Overview
Enterprises require timely and dependable visibility into policy renewals to reduce churn and protect retention. However, manual renewal analysis consumes weeks of effort and delays insight delivery, limiting proactive responses to churn risk. To address this, ML-powered Policy Renewal Predictive Analytics converts 18+ customer signals into forward-looking renewal risk insights, enabling 60-90 days of advance intervention and more reliable renewal forecasting. The solution delivers upto 87% prediction accuracy and helps reduce churn by upto 25%.
Timeline:
0:07: Introduction
0:20: Business Challenges...
1:04: Predictive Analytics ...
Enterprises require timely and dependable visibility into policy renewals to reduce churn and protect retention. However, manual renewal analysis consumes weeks of effort and delays insight delivery, limiting proactive responses to churn risk. To address this, ML-powered Policy Renewal Predictive Analytics converts 18+ customer signals into forward-looking renewal risk insights, enabling 60-90 days of advance intervention and more reliable renewal forecasting. The solution delivers upto 87% prediction accuracy and helps reduce churn by upto 25%.
Timeline:
0:07: Introduction
0:20: Business Challenges
1:04: Predictive Analytics Cycle via AI
1:39: Unlocking Business Value through ML Prediction
2:14 : Demo: Policy Renewals Predictive Analytics Using ML
2:47: Key Challenges & ML Statistics
3:19: Turning Customer Data into Predictive Action
5:46: ML-Powered Policy Renewal Prediction Dashboard
5:54: Summary
6:23: Thank you
See how ML-driven predictive models turn renewal data into measurable retention impact. Explore more at www.miraclesoft.com/aiforbusiness