AHP Based Data Mining for Customer Segmentation Based on Customer Lifetime Value

Data mining techniques are widely used in various areas of marketing management for extracting useful information. Particularly in a business-to-customer B2C setting, it plays an important role in customer segmentation. A retailer not only tries to improve its relationship with its customers, but also enhances its business in a manufacturer-retailer-consumer chain with respect to this information. Although there are various approaches for customer segmentation, we have used an analytic hierarchical process based data mining technique in this regard. Customers are segmented into six clusters based on Davis-Bouldin DB index and K-Means algorithm. Customer lifetime value CLV along four dimensions, viz., Length L, Recency R, Frequency F and Monetary value M are considered for these clusters. Then, we apply Saaty’s analytical hierarchical process AHP to determine the weights of these criteria, which in turn, helps in computing the CLV value for each of the clusters and their