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Can algorithms help to reduce absenteeism

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On October 2nd, Peter Beeusaert of SD Worx and Pieter Van Bouwel of Python Predictions shared our collaboration on Intuo.io’s Talent Enablement Conference. In this presentation, they explained how some of the most powerful data science applications have clear value in an HR context.

They discussed a recent joint use case ‘How data science can segment your employees to propose differentiated absenteeism treatment’ to demonstrate how bringing together data science and HR experts can improve and facilitate HR decision making.

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Can algorithms help to reduce absenteeism

  1. 1. CAN ALGORITHMS HELP TO REDUCE ABSENTEEISM Peter Beeusaert – Pieter Van Bouwel Talent Enablement Conference 2019
  2. 2. ARTIFICIAL INTELLIGENCE There is a lot of buzz about using algorithms in any context today
  3. 3. We don’t need more buzzwords We need more impact!
  4. 4. STRUGGLE TO GENERATE MAX VALUE OUT OF DATA Metrics Benchmarks Scorecards Surveys HR Reporting Added Value HR Intelligence Maturity Source: Investing in People, Wayne Cascio & John Boudreau, Dec 8, 2010, FT Press, adapted by SD Worx & Python Predictions In HR, there seems to be a ‘wall of Boudreau’ – most companies stop at HR reporting and do not engage in data science yet
  5. 5. TEXT ANALYTICS So which typical data science applications could work in an HR context? We could analyse textual information, for example to flag issues in employee satisfaction or classify CVs according to skills
  6. 6. RECOMMENDER SYSTEMS We could use recommender systems to make personalized suggestions of relevant trainings for every employee
  7. 7. PREDICTIVE ANALYTICS Or we could predict specific events, such as future absenteeism (see our previous case with SD Worx here).
  8. 8. HOW TO GET THERE Metrics Benchmarks Scorecards Surveys HR Reporting Added Value HR Intelligence Maturity Correlation Causation Prediction HR Analytics Source: Investing in People, Wayne Cascio & John Boudreau, Dec 8, 2010, FT Press, adapted by SD Worx & Python Predictions So we should probably move beyond reporting, to analytics
  9. 9. SEGMENTATION We’re sharing a concrete customer case around segmentation – in terms of absenteeism, we should not treat every employee as if he / she is similar to every other employee. Segmentation helps to understand differences in absenteeism
  10. 10. Average 'direct cost' of absenteeism in Belgium: € 1.000 per employee per yearAbsenteeism is of no interest to anyone: Not for the employer, but certainly not for the employee! ABSENTEEISM IN BELGIUM The past 10 years, total absenteeism increased by 40% 4.15% 5.81% 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 +40%
  11. 11. CLASSICAL APPROACH Drawing up an absenteeism policy Company X ❑ Notification procedure ❑ Notification call ❑ Medical certificate ❑ Contact while absent ❑ Control doctors ❑ Returning chat ❑ Reintegration ❑ Industrial doctor ❑ Reintegration agreements ❑ Frequent absenteeism interview ❑ Absenteeism coach ❑ Follow-up interview ❑ Signal call ❑ … Most companies have an absenteeism policy (‘one size fits all’)
  12. 12. Preventive working? PREVENTIVE WORKING Absenteeism policy Company X Absenteeism policy Company X To a large extent, these policies work on absence or recovery, not prevention
  13. 13. A good understanding of employees helps for prevention
  14. 14. ETHICS ● Surveys: inform and explain the goal to employees ● Individual information will NOT be shared to managers or the end customer ● Information is anonimised ● Items got an ethical/legal check ➔ even if they are relevant from an analytical point-of-view, they are removed if there’s a nogo! Of course there are some crucial ethical implications in an HR context
  15. 15. SEGMENTATION In segmentation, we aim to create groups of employees that are similar to each other, but where the groups are different We have analyzed the employees in a specific role in three companies in the same industry
  16. 16. EXPLORATORY Final Segments Project definition Collect data Build clusters Profile clusters Business review 1 2 3 4 IMPORTANT TO HAVE A GOOD METHODOLOGY Segmentation is an iterative process
  17. 17. FACTORS THAT IMPACT ABSENTEEISM Workload Engagement Performance & Talent reviews Employee Characteristics Absence in the past Employee Survey Manager Survey Payroll Data The data we used in the project
  18. 18. CLUSTERING -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 Clustering 1 An example of an initial result (bubble size represents segment size)
  19. 19. PROFILING EXAMPLE CLUSTER X 19 Physical charge Pleasure & energy Quality of work Too heavy Acceptable Low High Bad Good An example of how we profile the resulting segments
  20. 20. FEEDBACK BY BUSINESS Workload Satisfaction Engagement Clustering 1 Workload Satisfaction Engagement 2 Workload Satisfaction Engagement 3 Absence Evaluation Clustering 4 Segmentation is an iterative process
  21. 21. HOW TO ALLOCATE EMPLOYEES? Can we assign new hires or employees without survey info to one of the clusters? Absence Evaluation Clustering 4 ?? ?? ?? ????
  22. 22. DEFINE RULES Find key questions that can classify employees Absence Evaluation SURVEY ❑ Personality: politeness ❑ Work motivation ❑ -------------------------------- ❑ -------------------------------- ❑ Physical workload ❑ -------------------------------- ❑ Dutch speaking ❑ -------------------------------- ❑ -------------------------------- ❑ -------------------------------- ❑ Job flexibility is important ❑ -------------------------------- ❑ -------------------------------- ❑ ... Low absence High absence Example: ‘because I can choose my own hours’ We will show the detailed profile of one segment that suffers from absenteeism
  23. 23. 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 Acceptable time on the road? I always have care for my children PROFILING SEGMENT: ‘BECAUSE I CAN CHOOSE MY SCHEDULE’ I speak with I’m in job content Politeness W o r k i n g speed Work precision For this segment, it is crucial that they can choose their own working schedule, since they don’t always find care for their children pride of my work
  24. 24. Strategic level Operational level Tactical level The segmentation forms the basis of an action plan, that has benefits on different levels Monitoring size and evolution of the segments Prioritizing and designing solutions for each segment Helping teamcoaches to understand these differences
  25. 25. KEYS TO SUCCESS Cooperation between analysts and domain experts Project definition is crucial Realise the model’s power and limitations Complex algorithms are not the key to success Data and ethics can go hand in hand Not data volume, but the right data
  26. 26. BREAK DOWN THE WALL Time to break down the ‘wall of Boudreau’ – we hope we inspired you to make more use of data in an HR context
  27. 27. CONTACT US ● Pieter Pieter.vanbouwel@pythonpredictions.com +32 486/02.25.63 ● Peter Peter.beeusaert@sdworx.com +32 486/05.41.66

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