Legal and General develops new customer risk analytics tool to reduce lapse rates

© Legal & General
© Legal & General

The insurance company turned to the analytics specialists Tableau to build a new 'customer risk and opportunity management' tool to reduce customer lapse rates

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British insurer Legal and General has been building a new tool to give it better insights into customer risk and opportunity management in order to reduce lapse rates - the rate at which life insurance policies terminate because of failure to pay the premiums - using software from analytics and BI specialist Tableau.

The insurer wanted to better leverage its customer data to spot trends relating to customer retention, underwriting and claims experience. This started with a new distribution quality management (DQM) programme in 2002, now headed up by Rob Gaunt, head of commercial management and DQM.

Responding to Computerworld UK's questions over email, Gaunt said: "The DQM program has evolved by developing improved insights by combining different data systems to see how performance varies across a suite of metrics," delivering a reduction of 1-5 year lapse rates by around 30 percent since 2010.

"The next step was to dive deeper into the data at a more granular level to allow comparative analysis to drive out learning through improved correlation analysis," he added.

The result is a new 'customer risk and opportunity management' tool, built on top of Tableau software, which started development in early 2017.

So, why Tableau? "Its proven capacity to visualise variation, to link with a great variety of data repositories and to mesh large data sets quickly and the way in which data driven conversations could be conducted," according to Gaunt.

The idea is that Legal and General can be more data driven when it engages with customers. "It will create client needs analysis through profiling and segmentation," Gaunt explained.

"For example; understanding the customers preferences, knowing their stage in the sales lifecycle (what is the most appropriate product at their specific stage of life and family requirement)."

This data is then delivered to the relevant people - predominantly partner intermediary firms that sell insurance products to their own customer base - via dashboards and "an established management program which has structure that can either send notification for action both automatically and via the regular management forums to ensure the right individuals are engaged at the right time," Gaunt said.

According to Gaunt these partner firms tend to "have little shown in terms of CRM competency and we feel that this tool will give them an edge, provide them with a more timely and structured way to manage their client banks."

The tool will be trialled with these firms in late Q2 and go live fully in the second half of the year, according to Gaunt.

"Our initial limited roll-out will gather pace over time and our aim is to offer the service to any agency of relative size that can gain benefit," Gaunt added.

The first partners were selected based on a certain set of criteria, namely firms that "have the variety of product types, customer variety, cover requirements and geographical spread," Gaunt said.

"As such, we will be engaging with those that have the scope and scale that will allow us to have as broad a view as possible, such as networks and those firms who cover customers that have the diversity needed."

Gaunt admitted that there are no advanced machine learning capabilities baked in at this stage though.

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