Boosting Insurance Sales by Predicting Customers Next Purchase
The client had high customer turnover and large variability in their sales staff; while revenue was steady, growth was flat. Our team was tasked with building a Next Best Offer Model (propensity to purchase) to guide sales staff in presenting customers products they had a higher likelihood to purchase. The goal was to accelerate growth by driving sales, reducing churn, improving staff retention.
A machine learning model was developed to identify which products a customer had the highest likelihood to purchase, and integrated the Next Best Offer recommendations into existing sales staff dashboards. By empowering the sales staff with the best products to discuss with each customer, yearly sales rose by 17%.
The model incorporated historic sales data, customer demographics, and external data. By combining multiple disparate sources of data, the machine learning model identified the key attributes of a customer that made them more likely to purchase one product over another. Knowing what customers were likely to purchase enabled the client to be proactive and highly targeted with marketing campaigns ahead of open enrollment. .
Machine learning models are designed to evolve and continue improving as customers make or reject purchase option.
Other Applicable Industries: Finance, Ecommerce, Advertising, Event Management, Media, Communication, Services, Suppliers
Skills: Value Mapping, Customer Segmentation, Propensity Modeling, Marketing Channel Optimization, and Marketing Material Optimization