Xerago Partnered with a South Asian Insurance Giant to Retain 3 Million Customers
The Customer Retention Challenge
A prominent insurance company offering a comprehensive range of products, including life, health, motor, travel, and home insurance, faced significant challenges in retaining customers, particularly in the life and health insurance sectors.
High churn rates threatened profitability, and data inconsistencies across various sources made it difficult to predict which customers might lapse or discontinue their policies.
The company recognized the need for a predictive approach to improve retention.
The Customer Retention Challenge
A prominent insurance company offering a comprehensive range of products, including life, health, motor, travel, and home insurance, faced significant challenges in retaining customers, particularly in the life and health insurance sectors.
High churn rates threatened profitability, and data inconsistencies across various sources made it difficult to predict which customers might lapse or discontinue their policies.
The company recognized the need for a predictive approach to improve retention.
The Customer Retention Challenge
A prominent insurance company offering a comprehensive range of products, including life, health, motor, travel, and home insurance, faced significant challenges in retaining customers, particularly in the life and health insurance sectors.
High churn rates threatened profitability, and data inconsistencies across various sources made it difficult to predict which customers might lapse or discontinue their policies.
The company recognized the need for a predictive approach to improve retention.
Key Highlights:
- Predictive modeling helped identify high-risk customers and engagement opportunities, allowing proactive retention strategies
- The solution involved addressing data inconsistencies and building a persistency model for effective decision-making
Challenge
- Identifying data sources across different systems.
- Resolving data inconsistencies that hampered insights.
- Developing models to predict customer behavior and lapses.
- Resolve inconsistencies in data across multiple sources to create a unified and accurate dataset
- Build a persistency model to predict customer lapses and enhance retention strategies
- Identify critical engagement opportunities to prevent lapses and improve customer satisfaction
- Streamline operations by enabling the insurance provider to focus on data-driven retention strategies
Solution
Xerago deployed IBM Predictive Customer Intelligence to build a persistency model that enhanced customer retention efforts. Key steps included:
- Utilizing SPSS Modeler to perform predictive analytics on historical data.
- Resolving data inconsistencies and generating actionable insights into customer behavior.
- Identifying key engagement opportunities to prevent lapses.
Xerago tackled the issue by auditing and standardizing the client’s customer data, creating a clean, reliable foundation for analysis.
Using **IBM Predictive Customer Intelligence and SPSS Modeler**, we developed a model that predicted which customers were most at risk of leaving. The model helped spot key behavioral patterns and high-risk groups.
With predictive insights, Xerago pinpointed moments in a customer’s journey where intervention could prevent churn. Personalized strategies like timely communication or incentives were recommended.
We provided clear steps for engaging at-risk customers, tailoring campaigns to keep them onboard and enhance satisfaction.
- **Derived Target Variable:** "On-Time & Unpaid" based on customer payment behaviors.
- **Data Split:** Training data (2008-2014) and testing data (2015) for model validation.
- **Algorithm Selection:** Tested CHAID, C5, and Neural Networks; finalized CHAID decision tree for best performance.
- **Additional Models:** Developed models for 'Time of Payment' and 'Payment Mode' combinations (e.g., 13th month, yearly payments).
- **Comprehensive Solution:** Enhanced insights into customer behavior for improving retention and identifying high-risk groups.
- **Expansion:** Built Early Claims and Cross-Sell models for broader customer engagement strategies.
The Outcome
By addressing data inconsistencies and applying advanced classification techniques, Xerago's predictive analytics solution improved customer retention rates and operational efficiency for the insurance provider.
Key benefits included:
Accurate and predictive customer data at the policyholder level, enabling the insurance provider to personalize engagement strategies and improve customer retention.
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