/ Machine Learning

What is Propensity Modelling?

One of the many advantages of a composite insurer is the company-wide customer base that can be targeted with a range of product offerings. A customer who purchased a life insurance policy may also be interested in motor or home insurance.

However, it is often difficult for insurance companies to leverage the potential of this form of customer targeting.

Anytime you buy something in Amazon, you see data analytics for cross-selling in action. It comes in the form of “customers who bought this item also bought...” That is Amazon’s way to present you with products their data suggests you may also buy. In fact, Amazon reported in 2006 that 35% of their sales were due to cross-selling.

Insurance companies can adopt a similar strategy and leverage their customer knowledge to offer only relevant products to individuals likely to find them interesting.

This approach is not only beneficial to your company, it is good for your customers too. You increase the number of products you sell to your them, increasing their loyalty and making them more profitable. But you also offer only relevant products, do not irritate by them by offering products that are not relevant to them.

You do not need to be a large, composite insurer to benefit from this approach. You can use it to manage your offering of related or optional products and banks are using it extensively to provide a more tailored offering to their clients.

Predictive modelling is a cost-effective tool to increase sales from your existing customer base. This is particularly useful in a competitive environment where winning market share is increasingly difficult or can only be achieved at the expense of profitability.

Pedro Ecija Serrano

Pedro Ecija Serrano

Pedro is an Actuary with a passion for machine learning and AI. Pedro is Chair of the Data Analytics Committee of the Society of Actuaries of Ireland and has worked for KPMG, AIG, AXA and Aviva.

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What is Propensity Modelling?

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