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Predicting your employees !
Philippe Nemery emphasizes the necessity of digital transformation and predictive analytics in managing the human workforce, advocating for data-driven decision-making across organizations. He highlights that companies leveraging human capital analytics outperform their competitors by achieving greater stock returns and talent mobility. The document also discusses practical applications through case studies demonstrating how predictive analytics can optimize recruitment and employee retention.
Predicting your employees !
- 1. Predicting Your Employees PhilippeNemery nemeryphilippe@nemeryp Philippe.Nemery@sap.com
- 2. Digitalise or Die? I revitalize companies with digital transformation. BI & Predictive Analytics Philippe Nemery
- 3. 3 Key TakeAways: Digitalize, DIGITALIZE, even your ‘Human Workforce’ ! Predict your employees in 4 ‘clicks’. Make decisions based on data.
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- 6. World’s most popularmedia creator creates no content World’s largest taxi company owns no cars The most valuable retailer has no inventory World’s largest hospitality company owns no real estate
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- 8. The entire valuechain is digitized, including the core which serves as the platform for innovation and business process optimization. Customer Experience Omni-Channels Workforce Engagement Assets & Internet of Things Supplier Collaboration Business Networks The Digital Business Framework Every Company Needs to Think About Digitization Across Five Key Business Pillars
- 9. - People Analytics: « Accuratepeople management decisions are based on data. » People Operations or HR-Team
- 10. - Driving Deeper Insight– Aligning to Business Issues
- 11. Source: High-impact TalentAnalytics: Building a World-class HR Measurement and Analytics Function. Bersin by Deloitte 2013; Sierra-Cedar 2104 HR Systems Survey, and Human Capital Management Trends, 2012. Aberdeen Group Organizations that embrace Human Capital Analytics
- 12. 30% 75% 19X 2X greaterstock returns than average Higher return on equity better improvement in customer satisfaction measures likely to improve leadership pipelines greater talent mobility as effective at recruiting Source: High-impact Talent Analytics: Building a World-class HR Measurement and Analytics Function. Bersin by Deloitte 2013; Sierra-Cedar 2104 HR Systems Survey, and Human Capital Management Trends, 2012. Aberdeen Group Organizations that embrace Human Capital Analytics outperform those that don’t
- 13. of organizations usehuman capital analytics solutions — workforce analytics, strategic workforce planning, and predictive analytics … Why? Yet, very few do … 12% Sierra-Cedar 2104 HR Systems Survey
- 14. We live ina world informed by predictive insight
- 15. Organizations that anticipatewhat comes next drive better decisions 68% OF ORGANIZATIONS THAT USED PRED.AN. REALIZED A COMPETIVE ADVANTAGE Verdana Research 55% USE PRED.AN. TO CREATE NEW REVENUE OPPORTUNITIES 45% USE PRED.AN. FOR CUSTOMER SERVICES 43% USE PRED.AN. FOR PRODUCT RECOMMENDATION 45% USE PRED.AN. TO INCREASE PROFITABILITY
- 16. of organizations usepredictive analytics in human capital management … Why? Yet, very few do … 4% Sierra-Cedar 2104 HR Systems Survey
- 17. 26% KEEP CURRENT EMPLOYEESIS TOP PRIORITY Linked-In Benelux Research 2015
- 18. Common obstacles havebeen hard to overcome High-impact Talent Analytics: Building a World-class HR Measurement and Analytics Function. Bersin by Deloitte. 2013. CEB. Talent Management Report in The Times. October 2013.
- 19. Common obstacles havebeen hard to overcome High-impact Talent Analytics: Building a World-class HR Measurement and Analytics Function. Bersin by Deloitte. 2013. CEB. Talent Management Report in The Times. October 2013.
- 20. 20 Predictive Analytics atthe ‘Army’ of … Customer Case Study
- 21. 21 SAP PA enabledthe HR Department to conduct advance and predictive analytics to gain insight on human behaviour, recruitment, training... to help for an optimized management of the workforce. The first analysis consisted of: - Recruitment optimization: part of a global recruitment campaign, how to identify key profiles/job description. Identify talent. Start: the root cause analysis of early resignation for non-officers. - Proactive Talent Turnover Detection: specifically focussed on offices and potential resignation after a long carrier. - Optimized Career Path management. Why SAP? • Ease of use of the solutions • Quick results and implementation. Predictive Analytics at the ‘Army’ of … Customer Case Study
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- 23. • SAP predictiveanalytics enables business users to prepare, analyze and generate predictive models in a highly automated fashion. • We connected the SAP predictive engines to SAP SuccessFactors Workforce Analytics database for direct access to Wawa’s data. • We ran predictive explorations across three key areas: 1.Flight risk 2.Managerial Performance 3.Career Paths Customer Case Study
- 24. Finding the ‘holygrail’ – better decisions on strength of data Wawa finds a high correlation between terminating employees that are younger than 30 and have <3 months tenure. Finds « danger zone » for high turnover risk is employees working 7-16 hours per week and those with less than 39 days of tenure. If wawa can get employees to 39 days, their probability of staying one year dramatically rises. Wawa educates their manager on policies for new- hire scheduling and workings hours. With annual turnover cost savings estimated at: Wawa now plans to revisit recruitment, onboarding, training and other engagement practices to ensure employees receive full cultural immersion.39 1M Insight Action
- 25. Salary Evolution Working hours Interactions Number of years Churns Performances Retention Program Career Paths Training Offer Historical data Typical and recurrent behaviors Predict and act Predictive Analytics
- 26. Predictive Analytic Concepts Explore andprepare your data Design and validate your model Deploy processes and actions Analyze and optimize the results 1 2 3 4 1 solution
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- 28. 28 Demo 4 clicks =model Screenshots Here.
- 29. In-database Automated Dataset Production Automatedmodel creation Classification Regression Clustering Forecasts In-database Deployment Deployment in other apps Model productionization Control Recalibration Batch production Model Industrialization (Each Step has been Automated)
- 30. Predictive Analytics inHR: Action, Not Just Algorithms • Predictive algorithms are never ends in themselves. The only thing that counts is the business decision that a professional would take based on the information provided. • Our predictive capabilities aim to make things simpler for the user by seamlessly integrating highly-guided predictive functionality where it makes sense. Retention, Recruiting – Hiring Process, Development Program Recommendation, Career Path Recommendation, Leadership Discovery,…
- 31. Predictive analytics becomingpervasive across the suite Example: Learning Recommendations HANA-powered engine will provide ‘smart’, personalized recommendations specific to every employee – based on job, skills, learning preferences, etc. Powered by advanced machine learning algorithms and HANA Graph database matching people to content
- 32. SAP SuccessFactors HumanCapital Analytics Capabilities for every step in data-based workforce decision-making from HR & Operational Workforce Reporting Standard and ad-hoc reports Embedded intelligence Report and dashboard builder to Business Workforce Analytics Automated data integration Ensures data quality Predefined metrics, analyses, benchmarks & best practices Workforce Planning Supply-demand gap analysis Financial modeling and business impact Risk analysis and strategy guidance & Strategic
- 33. 3 Key TakeAways: Digitalize, DIGITALIZE , even your Human Workforce ! Predict your employees in 4 ‘clicks’. Make decisions based on data.
- 35. Philippe Nemery nemeryphilippe@nemeryp Philippe.Nemery@sap.com More Infoabout SAP Predictive Analytics 2.4: Here – Help Predictive Analytics 2.4 - Trial Available Download free version and try for yourself
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- 38. 38Back to Presentation 1 Specifyyour data.
- 39. 40Back to Presentation 2 Datais processed.
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- 42. 43Back to Presentation Modelis generated.
- 43. 44Back to Presentation Whatexplains the target?
- 44. 45Back to Presentation Whatexplains the target?
- 45. 46Back to Presentation Whatexplains the target?
- 46. 47Back to Presentation Whatexplains the target?
- 47. 48Back to Presentation Howreliable is the model?
- 48. 49Back to Presentation Takeaction: cost - benefit ?
- 49. 50Back to Presentation Howto deploy the model ?
- 50. 51Back to Presentation Choosethe deployment.
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