A framework for identification of high-value customers by including social network based variables for churn prediction using neuro-fuzzy techniques

• Published in: International journal of production research Vol. 51; no. 4; pp. 1279 - 1294
• Main Authors: Abbasimehr, Hossein, Setak, Mostafa, Soroor, Javad
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis Group 15.02.2013; Taylor & Francis LLC
• Subjects: Algorithms; Artificial neural networks; churn prediction; Clusters; Construction; Customers; data mining; Fuzzy logic; high-value customer; Linear programming; Mathematical models; Neural networks; neuro-fuzzy; Predictions; Studies; Turnover; word-of-mouth
• ISSN: 0020-7543; 1366-588X
• DOI: 10.1080/00207543.2012.707342

Customer churn has become a significant problem and is one of the prime challenges that many in the services industry are facing. While all kinds of churn lead to incur loss, the loss of low-value customers will be naturally less damaging than the loss of loyal and high-value ones. So companies need to build a churn prediction model for their high-value customers. In this paper, a two-phase framework for prediction of high-value customer churn has been proposed. Phase 1 is the identification phase which takes into account social-network based variables of customers in identifying the high-value ones. The data of an identified high-value customer is used as the input for Phase 2 to prepare the churn prediction model. Data of a major telecommunication company has been used to implement the framework. The customers were clustered by using K-means algorithm. After ranking clusters, the top-cluster was selected according to clusters ratings. The data belonging to the top cluster is used in churn prediction model building phase. In this phase, two neuro-fuzzy techniques, namely the adaptive neuro-fuzzy inference system (ANFIS) and the locally linear neuro-fuzzy (LLNF) have been applied together with locally linear model tree (LoLiMoT) learning algorithm on churn data. A new algorithm has been devised for comparing these methods with the most widely used neural networks such as multi layer perceptron (MLP) and radial basis function (RBF) networks. Results of comparison indicate that the neuro-fuzzy techniques perform better than neural network models and they are a good candidate for churn prediction purposes.