Speaker's blog: What is employee engagement good for?

by Andrew Marritt

Andrew Marritt is the founder of OrganizationView, a Swiss-based people analytics practice. He will speak more about the use of text analytics in HR at the HR Analytics online conference.

Greta Roberts, who I have a lot of time for, published an article yesterday called Employee Engagement? It’s Just a Meaningless, “Feel Good” Business Metric. Looking at our data and analysis, we disagree.

Greta describes Engagement as a middle measure. We would probably use the term ‘leading indicator’. I would argue both are emotional terms, but both are potentially accurate.

Article mentions 6 reasons, each of which I think are worth addressing:

1. Employee engagement isn’t the goal. Business performance is the goal.

I suspect a good parallel is that customer satisfaction isn’t a goal - sales is. Greta mentions that most businesses don’t and can’t link performance to engagement results. My view is that (a) I suspect more businesses could do this if they wanted - just ask their survey provider and (b) this argument is more a criticism of how engagement is measured and analysed than it is of the usefulness of measuring engagement.

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2. Justifying any kind of programme based on someone else’s research is a less than rigorous business practice.

OK, there isn’t much to disagree about this. Much of the published research has (a) been mostly correlations, written as to provide an impression of causation (b) written by consultants with a tool to sell. We've done a big literature review on this as part of a presentation to an industry group.

OK, so one could level this claim at us; however, we’d be more than willing to quantify the results with clients. I think it’s necessary to justify the ongoing use.

Are these studies useless? For the analyst they’re not. I would argue one of the ways of using them is as hypotheses to test. Testing others' findings in your own context is a valuable activity for analysts. In some ways, it’s one of the advantages of using a firm with deep domain expertise, like Greta’s firm or our own - we often know where to concentrate analysis resources.

I propose that you should be starting an engagement project with the objective to link engagement to performance. Design the intervention on that basis. Run an experiment, but certainly capture the data in a way to enable you to do the analysis.

3. Rigorous analytics often show little or no correlation between high engagement and an increase in business performance or a decrease in turnover.

This conflicts with what we’re seeing from our data and analysis.

Now, let me be clear, there is muddied water between the relationship between engagement and business performance. The key issue is that whilst we see engagement linked to performance, we also see employee populations of higher-performing companies more engaged.

There are a few ways of doing this analysis to disentangle the results. First, you need to capture engagement on an individual basis. In truth, most survey firms do this. Most surveys are confidential, rather than anonymous. Whilst we don’t report or analyse groups below a certain number, we do have the ability to do analysis using linked data. It’s pretty easy to tune ML algorithms to do this and also reduces the chance of overfitting.

A simple test to see if your survey is really anonymous: if your survey doesn't ask employees where they work, they’re almost certainly linking the perception data to demographic data later because you'll need to link to function, etc. for reporting purposes. There is no reason you couldn’t link individual performance data from a CRM system, for example, this way.

Second, as engagement data is captured more regularly, it’s becoming more valuable to analyse as a time series. The additional frequency is helpful in identifying the order of events – i.e., which comes first, the engagement or the performance? We could use such patterns to infer causality. In fact, when a presenter from IBM was questioned on how they had identified causality to engagement during a presentation I chaired at People Analytics 2015, it was via time series data.

One of the reports we provide for clients with Workometry is the rates of which employees are shifting between engaged and disengaged states. We do this by looking at patterns of engagement on an individual level over time.

An earlier one of my posts used systems dynamics to explain why this is so important. I argued that it wasn't the amount of engagement that is important, but the rate that the business was disengaging people. Either way, you need to be measuring engagement to get this.

You can find the complete blog post with all six reasons here.

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