INSURANCE RENEWAL & LEVERAGE PREDICTION Next Case Study

Client Organization:

South India's largest general insurance company

Project Owner:

CMO

The Problem:

Motor vehicle insurance policies, especially in segments like tractors, have abysmally low renewal rates in India. Individual customer decisions on renewal may be affected by a variety of factors such as location, relationship with dealer, quality of service from the company in past year, claims filed and paid, pricing for renewal, market share in the given geography, etc. Identifying in advance customers who are most likely to renew helps in focusing field efforts for maximum ROI.

The Solution:

We analyzed hundreds of thousands of policy records and were able to bin each record into one of the following clusters red, orange and green. Red bins are policy holders that are unlikely to renew despite any intervention, Green bins are the customers who will renew anyway, and Orange bins are those who are likely to renew in the face of specific interventions, which can differ significantly from one policyholder to the other. (Possible e.g. of interventions: premium reduction, claim issue resolution, more frequent contact with policyholder, etc.) We built a system that dynamically binned policyholders into the three bins, and recommended individualized interventions for orange bin members. The aim was to increase the policy renewal rates especially for tractor insurance.

Tools & Technologies:

R, KNIME, OpenRefine and WEKA