Insurance Conversion Optimization with Machine Learning
This case study highlights how CloudFountain helped a digital insurance platform overcome low conversion rates and inefficient lead spending using machine learning.
The company faced a major challenge: less than 2% of visitors were converting into approved policies, leading to wasted marketing spend and poor ROI. CloudFountain designed and implemented a predictive machine learning model to identify high-intent applicants early in the funnel.
The solution included feature engineering based on applicant data, model optimization using PR-AUC for better accuracy in rare-event prediction, cost-based decision thresholds, and lead source attribution analysis. The system enabled real-time decision-making and automated lead qualification.
As shown in the results (page 6), conversion rates improved to 4.5%, lead costs decreased by 36%, and decision-making time was reduced to under 1 second . The solution also delivered a 3x improvement in lead source ROI, helping the company allocate marketing budgets more effectively.