Customer Churn Prediction Using Local Linear ModelTree for Iranian Telecommunication Companies

For winning in global competition, companies need to recognition and monitoring ofcustomerʹs behavior to forecast their behavior and desires earlier than competitors. Thisresearch tries to recognize the attributes which lead to customer churn. For this, behavior of3150 subscribers of an Iranian mobile operator, has observed during one year and trends ofthem has analyzed by a customized LLNF model. For this purpose, the application of thelocally linear model tree (LOLIMOT) algorithm, which integrates the advantage of neuralnetworks, tree model and fuzzy modeling, was experimented.Results suggest that dissatisfaction of customer, his/her usage from services and demographicattributes have significant effect on decision to churn or retention. Furthermore, the active orinactive subscriber situation has mediation effect on his/her retention