All companies believe in strong customer relations, but what the best of them know, is that not all customers are equally profitable. The challenge therefore, is to identify customers based on their levels of profitability, so that insurers can select different target groups for specific products. Insurance analytics tools and techniques such as profitability analysis, survival analysis, and forecasting can make a valuable contribution when used in tandem with the Customer Life Time Value (CLTV) model.

The CLTV model segregates customers based on their future profitability and helps insurers create a database of high value customers who will generate the greatest profit in the long run. The CLTV model can help insurers lower market costs, identify the right target clientele, price their products correctly, retain current customers while increasing up-sell and cross-sell opportunities, and customize products based on a deep understanding of customer needs.

Using Customer-generated Data for CLTV Analysis

In this context, the feedback left by customers at the contact center can be of immense value. By using opinion mining analysis, customer comments can be turned into a detailed chart of the pain-areas felt by an insurer's clientele Predictive analytics can then be used to forecast the number of customers expected to attrite, and to offer customized products and offers to these customers.

The CLTV model also helps insurers prioritize their efforts across customer groups and in decide the quantum of resources to spend on each. For instance, a customer with a medium CLTV value and low priority of policy lapse does not need much attention, while additional efforts are necessary to retain a customer on the same CLTV level but with a higher policy lapse probability.

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