Customer Relationship Management (CRM) helps businesses to gain insight into the behavior of customers and their value so that the company can increase their profit by acting according to the customer characteristics. In order to analyze the customer needs and behavior, data mining is used to extract information from the customer database. For analyzing the customer behavior, the important attributes in the customer database are first chosen and then they are segmented into groups using clustering algorithm based on those attribute values. The rules are then generated to describe the customers in each group using LEM2 (Learning from Examples Module, version 2) algorithm and the proposed algorithm. These rules can be used by the business people to predict the behavior and to vary their promotional activities like coupon distribution and special discounts. It is observed that the proposed algorithm is better in terms of time complexity and performance measures.
Dhandayudam, Prabha and Krishnamurthi, Ilango
"Enhanced Rule Induction Algorithm for Customer Relationship Management,"
Applied Mathematics & Information Sciences: Vol. 07:
4, Article 28.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol07/iss4/28