Gaining insight to B2B relationships through new segmentation approaches: Not all relationships are equal. (15th December 2020)

• Record Type: Journal Article
• Authors: O'Brien, Matthew; Liu, Ying; Chen, Hongyu; Lusch, Robert
• Is Part Of: Expert systems with applications. Volume 161(2020)
• Publication Date: 2020-12-15
• Subjects: Market segmentation -- B2B market -- Multi-objective market segmentation -- Pareto optimal solution; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.113767
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Use both descriptive and predictive models to segment B2B relationship. Generate practical & effective outcomes from multiple segmentation bases. Gain business insights based on a set of Pareto optimal solutions. Develop new methods to choose the best solution from a Pareto optimal set. 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.