Leveraging AI and Data Analytics for Enhanced Customer Profiling and Lead Generation in Insurance Distribution: A Data-Driven Approach

Abstract

In the competitive landscape of the insurance industry, accurately identifying and targeting prospective clients is essential for maintaining market position. This study investigates the role of artificial intelligence (AI) and data analytics in refining customer profiling and lead generation within insurance distribution. Using advanced data processing techniques, machine learning, and predictive analytics, the study reveals how AI-driven segmentation and predictive models can enhance customer engagement, segmentation, and personalization. Customer segmentation was achieved through K-means clustering based on Recency, Frequency, and Monetary (RFM) values, identifying five unique segments. For instance, the high-value, high-engagement cluster displayed an average transaction frequency of 5, recency of 30 days, and a monetary value of $1,000, while a premium, highly engaged segment showed an even higher monetary value of $1,500. These segmentation insights allow insurers to tailor engagement efforts based on specific customer characteristics. For lead scoring, the Random Forest model outperformed others, achieving an F1-score of 0.86 and a ROC-AUC of 0.89, providing a reliable method for identifying high-quality leads and focusing resources on clients with strong conversion potential. Additionally, behavioral prediction models using Random Forest accurately forecasted policy renewals (91.2% accuracy) and new policy purchases (85.4% accuracy), with F1-scores of 0.90 and 0.85, respectively. These results allow insurers to proactively address client needs, enhancing customer loyalty and satisfaction. Moreover, accurate predictions of claim likelihood (83.3% accuracy) and high-risk profiles (87.5% accuracy) support effective risk management and targeted interventions. This study concludes with recommendations for integrating AI-driven profiling and lead generation tools within insurance operations, underscoring their impact on customer acquisition, strengthened client relationships, and long-term revenue growth. The findings highlight AI's potential in transforming customer engagement, ultimately contributing to profit maximization and sustainable growth in the insurance sector.