A Stacking-Based Data Mining Solution to Customer Churn Prediction

In today’s competitive world, organizations are in a constant struggle to retain their current customers while attracting new through various methods. Customer churn is major challenge different industries and companies. Despite initial successful attempts at customers, soon face the fact that may turn away toward rivals. By identifying candidates, will be able guarantee future success by revising customer relationship management policy. Analyzing data of telecommunications industries, this study provided an effective early-churn-detection solution using modern techniques stacking mining algorithms. Research findings indicate integrating support vector machines (SVMs) with chi-square automatic interaction detection (CHAID) decision tree can yield best outcome. The results show proper accuracy proposed prediction solution. addition, contributed improved results.