A hybrid model of data mining and MCDM methods for estimating customer lifetime value

Due to competitive environment, companies want to create a long-term relationship with their customers throughout customer relationship management (CRM). Building effective customer relationship management, companies should estimate customer lifetime value (CLV). CLV is normally calculated in terms of recency, frequency and monetary (RFM) variables. In this paper, a model for estimating CLV based on RFM variables integrated with data mining and multi criteria decision making (MCDM) methods has been proposed. The proposed methodology contains three phases in which Fuzzy Analytical Hierarchy Process (FAHP) has been used to determine RFM variables' weights. Then, Kmeans clustering method was employed in order to customer clustering and segmentation. Customer clusters were then ranked using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Finally, the proficiency of the model was shown by conducting a case study of cosmetics industry.