The availability of abundant data posts a challenge to integrate static customer data and longitudinal behavioral data to improve performance in customer churn prediction. Usually, longitudinal behavioral data are transformed into static data before being included in a prediction model. In this study, a framework with ensemble techniques is presented for customer churn prediction directly using...

customer is the axis of organizations' activities and the mission of all modern organizations is based on the customer. quality as a concern and concept of management is defined in relation to customer satisfaction. regarding the common purpose of customer knowledge management and quality function development technique, this research is to evaluate automobile industry emphasizing the types of c...

With the onset of social media and news aggregators on the Web, the newspaper industry is faced with a declining subscriber base. In order to retain customers both on-line and in print, it is therefore critical to predict and mitigate customer churn. Newspapers typically have heterogeneous sources of valuable data: circulation data, customer subscription information, news content, and search cl...

Association rule mining (ARM) is the process of generating rules based on the correlation between the set of items that the customers purchase.Of late, data mining researchers have improved upon the quality of association rule mining for business development by incorporating factors like value (utility), quantity of items sold (weight) and profit. The rules mined without considering utility val...

Today ,Banks to survive and grow it becomes critical to manage customers, build and maintain a healthy relationship with customers. Data Mining in Banks can play a significant role for customer relationship Management. The areas in which Data mining Tools can be used in the banking industry are customer segmentation, Banking profitability, credit scoring and approval, Predicting payment from Cu...