Redefining Customer Lifetime Value and Churn Analysis for Insurers

Reframing how you measure lifetime value and reduce churn using augmented data discovery

Insurers face a stiff competitive landscape today as companies move online and there are seemingly endless choices for consumers. The emergence of smaller, agile providers has disrupted business as usual and means that companies must understand exactly who their target customers are, and how to keep them from jumping ship as soon as they hit a snag in customer experience.
The problem

An up-and-coming online insurer has been expanding their user base rapidly in their first two years of operations, but they have a churn problem. Customers are not providing the value they’re estimated to have, and the cost of maintaining existing policyholders has become a serious drag on revenues. The company’s internal models consistently rank a specific region higher than the rest based on historic internal data, but predictions haven’t panned out accordingly, leading to misallocated budgets and unnecessary spending.

The solution

Our customer used Explorium’s Enrichment Catalog to expand its context for each customer and their likelihood to spend more. They used Explorium to retrain their existing machine learning models and were able to build new features to better predict customer lifetime value (CLV) and churn, including: 

  • Payment history compared to socioeconomic and income status
  • Social activity including likes, comments, and reviews of similar products 
  • Income and number of claims made
  • Claims history compared to income and financial stability history 
  • Cost of maintenance compared to revenue and social trends
The results:

Lower churn and more revenue per customer

Using Explorium, the company was able to redefine their CLV calculation by providing greater context around each customer’s value and improve their ranking model to prioritize those policyholders that actually offer the greatest CLV. The company was able to boost their revenues per customer by 13%. Moreover, our customer was able to reduce their churn by nearly 10%, cutting down the number of clients lost every month. In an industry where acquiring new customers is significantly costlier than keeping existing ones, the company managed to cut down on their marketing costs in a big way.

Reduce customer churn and predict customer lifetime value with better data. Schedule a call and see the impact today.

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