Home' Superfunds : Superfunds August 2014 Contents Superfunds August 2014
he big data journey can be
intimidating for superannuation funds,
whether they are at the beginning of
their journey, or are looking to add an extra
dimension to their data strategy. However,
while member engagement in the super
sector is difficult, compared to other sectors
where customers are offered almost instant
gratification – for example, the retail, travel,
entertainment or leisure industries – the
super industry does have its own advantage.
Superannuation funds have access to
individual data about their members, and,
as a result, are able to gain a detailed
understanding of them.
To help funds focus their data strategy, the
following are a series of steps that help break
the journey down, and enable funds to start
optimising their data assets and member value.
STEP 1: A DATA AUDIT
The first step is to ascertain what data you
actually have access to with a comprehensive data
audit. While this process can seem a little tedious,
it is an essential first step in the journey, and lays
the foundation for all the steps that follow. It’s
also an opportunity to identify ‘quick wins’.
There are often data variables that are
not immediately obvious as being useful and
predictive, and these can be identified through a
thorough data audit. For example, the presence
of full contact details of human resources (HR)
staff at an employer level can be linked to
the engagement level of the employer, and,
ultimately, their value.
STEP 2: BUILDING A SINGLE MEMBER VIEW
With the data audit completed, a single member
view can now be created. This does not need to
be a complex or expensive process to start with.
Using match keys, such as household and date-
of-birth details, will provide valuable insights, and
will also start to help build a wider, data-based
As an example of a quick win, the identification of
spousal relationships within households represents
opportunities for not only spousal contributions, but
they are also insurance ‘up-sell’ triggers.
STEP 3: DATA PROFILING
Data profiling will enable funds to understand
more about their members’ profiles, in relation to
both other fund members, and, where applicable,
against the population as a whole.
Profiling using internal variables –
such as age and gender – can then be linked to
behaviour and provide some clear insights, along
with operational and marketing opportunities.
An example is the link between age, gender and
the adoption of salary sacrificing. Analysis across a
number of funds has shown that females between
the ages of 37 and 39 are typically the most likely
to salary sacrifice. With this information, this group
can be targeted if they have not already started
to salary sacrifice, or encouraged to keep salary
sacrificing if they already do so.
We also see clear links between ‘milestone’
birthdays and the adoption of insurance. For
example, when people turn 30 and 40, they often
reassess their current insurance needs, which can
be seen in the data profiles.
STEP 4: TIME SERIES ANALYSIS AND
As noted, there are many data challenges
inherent to the superannuation industry, as
compared to other sectors. For example, the
immediate gratification in sectors such as retail,
entertainment and travel gives us instant feedback
on consumer purchases. However, one of the
great advantages of super data is that the industry
really knows the customer (member).
In most cases, super funds have access to a
member’s contact details, age, transactional
history and who they are employed by. They also
have permission to contact the member about
super-related products and offers, and, as we see
the war on privacy intensify, this permission to
contact customers directly (and legally) will be of
This all adds up to having, across the majority of
members, a very clear idea about that member’s
behaviour over time, and also if they are the same
or differ from their cohort. This provides a very
strong series of ‘time series’ and ‘data triggers’,
which can be used to identify when members are
moving into different behaviours.
For example, if a member is working for an
employer in a sector that is experiencing a lot of
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