The Application ofAdaBoost in Customer Churn Prediction

Shao Jinbo

18 citations0/0 files storedInternational Conference on Service Systems and Service Management

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

Abstract

Since attracting new customers is known to be more expensive, the enhancement of existing relationships is of pivotal importance to companies. Therefore, as part of the customer relationship management (CRM) strategy, predicting customer churn and improving customer retention have attracted more and more attention. Being aware of the defection prone customers beforehand, companies could react in time to prevent the churn by offering the right set of products, modifying the sales strategy and providing customized services. Therefore, high predictive performance could ultimately lead to profit increasing for companies. In this paper, we use the AdaBoost which is a main branch of boosting algorithms to predict the customer churn. We have implemented three different boosting schemes: Real AdaBoost, Gentle AdaBoost and Modest AdaBoost. Applied to a credit debt customer database of an anonymous commercial bank in China, they are proven to significantly improve prediction accuracy comparing with other algorithms, like SVM. The assessment and comparison of these algorithms are made to analyze the traits of them. Data processing and sampling scheme are also detailed in this paper.

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References

2002Turning telecommunications call details to churn prediction: a data mining approach 2006Bagging and Boosting Classification Trees to Predict Churn 1996Game theory, on-line prediction and boosting 2003A novel evolutionary data mining algorithm with applications to churn prediction 2005Customer Churn Prediction Using Improved One-Class Support Vector Machine 1999Churn Reduction in the Wireless Industry 1998Genetic modelling of customer retention 2002Data Mining and Causal Modeling of Customer Behaviors

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2010Prediction of Advertiser Churn for Google AdWords 2010Ensembles of Probability Estimation Trees for Customer Churn Prediction

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