True Benchmark analyses the demographic makeup of teams and slightly adjusts the benchmark based on what the expected engagement levels should be of a team of this type. This avoids misleading conclusions on engagement trends based on the specific demographic makeup of your teams/departments.
For example, comparing a team of apprentices to a group of highly tenured employees could lead to misleading conclusions. The True Benchmark will factor in location, age, seniority level, department and tenure and correct for biases caused by these factors. For configuration of True Benchmark, refer to Configuring the benchmark settings.
This article will contain:
How it works
Peakon has standardised data across industries about the key demographic attributes, making it possible to calculate how the typical driver answer varies with attribute values.
The below table contains the attributes which can be used for True Benchmark adjustments:
| Adjustment | Description |
|---|---|
|
Tenure |
Tenure can produce substantial variances in score. Scores in the first 6 months of tenure trend almost 2 points than scores of employees with 3-5 year tenure. |
|
Age |
Different factors affect employees at different stages of their life and career. Example: Factors affecting a 20 year old employee working in retail might differ to a 40 year old senior manager in the headquarters. |
|
Job level |
This category differentiates between employee, manager, senior manager, executive and director. Leadership teams often outperform the Strategy driver benchmark, because they build the strategy in the company. However, the benchmark mostly contains scores from the operational workers. |
|
Gender |
In most cases, gender provides little variance in the score but it can provide insight into areas like career growth and reward. |
|
Department |
Different departments may be generally more or less engaged than others due to the varying skillsets and experiences. |
|
Local office |
This accounts for differences in score across different regions based on global trends. Example: A broadly servile attitude to the workplace in APAC leads to disproportionately high scores. |
There are two things that affect the True Benchmark correction:
- The strength of the attribute effect: for instance, different tenure levels result in a big difference in average answers for most drivers, whereas gender generally doesn’t have a big effect. This means that tenure will often drive greater differences in True Benchmark compared to gender.
- The degree of difference between the company or segment attribute distribution, and that of the benchmark: if a team/company has far fewer low tenure employees than the benchmark contains, the corrections applied through True Benchmark will be higher. If a team/company is somewhat representative of the average the corrections will be small.
The key point is that corrections are made when the segment is very different from the benchmark composition. This feature makes intuitive sense - for example, almost every company has some low-tenure employees - it’s only when you have particularly many or few that a correction is required. In practice, most benchmarks will not move by more than +/- 0.2.
Once you have enabled the attributes and chosen your industry benchmark, you will notice that the benchmark displayed on your dashboard will now include the True Benchmark. In doing so, all dashboards throughout your organisation will also gain access to True Benchmark.
When a manager clicks on the True Benchmark icon for the first time, they will receive a short intro tour that will explain the concept. To enable True Benchmark, see Configuring the benchmark settings.
Viewing the True Benchmark adjustments
The benchmark score is available directly in the dashboard next to the engagement score. Clicking on True Benchmark will reveal the below slide-out panel, containing details of the benchmark source, as well as any adjustments made. The top of the panel will always indicate the final adjusted benchmark, which is also visible in the dashboard.
This can be done on any dashboard, whether it is a driver or a segment dashboard.
In the below example, the Technology industry engagement score is 8.0. Expand this section to learn more about the makeup of this industry, based by size and location.
To learn more about a specific adjustment, expand the relevant attribute. In the below example the Tenure attribute is expanded, showing how much each segment has affected the adjustment on the original benchmark score, based on team distribution.
Comments
0 comments
Article is closed for comments.