GSM churn management by using fuzzy c-means clustering and adaptive neuro fuzzy inference system

Churn management is important and critical issue for Global Services of Mobile Communications (GSM) operators to develop strategies and tactics to prevent its subscribers to pass other GSM operators. First phase of churn management starts with profile creation for the subscribers. Profiling process evaluates call detail data, financial information, calls to customer service, contract details, market details and geographic and population data of a given state. In this study, input features are clustered by x-means and fuzzy c-means clustering algorithms to put the subscribers into different discrete classes. Adaptive Neuro Fuzzy Inference System (ANFIS) is executed to develop a sensitive prediction model for churn management by using these classes. First prediction step starts with parallel Neuro fuzzy classifiers. After then, FIS takes Neuro fuzzy classifiers’ outputs as input to make a decision about churners’ activities.