Burnout – how to measure the biggest threat to employee engagement with just four variables

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From 2014 to 2019, Greg was the Chief Storyteller at TrustSphere, this article represents work Greg was engaged in during that period.

Greg is currently the Client Experience Lead for Deloitte's Workforce Analytics and is still super passionate about all forms of analytics!_________________________________________________________________________

If I were to define the two holy grails of HR Analytics, it would probably be increasing engagement and decreasing attrition. Ask anyone from the business what they think the HR Analytics team should focus on, and I bet you these would always be at the top of the list.

Unfortunately, engagement and attrition are also two of the most complex challenges for a HR Analytics teams to crack. Anyone who has been following the growing world of HR Analytics will have seen loads of articles, experiments, algorithms and software, all trying to decrease attrition and increase engagement, yet globally we are long way from “fixing” either of these problems.

One reason that this is so difficult is that there are many, many variables that need to be fed into algorithms to try accurately measure these things.

My favorite variable that has been shown to impact attrition is the number of kids an employee has. I recently saw a presentation that showed that employees with seven or more kids would never change jobs. That’s right folks once you have had seven of more kids, no matter how much you hate your job, how much your manager annoys you, or how terrible your performance review results are, you will never quit. The risk of being unemployed is simply too great! I think that’s a great illustration of how many variables you would need to be gathering to predict attrition successfully.

My favorite engagement variable is an employee’s “beliefs and needs,” the fit between the companies values and beliefs and those of the employee are critical to an employee's engagement. The sad thing is that once a company identifies a mismatch in the companies and an employee's values, it’s a really big challenge to change the employee's beliefs to bring into line with the companies. It's even harder to try and change a companies values to match those of the employees!

Another big issue which makes these business problems hard to solve is that the traditional set of HR data that is used to try and predict them is very static. One of my former clients from my SAP days discovered that the average employee only has three master data changes per year – that means their HR data isn't just static, it’s virtually carved in stone. In reality it means that an attrition model that was run in May last year will show pretty much the same results if you run it right now because the underlying data hasn’t changed.

So that lead us to start looking around to try and find a significant business problem, where the number of variables that needed to be analyzed was limited and where the data is constantly changing and therefore always up to date.

Employee burnout is a massive business issue. For example, the health costs associated with employee burnout are a staggering $190 billion per year. It’s also a powerful factor that contributes significantly to both employee attrition and employee engagement:

• 50% of millennials, 40% of Gen X and 35% of boomers say burnout is motivating them to consider changing jobs (Staples)
• The biggest threat to building an engaged workforce in 2017 is employee burnout. The newest study in the Employee Engagement Series conducted by Kronos Incorporated and Future Workplace® found 95 percent of human resource leaders admit employee burnout is sabotaging workforce retention, yet there is no obvious solution on the horizon (Kronos).

The good news is, there is a ton of good research and publications that show employee burnout is caused by only a few variables, all of which are relatively easily accessible. Unsurprisingly most of these variables are visible in our email activity – given that we spend up to 600 hours a year reading, writing and sorting email, it’s no surprise that many insights can be gained from this single business activity.

Using email as a source of data provides one extra compelling benefit, unlike many other sources of traditional HR Data (which we know are basically carved in stone), the constant stream of emails means these variables are changing continuously. This means that the ever-changing signals can be quickly interpreted, change instigated and its impacts measured to ensure the risk of burnout is reduced.

As listed out below, the contributing variables are probably not that surprising; I wonder how many people would answer yes too many of these questions? If this sounds like you, perhaps it’s time to admit that you may be at risk of burn out?

The good news is that to gather this data; we don’t need to read employee’s email, no semantic evaluation is required, the assumed privacy of people's conversations is not breached, we only need to access the meta-data available from the corporate email system logs to create these insights.

Let's look at these variables to see what they infer:

1                  You send a lot of email outside of working hours

Again, by establishing a set of “normal” working hours for a team of people, we can identify the outliers who are creating and replying to a large number of emails outside the hours that their colleagues are active. By looking at the team's behavior as normal, rather than a traditional 9-5 working zone, we can again quickly identify the outliers.

2                  Your work is constantly interrupted by email interactions

The concept of “flow” is used to assess how “in command” a person is of their situation. The theory being that the more continuous periods of work without the interruption of email, the more time an employee is dedicating to fully absorbed focus. Most of us now recognize that email is an interruption to work, rather than a way of actually completing work, so we look to find employees who achieve very little flow in their day.

3                  You spend too much time collaborating

My favorite SNA guru Karen Stephenson has done amazing work identifying the impact of over-collaboration. Where historically we would have imagined that an employee should have strong relationships with all of their teammates, it is now recognized that this resulting level of over collaborating is actually hindering work, and contributing to employee burnout. 

4                  Your network is larger than your line managers

Most people prefer to work with a manager who is an effective gatekeeper, which is when your manager acts as a something of an intermediary between you and the rest of the network (more on this in a later blog). So when your network of strong relationships is stronger and more active than you line managers, that means you are having to perform as your own gatekeeper and possibly even acting as the unofficial manager of your team or department! 

By combining the data on all of these variables, you can see in virtually real time how at risk an employee is of burning out!

The great thing about the approach is that source data is constantly changing, so we can see when specific parameters are exceeded, allowing organizations to respond quickly to modify behavior (by reducing collaboration, activity outside working hours, increase quiet flow time, etc.). Crucially we can also measure the impact of mitigation activities, ensuring the fix is impactful, and the risk is reducing.

Right now we are offering this analysis as a one off a piece of consulting - with minimal involvement from the business and access to just two months worth of data; we can tell you where you greatest risks are. If this sounds interesting to you, please send me a message, and we can get started today!

If you enjoyed this...

Please check out some of my earlier blogs related to my favorite subject, Organisational Network Analytics:

How can we better identify HiPos using network data?

Why Informal networks are set to revolutionize HR and People Analytics.

It’s time to start eating HR data for breakfast….

Who is Greg Newman?

Greg spent 15 years trying to restore a 1956 Morris Minor and when time permitted, building global HR systems, over time he became increasingly frustrated that HR technology was focused on doing what was easy, rather than what was important.

From 2014 to 2019 Greg was responsible for the People Analytics product development at TrustSphere, pioneers of Relationship Analytics. TrustSphere are focused on delivering HR analytics that businesses can use to increase their bottom line.