Optimizing Direct Marketing For Burial Insurance With Machine Learning
DOI:
https://doi.org/10.63282/3050-9246/ICRTCSIT-110Keywords:
Predictive/Propensity modeling, Machine Learning in Marketing, Insurance Marketing, Customer Segmentation, Burial InsuranceAbstract
Burial insurance, a niche but vital segment of life insurance, presents persistent marketing challenges due to low consumer awareness, price sensitivity, and heterogeneous response behavior. Despite widespread use of propensity modeling in broader insurance marketing, there is limited published research applying advanced machine learning approaches to burial insurance acquisition campaigns. This study addresses this gap by developing and validating a predictive modeling framework to improve campaign targeting for burial insurance products. Using a representative sample drawn from a production database of approximately 1.3 million consumer records with historical burial insurance response labels and enriched demographic, lifestyle, psychographic, and financial attributes, we identified key predictors of responseincluding low income, high mobility, limited assets, and behavioral markers such as sweepstakes participation. Ensemble models such as Gradient Boosting and XGBoost delivered a strong performance, achieving 1.8x lift in the top decile and capturing 43% of total responders in the top three deciles. These results demonstrate the value of data-driven segmentation and targeting in reducing acquisition costs and improving ROI for burial insurance marketing, with implications for applying machine learning approaches to other underserved, price-sensitive insurance markets
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