Calculating cost of turnover

It’s typical that organisations invest more than 40% of their revenue on salaries and wages of their employees. The management and more particularly the loss management of this resource is critical given it represents a substantial direct and indirect cost.

Despite this impact on profit, many organisations fail to accurately calculate employee turnover. At best organisations focus on the direct costs of turnover accurately but fail to calculate indirect costs which encompass turnover’s effect on employee productivity and prove difficult to quantify, but account for at the majority of total turnover costs.

Inaccurate diagnostics tools and calculation methodology contribute to HR inability to get traction around focusing on addressing costly employee turnover, with the outcome being that organisations continue to suffer the loss of valuable human capital, weakening their competitive position.

There are two types of turnover costs, direct and indirect.

Direct costs include:

•         Separation costs
•         Vacancy costs
•         Recruiting costs
•         Hiring costs
•         Training Costs

Indirect costs include:

•         Pre departure lost productivity; employee, supervisor, co-workers, subordinates
•         Vacancy period lost productivity; employee, supervisor, co-workers, subordinates
•        New employee on boarding lost productivity until fully competent; employee, supervisor, co-workers, subordinates

Direct costs are those that are easier to quantify as they’re tangible, whereas indirect costs that focus on the effect on productivity are much harder to quantify.

Although difficult to accomplish, the effective calculation of employee turnover results in the ability to get traction on interventions to prevent it.  If you find calculating your cost of turnover accurately, too complicated we can help you assess over 30 variables that give you a accurate and considered cost of turnover for a key position as well as share the best practice model we use.

Contact us now to get a free cost of turnover analysis worth $1,800 on any key position.

Why you're making the wrong decision

By John Millican

The battle between Predictive Analytics vs Intuition


Among Human Resources, Organisational Development and Leadership circles there is somewhat of a religious debate between using an intuitive approach to decision making against using a data-driven approach or “predictive analytics”.

Predictive analytics is a broad term for a methodology that includes gathering historical data and with techniques that combine data mining, statistical modeling and machine learning to identify patterns to make predictions about the outcome of a particular decision. Discovering risks and opportunities and modelling how several courses of action might play out, before implementing it in a real situation.

Consider these two facts; 1. Most organisations budget for 40 to 70 percent of their operating expenses to employee related expenses. 2. Important decisions affecting an employee base are commonly made subjectively, without proper validation, with a lack of rigor.

Within HR there are broad sweeping and significant opportunities to make a material difference to the largest slice of an organisation’s cost base and the most often the biggest influence on revenue – people and culture. So consider that a balanced approach that uses data, analysis and systematic reasoning to validate anecdotal, employee survey data or intuition is significantly more effective at reducing risk, improving performance, reducing turnover, workforce planning or sometimes selecting the right candidate.

There is a growing acceptance of the value predictive modeling can play in validating intuitive decisions. A combined approach that balances data-driven decision making to test intuition. However, there are some professionals that believe they are largely infallible decision makers, that they are correct the majority of the time. It’s often these same people that sometimes have a reluctance in accepting a contrary view identified by predictive analytics.

It’s absolutely true that no CPU can replace the human brain. However when making a decision, it’s simply too hard for most mere mortals to identify, weight, assess and model-out how the different variables that effect a decision will lead to an outcome and which of these outcomes will be more favorable.

In Neuroscientist and successful author, Dr David Rock’s book “Your Brain at Work” he describes the limited processing power your brain (more specifically the frontal lobe) has when accessing memory and assessing variables to make a decision. Additionally in the ground-breaking research into cognitive load theory by Miller, Simon and Chase we know that our working memory and processing power is more limited than most people would readily admit.

It’s simple, we need help. That help is more readily available with the advancement in technology, the conscious effort to collect high quality, relevant data and a growing appetite for balancing and validating intuition with data driven decision making.

Research conducted by Ian Ayres outlined in his NY Times best seller “Super Crunchers” details over a 130 studies into the accuracy of judgment of experienced managers. In every single case the power of predictive analytics matched or beat the manager’s intuition. In fact, in 46% of these studies it found the manager’s intuition significantly underperformed an algorithmic prediction.

Research shows that high performing businesses are much more likely to view predictive analytics as a core capability than a low performing competitor. That those environments that embrace predictive modeling within their decision making are yielding big rewards. In the study "Strength in Numbers” by  Brynjolfsson, Hitt and Kim in 2010 of 179 large companies, those adopting data-driven decision making were seeing productivity gains of 5 to 6 per cent higher than those of their competitors. Although the percentage gains are commonly higher for small to medium organisations, 5 to 6 per cent is a massive chunk of change for a multi billion dollar fortune 500 company.

There is a long way to go for predictive analytics to be used in the same manner and sophistication that its used in other functions like marketing, sales and finance. How we are going to make decisions is going to change over this next decade and it’s effectively equivalent to a change in mindset moving from "I think" to "I know".

So whilst you can’t and would never replace intuition, when next making an important decision, consider testing it with predictive analytics.

It all starts with collecting the right data. At smarterhire we validate the feedback received by our leading exit interview and probation review products with historical data and use predictive analytics to ensure the validity of any patterns or trends. We provide people intelligence informing organisations about insights about their teams and individuals as well as actions or interventions our clients can take to produce a better outcome.

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If I was to ask you what type of coffee you like, the vast majority of you would say you want a dark, rich, hearty roast. Well Howard Moscowitz who has a doctorate from Harvard and is a Psycho-Physicist found during an extensive study commissioned by Nescafe that only 30% of us like a dark, rich, hearty roast. In fact the vast majority of us like weak, milky coffee. I bet if I ask most people, “would you like a weak, milky coffee” – very few would own up to it!
So what does this have to do with exit interviews and employee feedback? For years HR and OD professionals thought we could find out what made our employees happy, what would make them more productive, more likely to be retained, more likely to join us in the first place. What we thought we needed to do was just ask them. We’d run surveys, engagement, climate, culture, exits, probation, onboarding and focus groups and listen blindly to anecdotal feedback. More on why your retention and engagement strategies don't work