A purchase-based market segmentation methodology

A novel market segmentation methodology based on product specific variables such as purchased items and the associative monetary expenses from the transactional history of customers to resolve problems of traditional segmentation.

Abstract

Market segmentation is critical for a good marketing and customer relationship management program. Traditionally, a marketer segments a market using general variables such as customer demographics and lifestyle. However, several problems have been identified and make the segmentation result unreliable. This paper develops a novel market segmentation methodology based on product specific variables such as purchased items and the associative monetary expenses from the transactional history of customers to resolve these problems. A purchase-based similarity measure, clustering algorithm, and clustering quality function are defined in this paper. A genetic algorithm approach is adopted to ensure that customers in the same cluster have the closest purchase patterns. After completing segmentation, a designated RFM model is used to analyze the relative profitability of each customer cluster. The findings from a practical marketing implementation study will also be discussed.