Gaining insight to B2B relationships through new segmentation approaches: Not all relationships are equal

Abstract

B2B market segmentation has both structure complexity and computation complexity. The existing market segmentation methods can not directly address these two challenges simultaneously and provide a comprehensive view of the whole problem. Nor are they able to provide guidance on the selection of the most suitable solution among candidates. This study formulates the B2B segmentation as a multi-dimensional optimization problem that integrates both customer behavior and marketing effectiveness. It applies an integrated segmentation method that unifies two market segmentation approaches: the Embedded Exchange Approach (a descriptive model) and the Predictive Satisfaction Approach (a predictive model). It proposes the use of an evolutionary based, multi-objective segmentation method to solve the structural and computational challenges. The method generates a set of Pareto optimal solutions which not only gives a holistic view of possible solutions in the Pareto optimal space but also allows marketers to use solution selection algorithm based on the properties of Pareto optimal sets. The study develops a solution selection algorithm that represents a good tradeoff of two objectives based on the geometric shape of the Pareto optimal solution front.