Smarter Conversion Predictions for Insurance With the Right Data

Focus on the applicants that will become customers with conversion predictions using augmented data
The increased level of competition in online insurance has put pressure on companies to find ways to boost conversion rates earlier in the sales cycle. For insurers, the challenge is understanding which factors could lead to conversion at the start of the cycle, as well as once users submit an initial form. However, reliance on internal data alone offers too narrow a scope.
The problem

Our customer built models to predict top-of-funnel and initial form submission conversion rates, but they were producing too many inaccurate predictions. The company was having trouble pinning down the factors that indicated a higher likelihood of conversion based on the data collected from their own website. They could gather data such as click locations and pages visited, but they couldn’t build a full picture of the visitors.

The solution

The company built two parallel models — top-of-funnel and initial form submission — using Explorium to identify the right factors for conversion rates. Our customer connected their datasets to the Explorium Enrichment Catalog to add the following data to each model: 

Start of funnel conversion prediction: 

  • US income by zipcode
  • The empirical distribution of rent as a percentage of income 
  • The average age based on census and demographic data
  • The average age of children under 18 in families in zipcode 

Form submission conversion prediction: 

  • The likelihood of obtaining accidental death and dismemberment insurance 
  • Purchase month of homeowners’ insurance 
  • Year of first home loan
  • Frequency of purchases made with a credit card
  • Likelihood of using an e-reader
The results:

Optimized marketing budgeting and higher conversions

The company’s top-of-funnel model saw an immediate uplift in predictive accuracy to 88.6%, leading to a much higher conversion rate and allowing for smarter marketing spend upfront. The initial form submission model had a 90.44% accuracy score when predicting conversions. Combined, these two models allowed the company to make smarter marketing decisions and to understand which actions would lead to user conversion. As a result, our customer was able to design a better user journey, and boost its revenues and customer engagement significantly.

Learn how the right data powers smarter conversion predictions and boosts your ROI.

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