Slides of a very short inspiration session of the use of AI in an HR context for Agoria, Feb 12 2021. In this session, we described our collaboration with SD Worx on using algorithms to reduce absenteeism. We explained how some of the most powerful AI applications have clear value in an HR context. We showed the highlights of our success cases with SD Worx, where we used Predictive Analytics and Segmentation techniques to predict, understand and reduce future employee absenteeism.

1. How algorithms help
to reduce absenteeism
Agoria AI Inspiration Session
Geert Verstraeten
Feb 12, 2021

2. The hype around AI
There’s a lot of buzz
about AI in general,
also in an HR context

3. We don’t need
more buzzwords
We need more
impact!

4. Why absenteeism?
6,33%
8,21%
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Absenteeism in Flanders + 30 %
Source: https://overheid.vlaanderen.be/bedrijfsinformatie/ziekteafwezigheidspercentage
9,2
12,5
0
2
4
6
8
10
12
14
2009 2019
Avg Duration
+ 36 %
Average 'direct
cost' of
absenteeism in
Belgium:
€ 1.000
per employee per
year

5. At sdworx, reducing
absenteeism is an
opportunity to help
their clients

6. Prediction
Prediction is like
playing a game of
Guess Who – you
narrow down until
you understand the
exact profile at risk

7. Building predictive models
https://www.linkedin.com/pulse/predictive-analytics-soup-story-geert-verstraeten/
Building models is
much like creating
soup

8. 69% 22% 10%
0 - 4 5 - 14 15 - 230
last year absenteeism
group size
@vincentvanlook @pythongeert
Predictors
This company has
10% of employees
with high
absenteeism last year

9. 69% 22% 10%
1,4
3,7
7,3
0 - 4 5 - 14 15 - 230
last year absenteeism
group size
group future absenteeism
@vincentvanlook @pythongeert
Predictors
The employees who
were absent last year
are also those who
will be absent next
year

10. 2% 16% 49% 7% 26%
8,1
4,5
2,0
1,3
2,1
0 - 1,0 1,1 - 2,0 2,1 - 3,0 3,1 - 4,0 undef
last evaluation score
group size
group future absenteeism
@vincentvanlook @pythongeert
Predictors Employees who
receive a poor
evaluation score
will be much
more absent
Many other
variables play a
role, and it is key
do combine them
into an optimal
combination

11. @vincentvanlook @pythongeert
First conclusions
Payroll data is crucial in predicting absenteeism
Prediction of absenteeism is difficult on individual level,
But works great on a team level
The exact recipe should be tailored to the context
You cannot simply copy the
algorithm of one company
and apply it to another

12. Segmentation
There are clearly
different groups in our
workforce, let’s not
make a ‘one-size-fits-
all’ prevention
approach

13. Detecting segments Segmentation
-2
-1,5
-1
-0,5
0
0,5
1
-2,5 -2 -1,5 -1 -0,5 0 0,5 1 1,5
Workload
Satisfaction
Engagement
Visualization of
a solution with
5 segments

14. An iterative process Final Segments
Project definition
Collect data
Build clusters
Profile clusters
Business review
1
2
3
4
Creating the final
segmentation
scheme is an
iterative process
and close
collaboration
between the data
scientist and the
business

15. I speak with pride of my work
I’m in job content
Acceptable time on the road?
I always have care for my children
Politeness
W o r k i n g speed Work precision
Reason for this job?
I like it!
Good work/life balance
I can choose my own schedule
I can work close to home
Speaks Dutch
V
V
V
V
Segment profile Segmentation
Workload
Characteristics Engagement
Performance
For this segment it
is crucial that they
can plan their work
schedule

16. Strategic level
Operational level
Tactical level
From insights to:
This only
delivers value if
concrete plans
are created

17. Keys to success Cooperation between analysts and domain experts
Project definition is crucial
Realize the model’s power and limitations
Only add complexity if that adds value
Data and ethics can go hand in hand
Not data volume, but the right data
Algorithms support decisions,
they don’t replace them

18. Questions?
Thank you!