Not only can data help you understand why people are leaving your organization, but it also gives you the opportunity to address issues in the future more proactively. By linking your employee feedback data with your churn data, you can get a sense of what factors might drive people to leave your organization in the future and where problem areas (e.g. departments, functions, or demographics) might arise in your organization.
If you want to look at why people are leaving your organization, consider using the following four-step, data-driven approach for understanding and addressing turnover and churn.
1. Clean your data and focus on the right people
First, you need to clean your data and ensure that you have the right people in your data set.
You don’t want to muddy this data with people who left involuntarily because of low-performance, or who were moving countries or retiring. The data from these folks can look very different from people who chose to leave of their own volition. In fact, they often look very much like any other person in your company, including the ones who stay. Thus, including people who left involuntarily can confuse your prediction models.
To create your basic data set, the first step is to look at your exit survey results and to make sure you have the data you need. Exit survey data should be able to tell you who has left. It should also indicate, at some level, whether the departure was voluntary, or not. If it was voluntary, then that person may be considered a regrettable leaver. If you don't have exit survey data, data from your HRIS may also work.
No step is more important in this process than cleaning your data by removing involuntary leavers from the data set.
2. Include a broad range of connected data
For those who left voluntarily, you can often see major trends in exit data.
For example, does the exit data show that many people are leaving for similar reasons, or that similar roles or demographic groups are exiting more frequently?
You can then connect this data with their previous survey responses and feedback. It’s important to find all the information that may be connected to your turnover data. Consider what other data you have about them - their age, their training profile, the promotions they’ve had. Then, couple this information with their feedback or survey data to look for connections.
Feedback data can be quite telling of how an individual was feeling early on. For example, a good predictor of turnover is asking people when they think they’ll leave, or if they can see themselves in the organization in two years’ time, the latter of which is one of our recommended employee engagement questions.
People often tell the truth when they give that feedback in surveys, so if someone says they might leave - they’re actually more likely to. This holds true for at least a year.
When looking at your survey data, there are four other specific areas we always suggest considering in your analysis - leadership, learning & development, alignment, and salary.
3. Use the right statistical techniques to identify patterns
To illustrate the statistical challenge, consider that for many companies. only 10-20% of people might churn in any given year. Out of that 10-20%, you only want to understand those who left voluntarily and regrettably. That’s why you need statistical models that can predict relatively rare outcomes.
Recently, we had a large dataset that included thousands of people who’d left an organization. We tried several different methods to look through the surveys - random forests, decision trees, logistic regression, and other algorithms. Ultimately, we found that random forests were the most effective for this type of work.
A random forest is an extension of a decision tree. Essentially, it contains multiple decision trees. So, instead of finding one tree, this technique finds a multitude of the best trees and combines them to predict an outcome. Random forests are quite good at picking up nonlinear effects and unusual combinations of things that are predictive - although they can be hard to interpret.
Other techniques we've found to be useful are survival analysis and sampling procedures such as the ROSE technique (that stands for Random Over Sampling Examples). These types of procedures boost and adjust your training data for the smaller number of churn cases to help your model.
These aren't tools or techniques that everyone is familiar with, but our People Science team are happy to assist anyone who has questions about using them. However, just looking at simple differences in how the regrettable churn groups and the people who stayed responded in previous surveys can reveal pretty powerful insights.
4. Address solutions at the group level, not the individual
Rather than predicting whether a specific individual is going to leave your organization, try to identify and address issues at the group level. For example, if your models suggest a certain role (e.g. Sales Managers or Engineering Managers) are at risk, you might carefully examine retention questions for that group and act accordingly. In that sense, this data can be very powerful, as it makes it possible for you to find at-risk groups within your organization that you can help.
However, the worst thing you can do is start targeting individuals that you predict will leave. If you use data to predictively target individuals, you risk making a big mistake. People may feel targeted or believe that you’ve been looking at their personal data. This has the potential to damage your credibility and that of your feedback process. Individuals who may have chosen to stay may also change their minds as result. There’s no worse outcome than turnover predictions becoming a self-fulfilling prophecy.
The best predictors of churn are often simple
The best predictors often include the most obvious questions. For example, one of our standard benchmarked questions simply asks people if they can see still see themselves at the company in two years’ time.
Surprisingly, a lot of people respond very honestly to this somewhat direct question. Across thousands of companies, we’ve found that people who say they can’t see themselves at the company in two years’ time are 2.6x more likely to leave within the next year. Hence this question can be considered a powerful predictor for turnover, regardless of the specific statistical techniques we use.
To illustrate this point, below you can see some real data from an anonymous company showing the percentage of some key groups of people that said they could see themselves at the company in two years’ time.
Overall you can see that over 75% of those who agreed or strongly agreed with the statement actually stayed. On the flip side, over 50% of individuals who strongly disagreed and about 35% of individuals who disagreed with the statement actually left the company.
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