Customer Churn Modeling via the Grey Wolf Optimizer and Ensemble Neural Networks - Maryam Rahmaty; Amir Daneshvar; Fariba Salahi; Maryam Ebrahimi; Adel Pourghader Chobar (Discrete Dynamics in Nature and Society)
The customer churn is one of the key challenges for enterprises, and market saturation and increased competition to maintain business position has caused companies to make all attempts to identify customers who are likely to leave and end their relationship with a company in a particular period to become the customer of another company. In recent years, many methods have been developed including data mining for predicting the customer churn and manners that customers are likely to behave in the future and therefore, taking action early to prevent their leaving. This study proposes a hybrid system based on fuzzy entropy criterion selection algorithm with similar classifiers, grey wolf optimization algorithm, and artificial neural network to predict the customer churn of those companies that suffer losses from losing customers over time. The research results are evaluated by other methods in the criteria of accuracy, recall, precision, and F_measure, and it is declared that the proposed method is superior over other methods.