Predictive data crucial for member retention

25 May 2017
| By Jassmyn |
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Many superannuation funds are losing the scale war and will need to master the art of member acquisition and retention to survive, according to Rice Warner.

The research house’s latest analysis said leveraging data would be crucial for funds to avoid becoming an exit statistic.

Rice Warner said random approaches to members such as cold calling or mass emails were likely to reach, on average, only 7.5 per cent of the intended audience. It said data-driven approaches to retention should improve these prospects.

The analysis said key demographic and account-based indicators could provide insight into a member’s likelihood of remaining in their fund.

“Analysis of the data shows that member exits peak around age 30 and remain subdued until members begin to plan for retirement from age 50. Retirement acts as a catalyst for change and is the peak age at which members are likely to adopt a new provider and exit the fund completely,” the analysis said.

A member’s account balance could also help predict departures as smaller account balances were more likely to be represented in exits from the fund. However, departure of members with high balances had a greater financial impact.

Rice Warner said datasets could tease out the nuanced relationships between a number of correlated interdependent variables and these models would arrive at far more accurate predictions of whether a member would exit the fund in the next year, and therefore guide targeted retention strategies.

Rice Warner said this kind of model could improve the hit rate of a marketing campaign from 7.5 per cent to more than 50 per cent.

“Funds need to correlate all this information and then work out what incentives to provide to encourage members to remain with the fund,” the analysis said.

“On a practical level, improvements of this magnitude can translate into significant increases in the profitability of retention campaigns as the ability to reach members who would otherwise exit can be increased over 10-fold.

“With data volumes forecasted to double every two years into the future, leveraging this data will be crucial for funds to avoid becoming an exit statistic.”

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