Customer retention using Survival model
3AI August 16, 2020
Leading Life Insurance Company in Poland
- Look out for an approach to retain policies and riders and to set up an early warning system to identify a potential attrition cases to prevent further revenue leakage
- Loss out on premium on riders which was lost due to rider and policies lapsation.
- Loss around 369 MN PLN in ANP due to lapsation
Analytics Led Approach
- After observing Attrition trend in the data over the years an approach was framed to provide a solution to contain further revenue leakage
- A Cox proportional hazard regression model was built to predict the propensity of policies to survive over a period of time
- Given are high level process steps which were followed
- Data Exploration – Combining Policy; Customer; Prospect and Agent Data
- Data processing and Variable Selection
- Analytical Model Development
- Model Output
- Reports & Insights
- Identification of product affinities of segments of customers
- A product recommendation engine to provide new product recommendations (5 each) with their probability of conversion for all 3.5 million lapsed customers was designed
- Primary users, retention team were provided with insights for customer retention
- Integrated the attrition prediction with every customer touch point system through a web service and formulated a growth strategy to retain the existing customers as well as grow their business
- Model output was segmented to optimize the approach to follow the case and provided insights to treat each policy based on the segmented matrix Re-evaluate;Retain;Grow;Protect– for example, Retain is the quadrant high value and high risky policy, retaining such policies will optimize the effort as well as cost.
Critical Success Factors
- identified attrition and revival opportunity of worth 37 MN PLN for one particular month. 1% retention will result in 3.7 MN PLN recovery in a month.
- Provided most probable list of policies and riders to be retained on a monthly basis