Employees leaving hits companies hard. With costs between 50% and 200% of a year’s salary to replace a team member who left, the economic damage is considerably high. But how can you tell that somebody is likely to leave? In this article, we explore this question more in-depth and introduce our own method of using people lifecycle indicators (PELI).
According to a study by Havard Business Review and Utah State University there are pre-quitting signs and behavior patterns that we can be observed. These signs can be compared to poker players who have ticks when they have a good hand or a bad hand. Got poker players can „read“ others and make reasoning based on their observations. In HR it is the same. Good managers can tell when there are signs of somebody leaving or they use tools to support them (like we do with or People Horizon AI). According to SHRM the 13 signs that somebody will quit are the following:
Now what we know signs that show that people might quit, it must be simple to tell when somebody quits, right? It is not that simple because on sign alone does not help yet. Productivity could also go down because a new tool is introduced in your company and not because somebody wants to quit. So we need to have some sort of framework that ties it together. For that reason, we created the PELI framework.
The PELI framework (People Lifecycle Indicators) aims to put numbers between 1 and 4 to six summarizing dimensions. These dimensions can be seen as indicators and we can generate them with data. The six PELI dimensions are:
We can illustrate these six dimensions in a radar chart that shows certain imbalances as „dents“ in the chart.
We can map the 13 signs that somebody will quit to the six dimensions of PELI. For instance, let us take Sign 3: The employee has been doing the minimum amount of work more frequently than usual. This can be generally measured with the performance indicator and with the environment indicator. Doing the minimum often correlates to performance and has an influence on how others perceive an employee’s work.
Now that we established these six dimensions the question is how to put a number to them. We need data for that.
In our People Horizon predictor AI, we utilize already existing data in your organization to feed into the six dimensions. For example, you can tell by the email frequency or Slack messages how involved people are. But other tools like surveys and interviews are also generating data that feeds into the model.
At People Horizon we carefully assess what data you have at hand to predict which of your employees might leave next. With our decentralized bring-to-code-to-the-data approach, we ensure that data privacy is guaranteed. Data never leaves your premises.
Want to learn more? Get in touch with us!