Augmenting Conjoint Analysis to Estimate Consumer Reservation Price

Consumer reservation price is a key concept in marketing and economics. Theoretically, this concept has been instrumental in studying consumer purchase decisions,competitive pricing strategies,and welfare economics. Managerially,knowledge of consumer reservation prices is critical for implementing many pricing tactics such as bundling,tar get promotions,nonlinear pricing,and one-to-one pricing,and for assessing the impact of marketing strategy on demand. Despite the practical and theoretical importance of this concept, its measurement at the individual level in a practical setting proves elusive.We propose a conjoint-based approach to estimate consumer-level reservation prices. This approach integrates the preference estimation of traditional conjoint with the economic theory of consumer choice. This integration augments the capability of traditional conjoint such that consumers' reservation prices for a product can be derived directly from the individuallevel estimates of conjoint coefficients. With this augmentation,we can model a consumer's decision of not only which product to buy,but also whether to buy at all in a category. Thus, we can simulate simultaneously three effects that a change in price or the introduction of a new product may generate in a market: the customer switching effect,the cannibalization effect,and the market expansion effect. We show in a pilot application how this approach can aid product and pricing decisions. We also demonstrate the predictive validity of our approach using data from a commercial study of automobile batteries.

[1]  K. B. Monroe Pricing: Making Profitable Decisions , 1990 .

[2]  Paul E. Green,et al.  Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice , 1990 .

[3]  Naresh K. Malhotra,et al.  An Approach to the Measurement of Consumer Preferences Using Limited Information , 1986 .

[4]  Shlomo Kalish,et al.  A comparison of ranking, rating and reservation price measurement in conjoint analysis , 1991 .

[5]  Kamel Jedidi,et al.  Measuring Heterogeneous Reservation Prices for Product Bundles , 2003 .

[6]  Charlotte H. Mason New Product Entries and Product Class Demand , 1990 .

[7]  Pradeep K. Chintagunta,et al.  Customer Value Assessment in Business Markets: A State-of-Practice Study , 1992 .

[8]  J. Scott Armstrong,et al.  Principles of forecasting : a handbook for researchers and practitioners , 2001 .

[9]  Rajeev Kohli,et al.  Consideration Sets in Conjoint Analysis , 1996 .

[10]  S. Postle,et al.  Product Innovation Management , 1990 .

[11]  S. Addelman Orthogonal Main-Effect Plans for Asymmetrical Factorial Experiments , 1962 .

[12]  Vijay Mahajan,et al.  A Conjoint Model for Measuring Self- and Cross-Price/Demand Relationships , 1982 .

[13]  V. Mahajan,et al.  A Reservation-Price Model for Optimal Pricing of Multiattribute Products in Conjoint Analysis , 1991 .

[14]  A. Page,et al.  Redesigning product lines with conjoint analysis: How sunbeam does it , 1987 .

[15]  Dick R. Wittink,et al.  Forecasting with Conjoint Analysis , 2001 .

[16]  Paul E. Green,et al.  Conjoint Analysis in Marketing Research: New Developments and Directions , 1990 .

[17]  Philippe Cattin,et al.  Commercial Use of Conjoint Analysis: An Update , 1989 .

[18]  Steven H. Cohen,et al.  Market segmentation with choice-based conjoint analysis , 1995 .

[19]  Z. John Zhang,et al.  Competitive Coupon Targeting , 1995 .

[20]  D. Aaker,et al.  Chapter 2 – Marketing research , 2004 .

[21]  Jordan J. Louviere,et al.  Design and Analysis of Simulated Consumer Choice or Allocation Experiments: An Approach Based on Aggregate Data , 1983 .