Three Questions Your Business Needs To Answer With Workforce Analytics

Sean Collins

One thing I’ve learned over the past decade is that not everyone agrees on what we mean when we talk about workforce analytics. Analytics in the HR domain is very often misunderstood. Even when we use the same words, we can take them to mean very different things.

In part this is due to the analytics maturity of those doing the talking, with focus changing as progress is anchored and built upon. For some, analytics is a technical challenge about man-handling data and turning it into information; for others, this complex process is more elegant and simple.

Analytics can mean live transactional reporting, or prescriptive analytics, or forecasting, or predictive. For the persistent and capable few, workforce analytics is about asking the right questions and using data to tell compelling stories that lead to change for the better.

One question I hear all too often is “What should we measure?” It’s no surprise that the only correct answer is “It depends.” It depends upon what your business strategy is shooting for, the role of the workforce in executing that strategy, and the relative significance of various talent policies.

Instead, let me frame a response in terms of the evolving focus of workforce analytics and give you a sense of how more mature teams analyse different questions compared to teams that are just starting out.

HR analytics helps us answer three questions:

  1. What is our workforce composition?
  2. How effective are our HR policies?
  3. Does our workforce have a positive effect on organisation outcomes?

Can you see the relationship between these three questions and how they build upon each other? Let’s explore each one in turn.

1. What is our workforce composition?

People are our best asset. This is often stated, and increasingly, people are the most unique asset any organisation has, and certainly the engine for maintaining business success and a driver for innovation. But this concept not always put into practice. Leaving aside the talent management maxim for a minute, let’s consider this statement at the basic level.

If people are our best asset, what does our best asset look like? A fundamental task for any HR analytics practice is that it should deliver absolute clarity about how many people are in the organisation and how that changes over time. I don’t mean just headline measures like headcount or FTE, but also being able to segment this population to truly understand what lies beneath the surface of this amorphous mass of employees.

Some of my favorite deep cuts include:

  • Job family – a well-structured job family framework can be leveraged to get a sense of the skill/competency balance within your organisation.
  • Tenure, preferably position tenure – this can tell you quite a bit about your workforce; for example, whether new hires are staying long enough to pay back the costs to get them onboard and up to productivity; when employees start to get itchy feet and self-manage their career by moving out; and which people have little opportunity for change, as it this can negatively affect productivity and engagement.
  • Grade – this provides a lens into workforce cost and how dynamic the internal labour market is.
  • Gender – this is elemental, but any organisation that is serious about diversity should know the balance of males to females.

We can use even these simple cuts as building blocks to create some important insights into our workforce composition and the health of our workforce. For example, what can we learn if all our power engineers have 25+ years of tenure? An experienced cohort, certainly, but are there risks associated with having too many of these experienced heads in one part of the business? Has they presented a bottleneck in the talent pipeline such that there aren’t enough mid-career engineers to replace them as they move toward the retirement window?

Let’s look at at another example based on grade and gender. Multi-dimensional analysis can reveal the true story around balance and opportunity. Male-to-female staffing ratio (Diagram 1) indicates that there are around 1.5 males for every female in the workforce. Overlay that with a grade analysis (in this case, all manager grades) and suddenly we can see that the story is quite different. There have consistently been more than three male managers for every female manager.

Diagram 1: Male to female staffing ratio, all organisation vs managers

Insight into a pocket of long-tenured engineers can help us to understand how we should tailor retirement, development, and succession policies. Insight into male-to-female staffing ratios can be the impetus to explore how our business can move beyond inherent bias and reconsider work-life balance policies, recruitment, and talent development.

2. How effective are our HR policies?

Policies for recruitment, development, compensation, and the like are the levers that can drive change in workforce composition and productivity. If well-designed and implemented, they help us to reshape the workforce quarter-on-quarter to make sure we assemble the best team, the one most aligned to and able to execute corporate strategy.

It’s simple, logical stuff. Analytics gives us insight into whether our policies help the organisation move closer to the best workforce, or ebb further away.

For example, if the organisation’s goal is to achieve 25% revenue growth across the next 5 years, what does this mean for future recruitment – targeted by job family, location, and timing? Perhaps 25% revenue growth means 10% headcount growth across the board but 35% growth in the R&D function. Good HR analytics teams will be able to build a recruitment target that considers workforce demand and supply, by job family and other cuts.

One of my favourite metrics is net hire ratio, by job family. A result of 1.0 means the numbers of people being hired and the number departing are the same. A result of 1.1 means we have the 10% extra hires compared to terminations. A result of 0.9 means we have the 10% fewer hires than terminations.


Diagram 2: Net hire, by job family

Measuring only hiring activity or terminations at a headline level is of limited value. It might tell us about churn, or volume of recruiter activity, maybe even a sense of the hit on productivity. But it doesn’t reveal whether we are building muscle in the right parts of the business to support our strategy ambitions.

You can repeat this model across the full gamut of HR policies:

  • Are we offering similar levels of learning opportunity across all tenure cohorts, or are we consciously targeting new starters over wise heads (with older skills), and is this intentional?
  • Do we have a conscious build versus buy policy for recruitment? Many organisations will predominantly buy talent in the external marketplace at entry-level grades, but aim to build more opportunities for internal hiring or promotions for mid into upper-level grades. Does the data bear this out in terms of internal recruitment rate (by grade) and is it reflected in supporting development and learning initiatives for pipeline cohorts?

3. Does our workforce have a positive effect on organisation outcomes?

Once we understand deeply who’s on the team and how effectively our talent polices sustain and improve workforce quality, our analytic mind will turn to how well these two elements together have an impact on business outcomes.

HR data alone isn’t going to tell you enough. A good HR analytics practice will start to introduce the kind of data that the business uses – finance, customer satisfaction, and operational. This starts to get into higher-order analytics around productivity, and ideally we can link investments in talent to the business results we see. In a perfect world, we could push toward causality, but an improved data set that allows HR to illustrate correlations is a win in itself.

On face value, we should be able to observe and draw some form of conclusion from integrating FTE, payroll, and finance data. For example, the return on human investment ratio can tell us how much profit we derive for every dollar spent on employee compensation and benefits.

Diagram 3: Drawing a line between terminations (high performers) and profitability

In Diagram 3 (above) we can see that the European region is more profitable (0.87, or $0.87 cents for every dollar spent on people) and that the termination fate for high performers is lower (9.6%), compared Asia-Pac (0.83 and 21.1%). Interesting, instructive, and makes sense, but maybe you want to understand more? Could be the start for hypothesis testing: retaining high performers leads to greater firm profitability.

So what?

Fair question. Let’s recap what we’ve learned so far. By understanding our workforce composition and the degree to which it changes, we can get a sense of whether our talent policies are having the effect we anticipate. When talent policies outcomes are thoroughly understood, we can refine them to be more effective. When talent policies are most effective, they help us build a workforce that aligns with our corporate strategy and that will help us to drive stronger financial, customer, and operational performance.

When I talk about workforce analytics, what I mean is creating insights that allow us to make better talent decisions.

For more on HR data analytics, see How HR Can Use Data And Metrics To Speak The Language Of Business.


Sean Collins

About Sean Collins

Sean worked for over 15 years as an executive and management consultant before joining SAP SuccessFactors in 2008. His professional focus across this period of his career was on business and industry strategy, functional management disciplines, issues analysis, and market research. He went on to manage SuccessFactors’ Workforce Analytics and Planning customer-facing teams, which including directing projects in the human capital domain for many leading enterprises across the region. Sean led the SuccessFactors Customer Value function and worked with key customers across the Asia-Pacific region to ensure that they achieve the most from their SuccessFactors investment. Sean’s work with customers looked to understand their business priorities and the contribution that human capital plays in contributing to successful strategy execution. Sean is an experienced workshop and conference presenter on human capital management challenges such as workforce analytics, workforce planning; HR benchmarking, human capital ROI, KPI analysis and data-driven decision making. Sean’s role at SuccessFactors today is to help companies who want to invest in Cloud HR to understand how it can contribute to human resources success and help drive business outcomes. This stretches from illustrating practices that differentiate leading organizations from the rest of the field, assessing maturity of current practices and readiness to grow, and explaining how our technology works. Sean holds a Masters of Business Administration from Brisbane Graduate School of Business at QUT, with a specialisation in Business Strategy.