Academic journal article Journal of Theoretical and Applied Electronic Commerce Research

Customer Behavior Analysis Using Rough Set Approach

Academic journal article Journal of Theoretical and Applied Electronic Commerce Research

Customer Behavior Analysis Using Rough Set Approach

Article excerpt

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 custom- er 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.

Keywords: Clustering, Customer relationship management, K-means, LEM2, Rough set theory, Rule induction, RFM, RFMP

(ProQuest: ... denotes formulae omitted.)

1 Introduction

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]. It helps industries to gain insight into the behavior of customers and their value so that the enterprise can increase their profit by acting according to the customer characteristics. It is classified into operational and analytical. Operational CRM refers to the automation of business processes whereas analytical CRM refers to the analysis of customer characteristics and behaviors. Analytical CRM helps the entrepreneur to discriminate their customers and decide their marketing activi- ties accordingly [30], It consists of four ideologies namely customer identification, customer attraction, customer re- tention and customer development. Customer identification is the process in which the customers are grouped and their characteristics are analyzed. Customer attraction is the process in which the customers buy for the next time by providing customer service, coupon distribution, direct mailing and discounts. Customer retention is the process in which the customer's needs are satisfied by introducing new products and rectifying their complaints. Customer de- velopment involves in expansion of transaction intensity, transaction value and individual customer profitability. Cus- tomer identification is the most important phase in analytical CRM because once the customer is identified correctly; he can be retained and developed further. The customer identification phase consists of customer segmentation and target customer analysis. Customer segmentation involves in segmenting customers into predefined number of cus- tomer groups. Target customer analysis involves in analyzing customer behavior or characteristics in each customer group. It helps the entrepreneur to vary the attraction process for existing customers and to predict new customer's behaviors [30], Data mining techniques are good at extracting and identifying useful information and knowledge from enormous customer databases, and for making different CRM decisions. The application of data mining techniques in CRM is an emerging trend in the global economy [2],

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]. …

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