Excellence in Data-Driven Decision Making: Enhancing Customer Retention Through Predictive Analytics

Siva Sankar Das developed a predictive analytics solution to make it easier for banks to keep their customers. The integration of advanced data science and real-time CRM systems helps with early management of churn. The system looks at activity from the past 12 months and 2 million customer profiles for analysis. There is a churn-risk scoring model as well as sentiment analysis taken from multiple customer feedback sources. These features let users see how customers behave, making it easier to discover early warning signs of churn. Logistic regression and random forest ensemble are used to build the churn model for its high predictive power. Each week, the churn-risk scores are added to the CRM dashboards so that managers from both teams can access them. Automated risk alerts provide quick action by letting you send personalized campaigns to at-risk customers. It supports sending personalized offers, optimizing onboarding for at-risk users, and taking real-time engagement actions for users at risk. The system helps decision-makers by combining data into easy-to-use insights for those handling customer experience. It automatically coordinates retention actions with business processes and customer attitudes and actions. Implementing immediate outreach is possible through workflow triggers and business rules that quickly improve the business's response to customers. This system makes it easier for IT, marketing, and customer service teams to work together with a single source of data. The platform’s architecture makes it simple to add the tool to more departments and keeps retention models up to date. Tailored services can be provided to customers at key times because the platform improves segmentation. The platform uses strong data governance practices, making certain the analytics are done safely, privately, and legally. The platform automatically updates models and cleans data to make sure customer segment information stays reliable and up-to-date. It lets non-technical users keep an eye on risk ratings and start interventions whenever it's needed. Because of this data-based product, the organization is now a leader in smart customer retention methods. This means workers spend less time fixing things and more time understanding risks, while also offering personalized services.