Optimal new product positioning: A genetic algorithm approach

Identifying an optimal position 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.