Case Study: Online Insurer achieves 13% higher revenue per customer and nearly 10% lower churn with Explorium

Online Insurer - Customer Case Study

Online Insurer, a fast-growing digital insurer, was struggling with higher-than-expected churn and misestimated customer value that led to misallocated budgets—internal models even overvalued a specific region based on limited historic data. To address this, the company engaged vendor Explorium and used Explorium’s Enrichment Catalog to broaden the contextual signals available for each policyholder.

Using Explorium, Online Insurer retrained its machine learning models and engineered new features (e.g., payment and claims history combined with socioeconomic, income and social-activity signals) to more accurately predict customer lifetime value and churn. The improved CLV ranking and targeting lifted revenue per customer by 13%, cut churn by nearly 10%, and materially lowered customer acquisition and marketing costs.