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Using predictive analytics for member retention

Using predictive analytics for member retention

Reporter
— 1 minute read

Super funds concerned about member retention will be able to use predictive data models to determine what clients are likely to leave and build strategies to try keep them, says Financial Synergy.

Speaking in Sydney yesterday, Financial Synergy chief executive Stephen Mackley said the biggest issue for a super fund is knowing when a member is going to leave the fund.

“If you look at the [reason people leave, funds] often don’t know why and they’re guessing why they are going,” he said.

This is where Financial Synergy has integrated the big data analytic capabilities from information management firm OpenText into its Acurity platform.

By doing this, Mr Mackley said it will improve the platform's capacity to analyse member data and predict potential client movement.

“With good predictive analytics you can have more of a view of the characteristics of those people that are leaving."

By understanding these characteristics, Mr Mackley explained that super funds would then be able to target specific clients based on their needs.

“[It is] not just to know where people are going, but why they are going and to predict people who could go and then try to service the members in a way that they will not leave,” he said.

“Most people don’t take any interest. So if people are going to get their member statements online the figures are that 70 per cent of people don’t click and go have a look at it.

“Super funds are very conscious of it and they are doing a lot of things to engage more with members,” he said.

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Using predictive analytics for member retention
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