As superannuation funds grapple with the most competitive landscape they have ever faced, the realisation is emerging that their data – the customer data they hold and the behavioural data they generate – may be the key not only to survival, but to success.
Superannuation is now a consumer business. Unlocking and leveraging their data can allow super funds to talk to their members in the most targeted and specific manner they have ever achieved. It is now possible for super funds to deliver to their members relevant personalised communication, helping the members to make better decisions, and securing that person as a member for life.
Fund managers have diligently tried to ‘segment’ their members based on what they know about them – usually into “personas” based on similar life-stages – in order to communicate with them, but even allowing for improved sophistication, this runs the risk of being a scatter-gun approach. “That’s been the first step, to come up with a good group of representative personas for your members, and keep refining and building on that,” says Richard Body, head of digital solutions, Australia, at consultancy Willis Towers Watson.
“Whatever you use to do that – whether it’s age, or lifestage, or behavioural types – for example, ‘active’ members and ‘passive’ members – there is no written rule on what works best, and there’s a lot of experimentation going on. Ultimately, the funds are looking at the Nirvana of segmenting down to the individual: ideally, they could talk to their customers individually. But getting down to 800,000 or one million ‘personas’ of the client – that is difficult.”
Body says the “gold standard” of super funds’ data is around using the data they know about a member as well as possible, to leverage that data to speak to their members in the most relevant way. “That’s what funds are trying to do at the moment, and to a certain extent, they’re succeeding. They know, for example, if someone is already making voluntary contributions, so they don’t try to sell that member on the benefits of voluntary contributions.
“But the future ‘gold standard’ – which is coming upon them rapidly – is going to be a much higher bar than that. It’s going to be around leveraging advanced data analytics and tools such as artificial intelligence (AI) to try to better understand what people are doing and why they’re doing it. The future gold standard is going to be around knowing the individual customer better, and knowing the future of those customers better, so that you can then help them make the right decisions, in the right way,” says Body.
According to a senior technology executive we spoke to at a leading superannuation fund, that “Nirvana” for funds would allow them to “be predictive, so as to be proactive.” Ideally, says the executive, the data analytics would allow the fund to see what members are doing. “If, for example, someone is showing signs of possibly rolling out and consolidating into another fund, it would help you message them, proactively, to try and prevent that attrition. It’s about knowing better what they want from you at the different stages of the customer journey, so you can provide it,” the executive adds.
According to Darrell Ludowyke, chief executive officer at data integration and analytics service Empirics Data Solutions, once super funds have a single view all of their data including administration data, transactions, insurance, and payments into and out of the fund they can then start to add predictive analytics into the mix.
“That is well and truly here,” he says.
One of the first applications for this – and still a major use – is in modelling defection, he says. “We help our super fund clients score every member on their risk of defection, within the next three to six months. That allows the fund to get on the front foot and act to retain that member, by whatever retention strategy they may wish to employ.”
Predictive tools are also used to identify when is the right time to talk to a member about particular products, for example, a fund’s pension product. “Funds used to put a finger in the air and say, ‘Let’s talk to them after their 53rd birthday, or when their balance is $X.’ Now we take a lot more granular and scientific approach to predicting when a member is ripe for that conversation, based on all of their activity across various touchpoints,” says Ludowyke.
But where the predictive analytics really gets interesting is in foreshadowing member behaviour.
“We bring in analysis of the external news, and the general sentiment of that news, whether negative or positive, and we can correlate that to the behaviours of members around how much are they going online, checking their balance, checking the available investment options, and then actually acting on that,” says Ludowyke. “We can see spikes in movement to more conservative options, when there is news such as the Brexit vote, or the election victory of Donald Trump, which caused turmoil on the markets.”
The problem is, he says, that often when the bad news passes, people don’t respond. “They don’t switch back from their conservative choice, and end up with a poorer result down the track. When you see things like 27-year-olds moving all of their super into cash, and then leaving it there, you know that is likely to give them a poor outcome when they’re 50.”
Aside from the member investment side, funds are using predictive analytics in their marketing and their product design, and also behind the scenes, where machine learning is looking for transaction patterns that could indicate fraud or money-laundering activity.
“It’s all about our fund clients being able to be as timely with their communication, and as relevant to an individual member’s situation, as possible,” says Ludowyke. “We are using data science to be able to predict and intervene as early as possible, so that our fund clients can ‘nudge’ members on a path that they believe will provide a better outcome,” he adds.
The leading-edge super funds are those that have grasped that this is a “workplace transformation,” says Nathan Gower, enterprise account executive at Dell Boomi. “The super funds that are well ahead in this area understand that it’s not only about building agile analytics skills and teams and capability, it’s analytics combined with marketing. We’re seeing in some businesses the combining of marketing and data analytics teams into one,” says Gower, although he says this way of thinking is “in its infancy” in super.
About the author
James Dunn is a writer for the Which-50 Digital Intelligence Unit. Dell Boomi is a corporate member of the Which-50 Digital Intelligence Unit. Members provide their insights and expertise for the benefit of our readers. Membership fees apply.