has previously served as an associate editor of the journal Management Science and was on the editorial boards of Marketing Letters and Journal of Interactive Marketing.
Journal of Marketing Research, Vol. XLV, No. 2, April 2008
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