Abstract
The customer relationship management (CRM) is a business methodology used to build long term profitable customers by analyzing customer needs and behaviors. The customer behavior is analyzed by choosing important attributes in the customer database. The customers are then segmented into groups according to their attribute values. The rules are generated using rule induction algorithms to describe the customers in each group. These rules can be used by the entrepreneur to predict the behavior of their new customers and to vary the attraction process for existing customers. In this paper a new rule algorithm has been proposed based on the concepts of rough set theory. Its performance has been compared with LEM2 (Learning from Examples Module, version 2) algorithm, an existing rough set based rule induction algorithm. Real data set of the customer transaction is used for analysis. Recency(R), Frequency (F), Monetary (M) and Payment (P) are the attributes chosen for analyzing customer data. The proposed algorithm on average achieves 0.439% increase in sensitivity, 0.007% increase in specificity, 0.151% increase in accuracy, 0.014% increase in positive predictive value, 0.218% increase in negative predictive value and 0.228% increase in F-measure when compared to LEM2 algorithm.
Highlights
IntroductionCustomer relationship management (CRM) technology is a mediator between customer management activities in all stages of a relationship (initiation, maintenance and termination) and business performance [41]
Customer relationship management (CRM) technology is a mediator between customer management activities in all stages of a relationship and business performance [41]
The rest of the paper is organized in the following: In Section 2 we describe the overview of customer relationship management, clustering algorithms, rule induction algorithms and LEM2 algorithm
Summary
Customer relationship management (CRM) technology is a mediator between customer management activities in all stages of a relationship (initiation, maintenance and termination) and business performance [41]. Data mining is a collection of techniques for efficient automated discovery of previously unknown, valid, novel, useful and understandable patterns in large databases These patterns are used in an enterprise's decision making process [19]. Each data in the cluster should be described by at least one rule in the rule set of that cluster This property of rule induction algorithm is called completeness. Rule set for a cluster should cover all the data within that cluster and no rule should be satisfied by any data in other clusters This property of rule induction algorithm is called consistency. Clustering and rule induction of data mining technique is used for customer segmentation and target customer analysis of customer identification phase in CRM. An improved rule induction algorithm based on rough set theory has been developed to generate rules for clustered customer’s data.
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