Case Study

The Challenge

Building the Churn Prediction Model: AI Framework and Pilot

Pilot Implementation and Follow-Through

Outcomes and Future Benefits

Case Study

Customer retention is a critical challenge for business growth, and data science serves as a powerful tool to predict cancellations and non-renewals through churn patterns. By using a churn prediction algorithm, companies can proactively reduce cancellation rates and strengthen customer loyalty. Here’s how a leading insurance company leveraged this approach to enhance its customer retention strategy with the help of Programmers.

The Challenge

A leading insurance company aimed to predict potential cancellations and non-renewals from its policyholders. Their goal was to identify at-risk customers early on, enabling them to take preventive actions to retain these clients. They turned to Programmers for assistance, and we proposed a churn algorithm pilot to assess the feasibility of a data science project for this purpose.

Building the Churn Prediction Model: AI Framework and Pilot

With expertise not only in advanced data analysis and engineering but also in strategic AI implementation, Programmers introduced its AI Framework—a proven methodology that enables organizations to adopt AI at scale effectively. After an initial discovery session guided by our proprietary AI Canvas, we initiated a pilot phase to test the model’s potential business value.

This pilot enabled the insurance company to assess the feasibility and operational impact of the churn prediction solution. During this phase, we developed a model using Databricks and drew data from Azure, leveraging variables such as customer demographics, payment history, and policy renewal frequency. Integrating this data with the XGBoost algorithm allowed us to estimate the churn probability of each customer, outputting the results in a CSV format for actionable insights. The AI Framework made accurate predictions, setting a clear path for scaling the model with higher precision over time as additional data is fed into the system.

Pilot Implementation and Follow-Through

Integrating an AI solution into existing workflows is complex, so we conducted a pilot to show potential business impact. This phase involved model training, testing, and fine-tuning, giving the client clear insights into the solution’s capabilities and the confidence to scale. Following evaluation, the insurance company moved forward with a tailored strategy for full deployment.

Outcomes and Future Benefits

With this approach, the insurance company was able to achieve the following:

• Reduced cancellation rates: By predicting at-risk customers, retention teams can implement timely and effective interventions.
• Increased customer loyalty: A proactive approach nurtures long-term relationships, creating a more engaged and loyal customer base.

Through predictive analytics and AI, the insurance company successfully tackled customer retention challenges. By piloting a targeted solution, they gained a strategic advantage in preventing customer churn.

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