Enhancing customer relationship management using data mining techniques

• Main Author: Lim, Justin Yimin.
• Other Authors: Lee Ka Man, Carman
• Format: Final Year Project
• Language: English
• Published: 2009
• Subjects: DRNTU::Engineering::Systems engineering
• Online Access: http://hdl.handle.net/10356/16792
• Institution: Nanyang Technological University

Customer Relationship Management has been one of the active areas for data mining applications. The purpose of CRM is to create, maintain and expand customer relationships. The CRM cycle can be broken down into four phases, Customer Identification, Customer Attraction, Customer Retention and Customer Development. In this report, our focus is on the Customer Development phase, which is the maximization of the profitability and value of the customer population. There are two important aspects in the Customer Development phase, namely the customer and the product. A framework for the use of data mining in enhancing the Customer Development phase was proposed. In addition, a decision framework was constructed to aid with the data mining decisions. Data mining techniques such as Decision Trees, Naïve Bayes, Association Rules and Clustering were then applied through Target Customer Analysis and Market Basket Analysis. The results were analyzed and discussed. Lastly, problems that could arise from the use of Association Rules for Market Basket Analysis during the development of the price promotion strategy were discussed and solutions proposed.