The problem

In the insurance sector, customer acquisition and retention are equally important, but the first one is a much more expensive process. Specifically, the insurance industry has the highest customer acquisition costs of any other industry , with the cost of acquiring new customers being seven to nine times higher than the cost of retaining a customer. Therefore , insurance companies rely on the existing data they have in their possession, to understand customer behaviour and prevent them from leaving the company or the policy.

The desideratum

Predict customer churn, i.e the percentage of customers ending their relationship with the insurance company in a given period, typically monthly, quarterly or yearly.

The solution

With the use of machine learning and artificial intelligence models which rely on the historical customer data, a more meaningful targeting of customers can be carried out in the process of organising the marketing strategies related to customer retention in the insurance company.

The benefit

The usefulness of the predictive model lies not only in identifying customers who are going to renew their insurance policy or not, but also in identifying the main factors related to this decision for each individual case. With this information at their disposal, insurance company executives or insurance consultants can identify the main points that lead to the customer's decision to renew or not the contract, reformulate or even create more personalised offers, thus improving management of their customer relations. Identifying at-risk customers can lead to more meaningful customer targeting, saving the insurer or agency time and money from unnecessary communications. Actuaries can also leverage the model's predictions of the number of customers who will renew and optimize their pricing plans.