A Novel FCM and DT based Segmentation and Profiling Approach for Customer Relationship Management

Abstract

In the current era of e-businesses, customer relationship management is a decisive process for selecting profitable customers and enhancing customer relationships for the betterment of the organization. However, most customer-oriented organizations face a common problem of categorizing customers, understanding the difference between them, and extracting profitable customers. In this paper, we present an approach to address these issues identifying the future and current values of customers. This helps in identifying and retaining the customers that a firm can most profitably serve. In our proposed system, we purify data through pre-processing and data cleaning, and then three key parameters i.e. recency, frequency, and monetary (RFM) are extracted from data. A Analytical Hierarchical Process is then applied to calculate the weights of RFM. These weighted RFM parameters are used for categorization of customers with the help of a fuzzy-c-mean algorithm. The validity of clusters is checked with Davies-Bouldin Index and finally, classification is done using decision tree and recommendation is given to enhance customer relationships. We evaluated the performance of our proposed system on two publicly available KDD Cup and Instacart datasets and achieved an accuracy rate of 95.5% and 94.3% respectively. The proposed system can be utilized for enhancing marketing strategies and developing new services for valuable customers.