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.
- B2B market
- Market segmentation
- Multi-objective market segmentation
- Pareto optimal solution
ASJC Scopus subject areas
- Computer Science Applications
- Artificial Intelligence