Purpose – Churn prediction is a very important task for successful customer relationship management. In general, churn prediction can be achieved by many data mining techniques. However, during data mining, dimensionality reduction (or feature selection) and data reduction are the two important data preprocessing steps. In particular, the aims of feature selection and data reduction are to filt...

The Customers are the base of many successful businesses; thus, all sectors starting to understand how important it is gain client satisfaction. technical infrastructure has expanded quickly, changing businesses operate. Due growing business competition, importance marketing techniques, and customers' increasingly aware behaviour in recent years, leaving organization a crucial issue one most wo...

Churn prediction has received much attention in the last decade. With the evolution of social networks and social network analysis tools in recent years, the consideration of social ties in churn prediction has proven promising. One possibility is to use energy diffusion models to model the spread of influence through a social network. This paper proposes a novel churn prediction diffusion mode...

Predictive modeling is a useful tool for identifying customers who are at risk of churn. An appropriate churn prediction model should be both accurate and comprehensible. However, reviewing the past researches in this context shows that much attention is paid to accuracy of churn prediction models than comprehensibility of them. This paper compares three different rule induction techniques from...

Churn prediction is becoming a major focus of banks in China who wish to retain customers by satisfying their needs under resource constraints. In churn prediction, an important yet challenging problem is the imbalance in the data distribution. In this paper, we propose a novel learning method, called improved balanced random forests (IBRF), and demonstrate its application to churn prediction. ...

Companies are investing more in analytics to obtain a competitive edge in the market and decision makers are required better identification among their data to be able to interpret complex patterns more easily. Alluring thousands of new customers is worthless if an equal number is leaving. Business Intelligence (BI) systems are unable to find hidden churn patterns for the huge customer base. In...

In this paper, we propose ADTreesLogit, a model that integrates the advantage of ADTrees model and the logistic regression model, to improve the predictive accuracy and interpretability of existing churn prediction models. We show that the overall predictive accuracy of ADTreesLogit model compares favorably with that of TreeNet®, a model which won the Gold Prize in the 2003 mobile customer chur...

Taiwan deregulated its wireless telecommunication services in 1997. Fierce competition followed, and churn management becomes a major focus of mobile operators to retain subscribers via satisfying their needs under resource constraints. One of the challenges is churner prediction. Through empirical evaluation, this study compares various data mining techniques that can assign a “propensity-to-c...

Internet-connected devices, especially mobile devices such as smartphones, have become widely accessible in the past decade. Interaction with such devices has evolved into frequent and short-duration usage, and this phenomenon has resulted in a pervasive popularity of casual games in the game sector. On the other hand, development of casual games has become easier than ever as a result of the a...