Employers, can you figure why your people leave?


The decision to quit a job is a very important and complicated one.  How do employees take this decision? Are there set patterns of behavior on employee attrition? Can employers decipher these patterns, and use them to reduce attrition? I believe, based on my work with some leading companies, employers who are smart enough to ask the right questions about their employees will be able to predict their employees’ behaviour, sometimes even before the employees themselves realize that they would eventually be making a decision on their employment.

There are so many reasons why employees leave a job. The common hypotheses that I have heard making rounds in various companies are that of high performing employees leaving the job due to inadequate recognition or compensation, employees being in the same role for a long duration leaving the job due to lack of growth opportunities, employees not being able to work with their first line reporting quitting the job, employees unable to manage work-life balance quitting their work to concentrate on life, etc… While the above is a small list, every HR department would have hundreds of such hypotheses. Which among the above list are correct? Most of them seem logical; then does it mean that they are all true? The best way to separate out truth and perception is to collect data and statistically prove or disprove them. With the advent of Big Data Analytics collecting diverse data from within and outside the company and arriving at meaningful insights has become a lot easier.  Big Data Analytics can tell us which two or three top reasons contribute the most and which hypothesis that we had is actually wrong. This will help HR executives focus their energy on high-yielding projects.

The approach to come up with the best predictive model for explaining attrition varies by the industry and I have seen it vary across companies in the same industry as well, depending on their operating model. The first step in coming up with a predictive model is to list the data that needs to be collected. Companies mainly depend on surveys such as employee satisfaction surveys and exit interviews to gauge the reasons on why employees quit. While they provide useful insights they are limited by the one-size-fits-all approach that is taken for such surveys. Adding to the fact is that overall satisfaction could be one of the many factors which could explain employee attrition and not the only factor that would determine employee attrition. Existing studies indicate that “age, tenure, overall satisfaction, job content, intentions to remain on the job, and commitment are consistently and negatively related to turnover. Generally, however, less than 20% of the variance in turnover is explained.”

Other than the ones listed above, I see there is lot of industry and context specific ‘employment’ attributes that could affect employee attrition. The most common factors among them include compensation and employee performance within the company. There are quite a few environment related attributes that contribute to employee attrition as well, such as industry growth, availability of skills and time taken to train new employees.

Once the data is collected, we should look at correlating it with the employees and alumina and see the differences. There are a few predictive analytics tools which can help churn data and decipher the actual reasons behind employees leaving. The tools themselves decide on which data points are more important and which don’t contribute to any predictive insights. There are various kinds of predictive models that could be developed. Decision-tree based predictive models are easy to understand and provide a rule based output which can be implemented easily. Sometimes the need is not necessarily which employees will attrite but the attributes of employees who attrite, the attributes when put together can provide us the answers on why they attrite.

In particular, a marketing firm that we did work for, the employer was highly worried about high performing employees leaving work. The predictive analytics engine on the contrary came up with rules saying that high performers were many times less likely to leave the organization. The causality could be explained by the fact that they were comparatively very well paid to other employees. The rule also indicated an abnormal amount of new employees who were falling into the low performance category and leaving their organization at a very early stage.

It was pretty clear for anyone seeing the results of the analysis that the company was not concentrating on developing skills in new employees and giving new employees adequate time to pick up the skills needed for high performance. The new employees sensing the bad environment attrite even if the company is ready to retain them for a longer duration with them being tagged as low performers. Our recommendation to the employer was to come up with a different set of performance measures specifically for new employees and increasing the duration between their joining and the first evaluation. Sometimes we have to go ahead and give diametrically opposite recommendations to the organizations than what they were expecting, because data, when asked the correct questions, always provides the correct answers.
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