Churn Prediction Model Based on Logistic Regression in the Telecommunications Industry: Big Data Analysis

The telecommunications industry faces fierce competition and rapid customer growth, but also a pressing challenge: customer churn. This phenomenon, where customers switch providers or stop using services, severely impacts revenue and profitability. To succeed in this environment, telecom companies focus on customer acquisition, sales growth, and retention, all tied to churn behavior influenced by factors like tenure, usage, age, and charges. Customer retention gains importance as acquiring new customers proves costly. The industry employs data analysis through churn prediction systems, using data mining for behavior forecasts. High churn rates, averaging 8-12% necessitate effective strategies. This study explores churn behavior and employs logistic regression as a predictive tool, offering insights for enhanced customer retention strategies. It aids telecom companies in understanding and retaining customers, ultimately elevating industry competitiveness.