Optimum profit-driven churn decision making: innovative artificial neural networks in telecom industry

Knowledge-based churn prediction and decision making is invaluable for telecom companies due to highly competitive markets. The comprehensiveness and action ability of a data-driven churn prediction system depend on the effective extraction of hidden patterns from the data. Generally, data analytics is employed to extrapolate the extracted patterns from the training dataset to the test set. In this study, one more step is taken; the improved prediction performance is attained by capturing the individuality of each customer while discovering the hidden pattern from the train set and then applying all the knowledge to the test set. The proposed churn prediction system is developed using artificial neural networks that take advantage of both self-organizing and error-driven learning approaches (ChP-SOEDNN). We are introducing a new dimension to the study of churn prediction in telecom industry: a systematic and profit-driven churn decision-making framework. The comparison of the ChP-SOEDNN with other techniques shows its superiority regarding both accuracy and misclassification cost. Misclassification cost is a realistic criterion this article introduces to measure the success of a method in finding the best set of decisions that leads to the minimum possible loss of profit. Moreover, ChP-SOEDNN shows capability in devising a cost-efficient retention strategy for each cluster of customers, in addition to strength in dealing with the typical issue of imbalanced class distribution that is common in churn prediction problems.