3 Questions Your Business Needs to Answer with Workforce Analytics

One of the things 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. We can be using the same words but take them to mean quite 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.

A far too common question I field 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 than those just starting out.

My position is that 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 and the way they build upon each other? Let’s explore each in turn.

1. What is our workforce composition?

People are our best asset? Often stated. Increasingly the most unique ‘asset’ any organisation has and certainly the engine for maintaining business success and a driver for innovation. Sure, not always put into practice. Leaving aside the talent management maxim for a minute let’s just consider this statement at the basic level. 

If people are our best asset, what does our best asset look like? A fundamental for any HR analytics practice is that they should deliver absolute clarity about how many people are in our organisation and how that changes over time. Not just headline measures like headcount or FTE. Also, being able to segment this population to truly understand what lies beneath the surface of this amorphous mass of employees. Some of my favourite deep cuts include:

• job family – a well-structured job family framework can be leveraged to get a sense of the skill / competency balance within our organisation.
• tenure, preferably position tenure – this can help tell you quite a bit about your workforce. Whether new starters are staying long enough to pay back the costs to get them on-board and up to productivity; when employees start to get itchy feet and self-manage their career by moving out; to where there are pockets of people with little opportunity for change, which can sometimes present risks around productivity or engagement.
• grade – provides a lens into workforce cost and how dynamic the internal labour market is.
• gender – this is elemental but any organisation 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?

 Looking 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 re-consider 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 an organisation has that can drive change in workforce composition and productivity. If well designed and implemented they help us to re-shape the workforce quarter-on-quarter to make sure we assemble the best team, the one most aligned to, and able to execute, corporate strategy.

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

For example, if the organisation has an ambition for 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% less hires compared to 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 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, 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 Rate for high performers is lower (9.6%), compared Asia-Pac (0.83 and 21.1%). Interesting, instructive, 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 re-cap 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 aligned with our corporate strategy and 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. 

How about you …? Please leave your comments below or share with your network here on LinkedIn.