A recommender system to avoid customer churn: A case study

[[abstract]]A major concern for modern enterprises is to promote customer value, loyalty and contribution through services such as can help establish a long-term, honest relationship with customers. For purposes of better customer relationship management, data mining technology is commonly used to analyze large quantities of data about customer bargains, purchase preferences, customer churn, etc. This paper aims to propose a recommender system for wireless network companies to understand and avoid customer churn. To ensure the accuracy of the analysis, we use the decision tree algorithm to analyze data of over 60,000 transactions and of more than 4000 members, over a period of three months. The data of the first nine weeks is used as the training data, and that of the last month as the testing data. The results of the experiment are found to be very useful for making strategy recommendations to avoid customer churn.[[incitationindex]]SCI[[incitationindex]]EI[[booktype]]紙

Mining Disjunctive Consequent Association Rules

100學年度研究獎補助論文[[abstract]]When associationrules A → B and A → C cannot be discovered from the database, it does not mean that A → B ∨ C will not be an associationrule from the same database. In fact, when A, B or C is the newly marketed product, A → B ∨ C shall be a very useful rule in some cases. Since the consequent item of this kind of rule is formed by a disjunctive composite item, we call this type of rules as the disjunctiveconsequentassociationrules. Therefore, we propose a simple but efficient algorithm to discover this type of rules. Moreover, when we apply our algorithm to insurance policy for cross selling, the useful results have been proven by the insurance company.[[incitationindex]]SCI[[booktype]]紙