Concept: Impact Metrics
Impact metrics enable you to examine the return of investment in engagement, by comparing driver scores against external metrics. You can import your metrics and contrast them against driver scores from any question.
These examples are some use cases for
Impact
:
- Attrition rate %
- Absenteeism
- Project completion %
- Sales growth %
- Customer retention %
- Other driver scores
- Customer satisfaction %
- Sales achievement %
- Mystery shop scores
How It Works
Linear regression enables
Impact
metrics to estimate the relationships among predictors and outcomes by fitting the best linear (straight line) relationship. The regression outputs these measures:
Measure | Description |
|---|---|
The impact value | The impact value is the regression coefficient that reports on the direction and effect between your predictor and outcome variable. It displays the expected change in your metric with a one-point change in your Peakon driver score.
The type of metric you're regressing against dictates whether your impact value is a positive or negative outcome. Example: A negative score when examining the relationship between attrition and engagement is a positive outcome, due to more engaged employees being less likely to leave. |
The explanatory power | The explanatory power (R-squared value in statistics) evaluates how well the regression model fits the real-world data you imported. It explains to what extent the variance of the first variable explains the variance of the second variable.
Academic research suggests that a value of ≤12% indicates low, 13% - 25% values indicate medium, ≥ 26% values indicate high model fit. The higher the explanatory power, the better the model explains changes in your outcome variable. These boundaries might seem low, but employee engagement is often one of many factors contributing to the success of a metric. |
Analysis
After importing your metrics, the graph displays a scatter plot with a trend line, the impact score, and explanatory power.
You can view the
Impact
report in the Analysis
section of your dashboard. The information on the graph is contextual and adapts the relationship depending on the segment context you’re viewing. It's possible to export your Impact
analysis to Microsoft Excel.When using available filters, the graph returns to the standard view after leaving the page.
Access
You can enable the
Impact
permission under the Access Statistics
section in Access Control
.Recommendations
Lead analysis with a research question to drive the narrative of your investigation. Examples:
- Is there a link between increasing employee wellbeing and reducing average absence days per employee?
- Do teams with higher workload scores also perform higher on productivity metrics?
- Do locations with higher engagement scores also have lower first aid incidents?
- Are more engaged teams providing better customer service?
- Are departments with lower growth scores more likely to have higher employee turnover?
Provide granular data from a higher number (>30) of smaller units.
Ensure that the employee data that each segment contains is independent and doesn't overlap.
Replicate the analysis often to improve the validity of the findings. Finding the same trends and effects over different time periods is an indication that the findings are robust and not coincidental.