The insurance industry continues to traverse through its digital transformation journey, which in recent years has been further accelerated by the COVID-19 pandemic. Carriers continue to look for innovative means to improve business outcomes by leveraging proven data management, and automated and continuous machine learning capabilities.

One such application of these capabilities is in the betterment of outcomes achieved through account penetration (cross-sell and up-sell) campaigns, a known lever to contribute toward profitable growth. This application would establish a paradigm of continued and predictive improvements in account penetration tactics, such as predicting future customer behavior in cross-sold insurance products. By focusing on the right customer segments for cross-sell campaigns, insurance carriers can significantly improve their customer engagement and cross-sell conversion rates.

In this paper, we outline how TAZI’s Cross-Sell Prediction solution works. This solution is based on TAZI’s Continuous and Explainable, No-Code, Automated Machine Learning (AutoML) platform. We describe how continuous learning and dynamic customer segmentation help predict the most qualified leads for a possible cross-sell purchase, such as an umbrella policy. We also describe how business units, such as sales managers and agents, can monitor their sales processes and take the best sales and marketing actions in order to improve their conversion rates.