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Improving Customer Retention through Machine Learning Insights

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Solutions 

Indiana Farm Bureau Insurance worked with New Era Technology to build a machine learning model that would capture the likelihood a given household will have an active policy or still be a customer within 30, 60, and 90 days. 

Project Goal

Fast Setup of Machine Learning  

We wanted to provide Indiana Farm Bureau Insurance with accurate, actionable insight quickly.  
Our Solution
  • Chose practical use cases with small but significant results 
  • Vetted use cases to drive maximum predictive value with minimal risk 
  • Automated machine learning technology instead of bespoke algorithm development 
Project Goal

Capture Accurate, Actionable Results

Indiana Farm Bureau Insurance wanted to provide the customer retention team with insight on who to contact and how to position retention strategies. We needed to quickly capture accurate, actionable results.
Our Solution
  • Built two machine learning modules including deep neural network and traditional statistical analytics 
  • Selected concrete questions that would leverage over 3 million policy snapshots and target 35,000 at-risk policyholders 
  • Drove returns through incremental machine learning investment 

Outcomes

Implementing machine learning directly led to  improvements in identifying and connecting with policyholders most at risk of leaving Indiana Farm Bureau. It also builds a foundation to dig deeper into customer behavior in the future. 

a-machine-learning-models
2 Machine Learning Models Built, Including a Deep Neural Network
b-data-points-available
180 Data Points Available for a Given Household Per Year and Per Month
c-Improvement-risk-policy-holder
25% Improvement in at-Risk Policy Holder Determination

Recent work

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