Customer Churn Prediction with Machine Learning Methods In Telecommunication Industry

With the emergence of new competitors and increasing investments in telecommunication services, change often occurs and hence importance of marketing strategies and customer behavior prediction have become an important demand for companies. New regulations and technologies increase competition among mobile operators. Since acquiring a new customer is more expensive than acquiring active customers, companies seek solutions to reduce the churn rate. Therefore, telecommunications companies want to analyze the concept of the customer's desire to change service provider and take necessary measures to protect their existing customers. In this study, usage information, usage trends, subscription commitment, subscription age, ARPU and billing information, competitor familiarity, outgoing call information, number porting experience, etc. Loss estimation modeling is taken into account. Dataset includes 593 columns and 1826588 lines. Corporate mobile customers are analyzed by dividing into three subgroups as Single Line Mobile Customers, 2-5 Line Mobile Customers, and 6-15 Line Mobile Customers. In order to estimate customer loss, four different ML methods are used while creating loss prediction models. The model is developed by using 600 different variables and loss estimation. ROC curves and lift chart results for different corporate mobile customer groups are compared and the most suitable models are depicted.