Predicting employee burnout
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The document discusses a project that aimed to predict employee absenteeism using predictive analytics. It describes some of the key factors for the project's success, including having an interdisciplinary team and creating a large number of potential predictor variables from available data. The results showed that previous absenteeism, job evaluation scores, and feeling supported by colleagues were good predictors of future absenteeism. The model performed well at an aggregated level and can now be used and maintained by the company itself.
Predicting employee burnout
- 2. Figures of recent studies in Belgium show that burnout represents a huge potential cost to organizations and a huge personal risk for employees. (Calculated on capacity of the main room at DIS2017)
- 3. In this presentation we’ll cover the 4 main reasons for the success of the project
- 4. This looks cheasy, but there is much more to cracking the case than having a superhero data scientist. We had an excellent team of sponsors, domain experts, data experts, project managers and a data scientist
- 5. Since burnout is not registered in the data (privacy reasons), we predicted a proxy: unplanned absenteeism Within the whole team, we created a huge number of ideas for potential predictors. We were later able to turn 85% of ideas into data.
- 6. We benchmarked several algorithms, some more complex than others, but we always focused on presenting our results in a way that business experts understood technical experts and vice versa For this, we used for example predictor insights graphs (following slides)
- 7. 69% of employees had 0 to 4 days of absenteeism in the last year Starting to explain predictor insights graphs
- 8. Those employees who were absent 0 – 4 days in the previous year, were on average absent for 1.4 day during the next quarter As expected, previous absenteeism is a good predictor of future absenteeism
- 9. We also add the overall average number of days of absenteeism during next quarter
- 10. Low evaluation scores are related to higher future absenteeism
- 11. People who feel they have a backup tend to be less absent
- 12. The project was executed in the environment of SD Worx, so the scoring and monitoring of this model can be performed without external intervention We started the project with a non-technical training for the whole team about projects in predictive analytics and ended the project with a technical training for data scientists – how they can perform similar exercises autonomously
- 13. We noticed there is a lot of variation in individual absenteeism, but our prediction works very well when aggregated SD Worx is able to roll this out towards their current clients without external help