Adaptive Product Optimization and Simultaneous Customer Segmentation: A Hospitality Product Design Study with Genetic Algorithms
Successful product development depends on many factors. Among the most important factors are identification and satisfaction of customers’ perceived needs, the accessibility, size and growth rate of the target market, and, of course, production costs. Since the early 1970s marketing researchers achieved remarkable results in developing methods to measure consumer preferences of multiattributed products. Additionally, market segmentation methods have been an important issue in strategic marketing research. This study, however, concentrates on a new method of product design optimization. It is shown how genetic algorithms are used to simultaneously discover optimal multi-attributed products for different customer preferences. For that purpose we chose an interactive version of the genetic algorithm where genetic operators like selection, mutation and crossover are applied as usual. The use of the interactive genetic algorithm is most suitable, where measures of utility are difficult or impossible to specify mathematically. Imprecise optimization in terms of a priori unknown individual consumer decision rules and preferences is an important issue for marketing researchers. The interactive genetic algorithm tries to solve design problems in that the consumer plays the role of the objective function during data collection.
KeywordsGenetic Algorithm Conjoint Analysis Customer Preference Group Fitness Marketing Researcher
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