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Insurance CrosSell

Its time to get personal. Boost your cross-sell and up-sell with an individualized offer for each customer

The Problem

An investment product that matures is paid back to the customer. Every month the insurer, one of the top life insurance companies in Singapore with over a million policy holders, has a volume of customers maturing, for whom cheques are written to return their investment – with a total value of hundreds of millions of dollars per year for Singapore alone. Currently, the vast majority of these paid out investment funds flow out of the organization as customers reinvest or repurpose these funds outside of the insurer. No proactive action is taken by the insurer to recapture these funds as the time, skills and data required to come up with recommended products are not readily available.

The Opportunity Cost

Hundreds of millions of customer funds flowing out of the organization – yearly

Potential loss of customer as a whole, not just for this one product

Missed win-win customer engagement opportunity

Our Solution

Our Next-gen AI platform grows Revenue through Micro-Segmentation. Get Immediate Results with a 3 Week Pilot.

To get customers to reinvest their funds, the insurer first of all needs to know which policies are expiring, and secondly, what those customers would be interested to reinvest in. By using its intelligent data platform, Ulysses, combined with expertise from its data scientist team, Latize now provides the insurer with 1-1 personalised product recommendations – just before their policy will mature. How this was achieved?

Diverse customer data (transactional, behavioural, demographic), product information (product features, performance, sales) and channel information was brought together and transformed by Ulysses into a harmonized, intelligent knowledge base. Next, semantic algorithms were applied to this harmonized data to recommend to the insurer which product to cross sell to which customer, when and – very importantly – why.

Finally, through an automated feedback learning loop from executed campaigns, Ulysses was able to evolve it’s customer understanding, thereby improving the recommendations’ accuracy.

Increase customer engagement – proactively address the customer ‘s needs in a highly personalised way

Fast time to market with no complex data integration – after 3 weeks the first iteration of personalised recommendations was delivered

Ever Increasing Conversion – the first iteration already delivered a 10% increase in conversion, which is improving further as more campaigns are conducted

Forward looking regulatory compliance as each recommendation comes with the underlying reasons for it: a No Black Box approach