Statistics from the Australian Prudential Regulatory Authority (APRA) indicate that 44% of superannuation funds experienced negative cashflow (excluding investment returns) over the 2015-2016 financial year. Thus, it becomes clear that many superannuation funds are slowly losing the scale war and to survive will need to master the art of member acquisition and retention.

Member retention programs are costly to administer and their effectiveness is difficult to measure. Members who will leave a fund make up a small proportion of the fund’s membership. As a result, simple random approaches to contacting members such as cold calling or mass emails are likely to reach, on average, the intended audience in only 7.5% of cases¹.

Recent work by two Rice Warner actuaries, Stay or Go? The science of departures from superannuation funds presented at the Actuaries Summit 2017 suggests that data-driven approaches to retention should improve these prospects. Analysis of a sample of superannuation member records from the Rice Warner Superannuation Insights database suggests that key demographic and account-based indicators can provide insight into a member’s likelihood of remaining in their fund.

But what factors are important?

Age and Tenure

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.

Graph 1 reflects this trend, which is consistent across both new and long standing members, although longer serving members are less likely to leave the fund when compared with newer entrants.

Graph 1.            Proportion of members exiting as a function of age and tenure

Balance

Graph 2 demonstrates that, in addition to age, a member’s account balance can also help predict departures as smaller account balances are more likely to be represented in exits from the fund. This could follow from:

• Members with low balances being more likely to consolidate to their primary (or larger) account.
• Higher levels of exits at younger ages (where members have not yet accrued large balances).
• Active but disengaged members accruing higher balances but being comparatively less likely to exit relative to inactive members due to being not subject to automatic processes such as ATO Lost Super transfers.

Of course, while low balance members are more likely to leave a fund, departure of members with high balances have a greater financial impact.

Graph 2.            Proportion of members exiting as a function of balance

Improving the effectiveness of marketing efforts

Statistical models built on datasets such as this can tease out the nuanced relationships between a number of correlated interdependent variables. Thus, these models can arrive at far more accurate predictions of whether a member will exit the fund in the next year, and therefore guide targeted retention strategies.

Our research has shown the use of such a model can improve the hit rate of a marketing campaign from 7.5% to more than 50% in terms of successful predictions. Of course, funds have more information at their disposal, including interactions by Call Centre or websites. Funds need to correlate all this information and then work out what incentives to provide to encourage members to remain with the fund. 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 ten-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.

¹ Bonarius, N. & Dunn, R. (2017). Stay or Go? The science of departures from superannuation funds. Paper presented at The Institute of Actuaries Australia Actuaries Summit 2017, Melbourne, 22 May.