Customer retention is very important issue. In this reasons, many studies have been conducted for customer retention to demonstrate the potential of data mining through experiments and case studies. But current customer retention procedures only focus on detection of potential defectors based on likelihood of defection overlooking cost. In this paper, we propose an economic procedure for profit...

Online Analytical Mining (OLAM) is an architecture integrating data mining into OLAP. With this integration, data mining algorithms can be performed with OLAP abilities. OLAM enables users to choose a particular portion of data and analyze them with data mining models. Previous studies have provided examples of OLAM applications with the motivation to improve technical performance. This chapter...

Online Analytical Mining (OLAM) is an architecture integrating data mining into OLAP. With this integration, data mining algorithms can be performed with OLAP abilities. OLAM enables users to choose a particular portion of data and analyze them with data mining models. Previous studies have provided examples of OLAM applications with the motivation to improve technical performance. This chapter...

A holistic approach, in the form of a process, is proposed in order to discover marketing intelligence from Internet data. The Internet-enabled knowledge discovery process contains the steps human resource identification, problem specification, data prospecting, domain knowledge elicitation, methodology identification, data pre-processing, pattern discovery, and knowledge post-processing. It al...

This report examines the problems of customer relationship management (CRM) particularly customer segmentation and customer profiling, and how data mining tools are used to support the decision making. We first describe the steps towards predicting customer’s behavior, such as collecting and preparing data, segmentation and profile modeling. Then, we present a general overview of most used data...

The key question of this study is: How long should the length of customer event history be for customer churn prediction? While most studies in predictive churn modeling aim to improve models by data augmentation or algorithm improvement, this study focuses on a another dimension: time window optimization with respect to predictive performance. This paper first presents a formalization of the t...

This study clusters customers and finds the characteristics of different groups in a life insurance company in order to find a way for prediction of customer behavior based on payment. The approach is to use clustering and association rules based on CRISP-DM methodology in data mining. The researcher could classify customers of each policy in three different clusters, using association rules. A...

In order to compete for profitable customers, companies are looking to add value using Customer Relationship Management (CRM). One subset of CRM is customer segmentation, which is the process of dividing customers into groups based upon common features or needs. Segmentation methods can be used for customer portfolio analysis (CPA), the process of analyzing the profitability of customers. This ...

Due to the proliferation of information systems and technology, businesses increasingly have the capability to accumulate huge amounts of customer data in large databases. However, much of the useful marketing insights into customer characteristics and their purchase patterns are largely hidden and untapped. Current emphasis on customer relationship management makes the marketing function an id...

Retaining customers is one of the most critical challenges in the maturing mobile telecommunications service industry. Using customer transaction and billing data, this study investigates determinants of customer churn in the Korean mobile telecommunications service market. Results indicate that call quality-related factors influence customer churn; however, customers participating in membershi...