Fixing employee engagement (and why we’ve been doing it wrong)

According to Josh Bersin, businesses spend USD 1 bn on employee engagement. We’ve been measuring engagement for the last 15 years. During this time engagement figures have been flat or declining. Why so little return on our investment?

I’m not going to question the concept of employee engagement. Yes, there are issues including a lack of commonly agreed definition but I don’t believe that this is the issue. Regardless of how you define engagement it doesn’t seem to have improved.

My view on why engagement has been so flat is because of the way the research-to-action cycle has been conducted. Basically, because the measurement & analysis approach has been flawed the actions have been misguided.

Systems dynamics to the rescue.

There may be some readers aware of systems thinking. Peter Senge who wrote about the ‘learning organization’ is part of the systems group at MIT. He’s also an aerospace engineer. This is important.

Last week I had a long conversation with the wonderful Alistair Shepherd or Saberr about viewing organizations as systems. Alistair is an aerospace engineer. Notice a pattern?

When I teach People Analytics the section about applying Systems Dynamics to workforce issues is always the most popular part. People are amazed at how the complex patterns we see in organizations can often be predicted using quite simple rules and models.

Engineering, and especially aerospace engineering is heavily systems based. It gives engineers a different way of viewing the world.

The two basic components of a dynamic system are stocks and flows. A simple explanation is a bathtub. If water is coming into the bath quicker than it is leaving the bath continues to rise, if the rate of outflow is greater than the rate of filling it empties. We can have stocks and flows of pretty much anything.

So what has Systems dynamics got to do with engagement?

With Workometry we’re in the fortunate position of capturing a very large amount of perception data. We have large, enterprise clients running monthly pulse surveys. We store the data in a way that enables us to study at the individual level up how perceptions change over time.

As analysts we built a systems dynamics model for engagement and plugged our data in. It worked, our data pretty much validated the model.

The great thing about systems dynamics models is that you can use them as simulators: make a change to the assumptions and the model will create predictions. We can therefore use it as a management simulator to understand what we need to change to improve what we care about – engagement.

How have we measured engagement?

Analysts tend to use a very simple approach to measuring engagement. Through a set of questions – an engagement index – we measure the number of engaged employees and divide this by the total employees and we get a rate.

If we step back and think about this model there are only two states – we treat people as either engaged or disengaged. With a systems dynamics model we can think of this as two stocks – engaged and disengaged employees, with flows defining people being recruited, leaving and moving from the engaged to the disengaged stocks and vice versa.

What does our data show?

  • Almost everyone joins the organization engaged
  • More people leave who are disengaged than engaged. Not only is this what the engagement literature predicts but it turns out to be necessary for the system to be stable
  • There is a rate by which engaged people become disengaged
  • There is a rate by which disengaged people become engaged. (it turns out that this must be lower than the disengagement rate for the model to be stable and the data certainly shows this)

The model also predicts that in periods of recession, when fewer people voluntary quit an organization, we see engagement levels fall. This is exactly what we saw in the last recession.

How have we been doing analysis?

At a high level we can think of an engagement survey as having two parts. First we ask a series of questions to create an engagement index. We then ask a large number of questions about various factors we think will be linked to engagment.

Traditional analysis uses a bunch of statistical techniques to work out what factors seem to drive engagement. What we’re effectively doing is seeing what factors most closely link with engaged / disengaged employees. We then build action plans based on this analysis

The problem with this analysis, however good your statistician, is that it is static. We take a cross-section of the data at one period. We’re looking at the differences between people who are engaged and those who are disengaged. We haven’t looked at what has changed an engaged employee to be disengaged. Basically we’re looking at the wrong thing and our action planning is therefore misguided.

What should we be doing?

It turns out the model, validated by our data, makes a very simple recommendation – the most important aspect to focus on is changing the rate by which you’re disengaging previously engaged employees. The only other reliable way of changing engagement is to encourage more disengaged people to leave.

Our model shows a very interesting and non-intuitive finding. If you do something to change the numbers of engaged and disengaged employees (our current post survey actions) the results will inevitably be short-term. You always return to the same engagement rate after a few periods!

The only reliable way of shifting the engagement rate is by changing the rate by which you disengage employees. Our traditional, cross sectional analysis is useless at determining this. We spend vast amounts of resources making big changes which are doomed to failure.

How people change their levels of engagement

What’s fascinating is seeing how people change from engaged to disengaged. We have found two generic patterns

  • The Faders: These employees move from engagement to disengagement gradually over a period of around 3 or 4 months.
  • The Fallers These employees move from a state of engagement to disengagement in one month. Often they’ll move straight from a high engagement score to the lowest in one month and the next month ‘bounce’ to a low, but a bit higher, state.

As predicted by the model we see few people who were disengaged becoming engaged.

Recommendations for fixing engagement

Fortunately the changes we need to make in engagement research are reasonably small:

  • Conduct frequent pulse surveys to be close to the change
  • Do longitudinal (sometimes called panel) analysis and look for switching patterns
  • Ask people about their experiences / why they changed their view
  • Identify factors that are related to these switching patterns
  • Act on these factors to change the switching rate.