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Predicting employee burnout

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Page 1: Predicting employee burnout
Page 2: Predicting employee burnout

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)

Page 3: Predicting employee burnout

In this presentation we’ll cover the 4 main reasons for the success of the project

Page 4: Predicting employee burnout

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

Page 5: Predicting employee burnout

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.

Page 6: Predicting employee burnout

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)

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69% of employees had 0 to 4 days of absenteeism in the last year

Starting to explain predictor insights graphs

Page 8: Predicting employee burnout

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

Page 9: Predicting employee burnout

We also add the overall average number of days of absenteeism during next quarter

Page 10: Predicting employee burnout

Low evaluation scores are related to higher future absenteeism

Page 11: Predicting employee burnout

People who feel they have a backup tend to be less absent

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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

Page 13: Predicting employee burnout

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