Concept: Attrition Prediction Reporting
Attrition Prediction can help you identify attrition risk on the segments in your organization. To learn more about how Peakon calculates attrition risk, see Concept: Attrition Prediction Methodology.
Peakon’s Attrition Prediction model:
- Can help predict costly turnover before it happens and delivers insights into the possible reasons why.
- Estimates the attrition risk for your employee populations in real-time, staying up to date by recalculating every time an employee submits feedback.
- Displays as an aggregated segment-level view, keeping the accuracy of your predictions high while protecting individual employee identity.
- Can assist you in optimizing your engagement strategy and preventing regrettable churn.
Navigation
The attrition heat map is available in the below areas for users of the
Administrators
access group. Administrators can also enable it for other access groups using the Attrition prediction
permission. Because the attrition heat map displays sub-segments of the context you're viewing, it's most useful for senior leaders.Area | Description |
|---|---|
Main dashboard | The main dashboard displays attrition risk in the Focus segments area for all segments except Separation date and Separation reason . Typically focus segments display on dashboards with a large group of employees. |
The Segments area | You can find the full attrition report in Analysis > Segments > Attrition . The report displays a column for each of the 5 key factors influencing attrition, as well as the Resigned column, for context. |
Interpretation
The risk level that displays on each segment in the attrition report relates to the
attrition risk of the segment relative to the average attrition risk of the wider organization
.Example: The
Marketing
segment is in the top 25% of attrition risk in your company.This example indicates that when Peakon compares the attrition risk of all segments in your company, the
Marketing
segment is in the top 25% group. This doesn't mean that the Marketing
segment has a 25% chance of an employee leaving, or that 25% of the segment's employees will leave (attrition risk displays on a segment level).The risk levels that apply to each segment indicate the predicted probability of an employee leaving within the next 6 months. However, we observe that leavers start displaying warning signs around 9 months before resigning.
Recommendations
If a segment has severe attrition risk, it doesn’t necessarily mean that those employees will leave. It simply means that in comparison to the rest of the organization there is a (much) higher risk of attrition. By showing even more diligence to this segment, you can play part in hopefully reducing the risk.
No matter the factors causing the attrition risk, we recommend using a holistic approach:
- Identify focus areas and try to understand the 'why' by reviewing comments.
- Review recommended actions and resources, and create a clear action plan that you communicate to the team. Example: Using the SMART framework can help you specify the goals. Ensure that actions are visible on shared and personal dashboards. In communications, connect any milestones and results back to the actions you took, closing the feedback loop.
- Have open and transparent communication with employees to ensure they understand how you're taking their feedback on board.
- Be clear on who can see the feedback, how Peakon aggregates scores and how comments work, to help alleviate confidentiality concerns. Employees may not provide meaningful feedback, or participate at all, if they don't have an understanding and visibility of how the system protects their confidentiality.
- Encourage Human Resource Business Partners to assist managers in discussing survey results in team meetings, and share their team dashboards.
- Enable the personal dashboard for your organization, so employees can track their own survey results and compare them to team and organization scores. SeeEnable Personal Dashboard for steps.
- For bigger segments, try using combination attributes to help identify the areas where you should prioritize efforts. Example: Combining a manually created employee lifecycle attribute with the location or job level attribute. See Create a Combination Type Attribute for steps.
- For segments that have a large number of new-joiners or low tenured employees, this is a critical time to show that your organization welcomes feedback and acts on it in a transparent way.
Examples
To learn about the factors that contribute to the attrition risk calculation, see Concept: Attrition Prediction Methodology. Note that these examples exclude the
Resigned
column, because the report only displays it for context.Consult with your legal counsel to determine whether your configuration of segments, and thus the attrition risk by segment, satisfies your organization’s compliance requirements. Customers can configure and provide instructions to their workforce on the use of this feature to ensure it doesn't directly or indirectly cause discrimination or discriminatory results, whether intentional or not. Customers are responsible for understanding and complying with any legal obligations arising from their use of the Attrition Prediction model and attrition risks, including any assessment, testing, or documentation that may be required under anti-discrimination laws.
Example 1
Segment | Risk Level | Tenure | Participation | Benchmark Difference |
|---|---|---|---|---|
Tokyo | Low |
|
|
|
The contributing driver scores are well above benchmark, so this group of employees is telling us that they would recommend the organization as a place to work, remain loyal, and see growth opportunities.
Although the risk level is low, it's not 'minimal'. This is likely due to a large proportion of employees being in either of the first 2 tenure phases, that typically have a higher chance of people leaving. The segment also has a low participation rate in the latest round, which could be concerning if it continues decreasing. Disengaged employees aren't interested in providing feedback or they don't believe their feedback matters.
Example 2
Segment | Risk Level | Tenure | Participation | Benchmark Difference |
|---|---|---|---|---|
New York | Above average |
|
|
|
The majority of employees have high tenure so they are more settled in. However, the contributing driver scores are only slightly above benchmark and only 2 thirds of the segment completed the latest survey.
It's possible that although employees with 2+ years of tenure have less attrition risk, in this segment they may simply be too comfortable to actively search for a new job, and are not necessarily engaged.
Example 3
Segment | Risk Level | Tenure | Participation | Benchmark Difference |
|---|---|---|---|---|
Auckland | High |
|
|
|
This example might look similar to Example 1 at first glance, however the tenure distribution of this example affects it more. Overall, this segment has 2 potential attrition risk drivers: tenure and participation.
One third of the segment is in the onboarding phase, which typically poses higher attrition risk. We know that employees in this phase often display higher signs of engagement, which tends to drop after the 3 month mark. All in all, almost half of the segment being in the first 2 tenure groups certainly impacts the attrition risk.
Additionally, the segment has lower participation rates, suggesting that a significant portion of this segment either don't feel that their feedback matters or they don't care enough to have a hand in improving the workplace. The segment does have a very positive difference to benchmark, for those employees who participate. This could be coming from the engaged new-joiners or content and loyal seniors.
Example 4
Segment | Risk Level | Tenure | Participation | Benchmark Difference |
|---|---|---|---|---|
Paris | Severe |
|
|
|
The 3 relevant drivers have a strong negative difference to benchmark. This group of employees is clearly communicating that they would not recommend the company as a place to work, they wouldn't remain loyal and that they don't see growth opportunities.
The segment has more than a third of its employees in the first 2 tenure groups, which also contributes negatively to attrition risk. All in all, this group of employees is not settled in yet, and they're scoring the key drivers well below benchmark.