A large insurer sought to shift their model for selling long term care insurance by pivoting from third party brokers to direct sales. In order to do profitably, they needed to decrease their lead-to-policy cost. This, paired with the volume of data available, presented the opportunity to apply machine learning in a proof of concept which would help the company determine those leads most likely to convert. The UDig team evaluated thousands of model variations before identifying a successful artificial neural network variation as a model that identified patterns indicative of purchasing behavior.

With the goal of increasing product profitability around the client’s specific insurance product, and in light of a changing marketplace, our client sought to eliminate the use of third-party brokers. Instead they sought to pivot to a direct sales approach focusing on organic lead generation.  

Our client combined their existing data sources with a third parties rich market research data. This created a very broad data set to identify trends in customer conversion. 

The pivot in approach led to an interesting challenge: while they could describe historic behavior for this product well with their data, it was not directly applicable to their new approach. Indeed, the data presented a very biased view of customer acquisition due to the nature of brokerage services.