About Client

A leading online insurance company in India sought to identify at-risk customers and implement effective retention strategies, ultimately improving the 13th-month persistency rate.


  • Identify customers at risk of lapsing in the 13th month.
  • Develop effective reactivation and retention strategies.


To achieve these objectives, Transorg implemented the following approach:

  • Segmentation of customers into categories, including urban mass, rural, and urban affluent, among others.
  • Comprehensive analysis of historical policy surrender data to predict the likelihood of surrender one month in advance.
  • Utilization of advanced machine learning techniques, such as random forest and gradient boosting, to improve predictive accuracy.
  • Ongoing monitoring and analysis, including coverage assessment, accuracy evaluation, trend analysis, and opportunity sizing on a monthly basis.


  • Enhanced identification of potentially lapsed customers compared to random selection.
  • Establishment of a proactive customer retention approach, with a focus on engaging high-value and high-propensity customers at risk of lapsing.
Want to learn more about TransOrg’s value proposition, solution methodology and implementation approach?