Peakon’s predictive analytics helps to predict costly turnover before it happens and delivers insights into the possible reasons why. The Attrition Prediction model estimates the attrition risk for each of your employee populations in real-time, which is recalculated every time an employee submits feedback. The aggregated, segment-level view keeps the accuracy of your predictions high while protecting individual employee identity. The ability to predict departures in your organization is extremely valuable, as it allows you to optimize your engagement strategy to address issues in high-risk groups and prevent regrettable churn. Secondly, it allows you to take a proactive approach to recruitment and plan ahead for new hires that will be needed throughout the year.
Check out our Heartbeat report: The 9-Month Warning: Identifying Quitters before It's Too Late.
This article covers:
- How the attrition risk is calculated
- Enabling Attrition Prediction
- Improving the prediction accuracy
If you are looking to report on attrition risk, see Reviewing your attrition risk forecasts.
How the attrition risk is calculated
The Attrition Prediction model is trained on hundreds of thousands of Peakon employee data points, that include both behavioral and demographic data. This model takes into account these six key factors for each employee and general trends to indicate a high risk of departure:
| Recommendation |
Response to the eNPS question: “How likely are you to recommend [your organization] as a place to work?” Employees who provide a 0-6 score to our eNPS question are more than 3 times more likely to resign than those who score the question 9-10. On average, employee engagement declines by as much as one point during the first two years of employment, regardless of whether the employee has any intention of leaving. This is expected and the decline is likely to level-out after two years of tenure. It’s natural that employees are most engaged when they first start a new job. Ideally, employee engagement wouldn’t decline at all but the fact that it does should not be alarming. Employees who have many years of tenure in a company would also have gone through this process. |
| Loyalty |
Response to the eNPS loyalty question: “If you were offered the same job at another organization, how likely is it you would stay at this organization]?”. |
| Growth |
Response to the Growth driver question: “I feel that I’m growing professionally.”. This question relates to an employee's perceived opportunities to improve their personal and career growth. Enabling this question will contribute towards a higher quality prediction. |
| Responsiveness |
Engagement survey response rate. The survey response rate of employees that resign from a business is 15% lower than that of employees who remain. Employees that have become disinterested in answering the survey have also become disinterested in trying to improve their workplace and therefore are at a higher risk of leaving your organisation. Improving the participation rate mechanically will not lower the attrition risk unless feedback is also actioned. If employee feedback is left unactioned it can actually lead to an elevated desire to leave. Therefore, it’s crucial to mobilise an active feedback loop between employees and your organisation. Adopting this method results in active survey participation being a strong predictor of lowered attrition risk. |
| Tenure |
Time spent at the organization. Employees in the 3-12 month tenure bracket are most likely to leave an organization. This is also when the biggest drop in engagement occurs - after 3 months of tenure. This presents a paradox, as on the surface you will have an employee with higher engagement, relative to other tenure segments, but who is also more likely to leave your organisation. It’s therefore crucial to factor length of tenure into the attrition risk calculation as relying solely on engagement is not an accurate indication of attrition risk. |
| Past resignations |
Number of employees already resigned. The attrition model utilizes engagement trends based on the employees marked as "Resigned" within the Separation Reason attribute. It is recommended to use this attribute and the "Resigned" option for any employees who have left voluntarily, to enable a higher quality attrition report and prediction. |
Attrition risk levels
The attrition prediction model presents a segment's attrition risk relative to the rest of your organization. The risks are displayed within your segment heatmap and employee cycle reports, as six distinct risk brackets, each indicating a different level of concern for leadership:
|
Risk |
Meaning |
Manager action |
Recruitment strategy |
|
Severe |
The risk of attrition in this segment is in the top 10% of your organization. |
This requires urgent attention. Address the priority areas highlighted in the segment overview immediately. |
Expect to have to replace employees in this segment in the immediate future. |
|
High |
The risk of attrition in this segment is in the top 25% of your organization. |
This requires serious attention. Address the priority areas highlighted in the segment overview immediately. |
Expect to have to replace employees in this segment in the near to immediate future |
|
Elevated |
The risk of attrition in this segment is in the top 50% of your organization. |
This segment requires attention. Consider the priority areas highlighted in the segment overview when building your action plans. |
Departures are likely. Factor this segment into your recruitment plan |
|
Reduced |
The risk of attrition in this segment is in the bottom 50% of your organization. |
This segment requires some attention. Consider the priority areas highlighted in the segment overview when building your action plans |
Departures are possible. Factor this segment into your recruitment plan |
|
Low |
The risk of attrition in this segment is in the bottom 25% of your organization. |
Engagement in this segment is high. Continue to monitor its performance to ensure attrition risk doesn’t increase. |
Departures are less likely. Prioritize other hires in your recruitment plan. |
|
Minimal |
The risk of attrition in this segment is in the bottom 10% of your organization. |
Engagement in this segment is excellent. Continue to monitor its performance to ensure attrition risk doesn’t increase. |
Departures are unlikely. Prioritize other hires in your recruitment plan. |
Enabling Attrition Prediction
Attrition prediction is enabled by default for all administrators (the Administrator access control group). To enable this for other users within Peakon, administrators need to follow these steps:
- Click on Administration in the top menu bar.
- Select Access control.
- Select the Access control group you’d like to enable it for.
- Under the Access statistics permissions toggle on the Attrition prediction feature.
Improving the prediction accuracy
Peakon’s Attrition Risk model works most accurately when the following is implemented:
- Increasing active survey participation whilst also actioning feedback.
- Moving to a higher survey frequency to identify trends and risks earlier - the more recent the answers, the more accurate the prediction of current risk.
- Enabling Peakon’s standard ‘Loyalty’ question.
- Collecting leaver data using Peakon’s Separation date and Separation reason attributes
The above points are not required for the Attrition Risk feature to work. However, enabling them will improve your score accuracy and contribute to the overall aggregated data set used in the model.
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