PREDICTIVE ANALYTICS FOR CUSTOMER CHURN MANAGEMENT: A DATA-DRIVEN APPROACH TO MODELING AND ENHANCING RETENTION STRATEGIES IN SUBSCRIPTION-BASED SERVICES
Keywords:
Customer Churn, Predictive Analytics, Machine Learning, Customer Retention, Subscription-Based ServicesAbstract
The issue of customer churn is severe with companies that are in the business of a subscription-based service, where customer retention plays a vital role in ensuring profitability and sustainability. This research focus on developing a predictive analytics model that can be applied in predicting customer churn and the key variables that influence customer loss. The study uses machine learning techniques on a structured dataset with approximately 10,000 customer records, to analyze the demographic, behavioral and financial customer churn related data. The findings show that the type of contract, tenure and monthly charges are the most significant factors that predict churn behavior. The gradient boosting model and other ensemble models have demonstrated that they are more predictive than the traditional models and the sophisticated tools of analysis are worthwhile in the determination of the complex trends that characterize customers. The results also indicate that the churn rate of short tenured customers and customers with flexible contracts is high, and early engagement and long term commitment should be the strategies to focus on. The article is highly educative to organisations that want to ensure that the number of customers they retain increases as they make decisions that are data-driven. With predictive modeling and strategic interventions, organizations can find out the high-risk customers early and implement retention measures to them.
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