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 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.

This article focuses on using the results, and includes:

• Navigation
• Interpretation
• Recommendations
• Examples

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. Because the attrition heat map displays sub-segments of the context you're viewing, it's most useful for senior leaders.

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 the attrition risk of all segments in your company is compared, 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. See the report for more: The 9-Month Warning: Identifying Quitters before It's Too Late.

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 priority drivers and try to understand the 'why' by reviewing comments.
• Review suggested 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 scores are aggregated 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.
• 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.
• 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 Attrition Prediction methodology. Note that these examples exclude the Resigned column, because the report only displays it for context.

Example 1

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 they will be heard. 

Example 2

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 two 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

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

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.