## 20 Metrics in 20 Days- Day 16: Team-level Engagement

Day 16 in our series of 20 consecutive posts on HR metrics: Team-level Engagement

## Definition

Team-level Engagement as defined here is just the average employee engagement score across a team (defined by a common manager/supervisor).

Note: You can of course look at other team-level aggregate measures too like the median, standard deviation, etc. The key is that your unit of analysis is the team, not the individual.

The use of engagement scores can vary substantially by organization, but one common use is to aggregate engagement scores by team.

This can provide leaders of those teams as well as senior leadership the opportunity to assess the quality of management and identify a path forward for better engagement.

Getting quality engagement scores can be tricky though because some employees fear those scores could be used against them, despite assurances that individual scores will not be revealed. In addition, some employees might be low on engagement even with a great supervisor because of overarching company culture issues or problems with the future direction of the company.

This can be particularly true in cases of a takeover where employees face an uncertain future.

In other cases, employees might give high engagement ratings to protect a manager they really like even though they are unengaged because of broader issues outside their manager’s control.

## Example in R

For Excel users, aggregating results by team can be handled in a simple pivot table.

In R, we can accomplish this in multiple ways but I think the easiest is to use our old favorite, the aggregation function.

Let’s create some data and show the basic steps; these are not meant to be realistic scores, just illustrative. For this exercise, let’s assume we have a 5-point engagement scale.

In addition, we’ll assume each of our individual employee scores are averages of their individual engagement survey responses.

In this example, we are using the formula in the aggregation function (eng ~ team) to split engagement by team. Then, we apply the mean function to those engagement scores.

```set.seed(42)

team <- rep(c(1,2,3), each = 7)
eng <- c(4, 4, 4.2, 4.2, 4.4, 4.6, 4.6, 3.5, 3.5, 3.7, 3.7, 3.7, 4.1, 4.3, 2.6, 2.7, 2.8, 2.9, 3, 3.1, 3.2)

df <- data.frame(team, eng)
aggregate(eng ~ team, data = df, FUN = mean)```
```##   team      eng
## 1    1 4.285714
## 2    2 3.785714
## 3    3 2.900000```

## Why You Should Care

• Engagement is a huge topic and it is one of the few measures that appears to be consistently predictive of both turnover and profitability.
• Provided the teams are of reasonable size, the team-level engagement scores can help you identify what managers are likely doing something right and who could use some improvement.
• Tracking team-level engagement across the whole company can help you determine the extent to which it tracks or even predicts other measures such as turnover. You may need to dig in deeply here to find the ultimate story but start with the high-level observations first.
• Team-level trends can reveal who is on the right path and who is headed in the wrong direction.

## Actions

• Talk to the leaders with the best engagement and figure out what they do. Every organization is different so capitalizing on local knowledge is key.
• Work together with senior leaders to get actionable plans in place to help drive engagement where it is low.
• Be open to experimentation and don’t expect changes overnight.
• Engagement action plans can fail because of design, implementation, or measurement.
• Be thoughtful and don’t overpromise here; every company is different.
• Track high-level relationships between engagement and other indicators like turnover, retention, absenteeism, or willingness to engage in learning opportunities. The basic question you need to be asking “Is our engagement measure actually related to anything that matters for our business?”