Choosing the Right Baskets for Your Eggs: Deriving Actionable Customer Segments Using Supervised Genetic Algorithms

Abstract

In the context of key driver analysis in applied customer satisfaction research, the assumption of sample homogeneity (that single models perform adequately over the entirety of a survey sample) can be shown to restrict the value of the insights derived. While latent class regression has been used as a method of circumventing some of these issues, it is proposed that there are major barriers to both uptake and successful practical usage of the technique. Several of these issues are common to any multivariate technique, while others are specific to latent class regression. Following an examination of these issues, we introduce an alternative technique for deriving discrete latent classes, using a combination of genetic algorithms and (bivariate) correlations. This paper concludes that the proposed approach outperforms latent class regression in its ability to deliver action-orientated insights, and is better placed to assist marketers facing real-world research questions and datasets.