It’s great to see more and more Superannuation Funds turning their attention to Enterprise Performance Management frameworks and how such capability can be deployed in their organisations.

With Enterprise Performance Management (EPM) we are talking about how an organisation can better measure and manage the delivery of its strategic objectives and, fundamentally, its mission.

For Funds, this is using data to provide line of sight into how they are tracking to their core performance indicators. This could be anything from member engagement, retention, member satisfaction, to cash-flow projections. Ultimately, the question to be answered by their Boards and Trustees is: how is the Fund delivering better outcomes for their members?

With a background in performance analytics, largely in corporate’s where these frameworks are very common. I have seen the trend in EPM shift over time, from management & statutory reporting to budgeting, and then following the GFC, to creating sophisticated rolling forecasting solutions.

When EPM is a newer area of focus

My observation in working with Superannuation Funds is that this is a newer area of focus. It turns out this requires particular expertise to adapt common corporate performance approaches (such as Kaplan & Norton’s Balanced Scorecard) to reflect the member focused and growth metrics underpinning profit-to-member funds.

In our work with Funds we are seeing many similarities in how Funds are developing their performance management frameworks, while at the same time each is taking slightly different tacks.

And of course, as our team works with different Funds, we are seeing some common challenges.

At the heart of it, the key challenge with Performance Management analytics is bringing together disparate data to report on how actual business performance is tracking to targets.

Top 3 outcomes to consider

Based on our experience, we present three Top Outcomes to consider in your performance management initiatives.

Top Outcome 1 – Variance Analysis

At Laneway Analytics, our view is that understanding how and why a metric is changing is the most important outcome. By understanding the variance to budget, target, benchmark, and/or trend we can provide deeper insight into the business drivers that are impacting the change in the metric. The benefit of this is to create a clear focus on the best opportunities to improve the metric.

In working with Funds, we are seeing these common challenges:

  • Understanding what a good system looks like, and then tailoring good practices to the Fund’s unique requirements and maturity
  • Collecting and consolidating the many data feeds: member data, finance data, investment data, commentary, offline ad hoc data (where a system doesn’t exist), survey data, and external data
  • Creating a common system for collecting reference data, usually manually input, such as targets, assumptions & drivers, and commentary
  • Calculation logic/business rules for metrics – this is both political and technical
  • Creating an intuitive interface that allows executives and managers to complete their reporting process and visualise the results, and then assess if the business is at risk of missing the target
  • How to drill into the drivers and data elements that help explain the variations to target

Top Outcome 2: Context and Comparison

As well as a deep understanding of variance, it is critical to add context and comparisons to fully develop and enrich any evaluation of the changes in metrics, and for drilling into variances.

  • How can a system help a user intuitively assess if the absolute value of a metric is good, bad or otherwise? How significant is the change over time?
  • We have a deep belief in providing comparison points such as: trend lines, actual to budget, prior period, prior corresponding period, moving average, and peer based comparisons, to name a few. In this, we are meeting the technical challenge of creating a dynamic system that provides these calculations depending on a user’s point of view.
  • Our approach to this is to provide engaging context dashboards that help users quickly assess their metrics and understand where and how their metric is changing, and where to focus their analysis.
  • In our latest release of our SuperFund data & analytics platform, Provident Lane, we are providing automated insights via natural language generation AI. We believe this text based narration provides additional context for users.
  • Peer based comparisons are invaluable in determining performance. Far too often we have seen performance rewarded on an Actual vs Budget basis. But if actual performance is 5% above plan, and your peers are >5% above plan, then you have to really wonder if the traditional comparison to budget is the most meaningful comparison point.

Top Outcome 3: Scenario Modelling

If our system can accurately consolidate actual’s, calculate the variations to the target, and provide robust understanding about the variance, the next most important question is: ‘Ok I understand what has happened, what does this mean for the future?’

This brings us into the territory of forecasting – our performance management system must help us forecast what the changing drivers and trends mean for the subsequent reporting periods.

This then assists us understand the criticality of taking action, and the variance and driver analysis helps us understand where to focus for the biggest opportunities and risks.

The value of a well thought out and well executed performance management structure for Super Funds is considerable, however the success of this is often determined by the experience and expertise of the management and the partner a Fund uses to execute.

About Author

Grant is a successful entrepreneur, a visionary and leader in data & analytics, performance management and related technologies. He has extensive experience working in and leading the design of complex analytic and reporting systems projects and businesses. Grant has delivered analytics, performance & reporting solutions for some of the largest firms in Australia and globally; including Amcor, Google, Swisse Vitamins, The Red Cross Blood Service and Toll Holdings.


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