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European Journal of Operational Research 146 (2003) 621–633 www.elsevier.com/locate/dsw Interfaces with Other Disciplines

Optimal new product positioning:
A genetic algorithm approach
Thomas S. Gruca a a,1

, Bruce R. Klemz

b,*

Department of Marketing, University of Iowa, W376 Pappajohn Business Building, Iowa City, IA 52242-1000, USA b Department of Marketing, Winona State University, 101 Somsen, Winona, MN 55987, USA
Received 23 March 1999; accepted 24 October 2001

Abstract
Identifying an optimal positioning strategy for new products is a critical and difficult strategic decision. In this research, we develop a genetic algorithm based procedure called GA SEARCH that identifies optimal new product positions. In two simulation comparisons and an empirical study, we compare the results from GA SEARCH to those obtained from the best currently available algorithm (PRODSRCH). We find that GA SEARCH performs better regardless of the number of ideal points, existing products, number of attributes or choice set size. Furthermore, GA
SEARCH can account for choice set size heterogeneity. Results show that GA SEARCH outperformed the best current algorithm when choice set size varied at the individual level, an important source of consumer heterogeneity that has been ignored in current algorithms formulated to solve this optimization problem.
Ó 2003 Elsevier Science B.V. All rights reserved.
Keywords: Genetic algorithms; Marketing; Product positioning

1. Introduction
For the brand manager, optimizing a new productÕs positioning is a critical and difficult decision. Addressing this issue, Shocker and Srinivasan (1979) developed a framework for identifying optimal new product concepts using joint space models of consumer perceptions and preferences. Joint space analysis entails mapping the
*

Corresponding author. Tel.: [phone]; fax: [phone].
E-mail addresses: [e-mail] (T.S. Gruca),

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