A purchase-based market segmentation methodology

Abstract

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.