A comparative framework for Churn analysis in banking and telecom sector

Churn analysis is critical for businesses in the banking and telecom sectors, as it helps them understand customer behavior, identify the reasons behind customer attrition, and develop strategies to retain customers. By analyzing customer behavior, companies can identify patterns and trends that may indicate customers’ intentions to leave, such as a decrease in usage, missed payments, or a change in spending patterns. It helps banks develop targeted marketing campaigns and loyalty programs to retain customers. Also helps telecom companies identify the reasons behind customer attrition, such as poor network coverage or inadequate customer service. Companies use this information to improve their services and retain customers by offering incentives, such as discounts or free upgrades. Machine learning algorithms segment customers based on their behavior patterns, demographics, and preferences. This segmentation helps these sectors target specific customer groups with personalized retention strategies, marketing campaigns, and product offerings. Therefore, in this paper, we have compared churn analysis using ML algorithms with graphs and results to analyze the future difficulties of banking and telecom sectors.