Predicting Your Employees

Transcription

1 Predicting Your Employees Philippe nemeryphilippe

2 Philippe Nemery Digitalise or Die? I revitalize companies with digital transformation. BI & Predictive Analytics

3 3 Key Take Aways: Digitalize, DIGITALIZE, even your Human Workforce! Predict your employees in 4 clicks. Make decisions based on data.

4 IGITAL 52 % ARWINISM Fortune 500

5 IGITAL ENJAMIN UTTON FFECT

6 World s largest taxi company owns no cars World s most popular media creator creates no content The most valuable retailer has no inventory World s largest hospitality company owns no real estate

7 - IGITAL CONOMIES NTERPRISE ORE

8 The Digital Business Framework Every Company Needs to Think About Digitization Across Five Key Business Pillars The entire value chain is digitized, including the core which serves as the platform for innovation and business process optimization. Workforce Engagement Supplier Collaboration Business Networks Assets & Internet of Things Customer Experience Omni-Channels

9 - People Analytics: «Accurate people management decisions are based on data.» People Operations or HR-Team

10 - Driving Deeper Insight Aligning to Business Issues

11 Organizations that embrace Human Capital Analytics 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, Aberdeen Group

12 greater stock returns than average Higher return on equity better improvement in customer satisfaction measures 30% 75% 19X Organizations that embrace Human Capital Analytics outperform those that don t likely to improve 2X 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, Aberdeen Group leadership pipelines greater talent mobility as effective at recruiting

13 Yet, very few do 12% of organizations use human capital analytics solutions workforce analytics, strategic workforce planning, and predictive analytics Why? Sierra-Cedar 2104 HR Systems Survey

14 We live in a world informed by predictive insight

15 Organizations that anticipate what comes next drive better decisions 43% 45% USE PRED.AN. FOR PRODUCT RECOMMENDATION USE PRED.AN. TO INCREASE PROFITABILITY 68% OF ORGANIZATIONS THAT USED PRED.AN. REALIZED A COMPETIVE ADVANTAGE 55% 45% USE PRED.AN. TO CREATE NEW REVENUE OPPORTUNITIES USE PRED.AN. FOR CUSTOMER SERVICES Verdana Research

16 Yet, very few do of organizations use predictive analytics in human capital management 4% Why? Sierra-Cedar 2104 HR Systems Survey

17 26% KEEP CURRENT EMPLOYEES IS TOP PRIORITY Linked-In Benelux Research 2015

18 Common obstacles have been hard to overcome High-impact Talent Analytics: Building a World-class HR Measurement and Analytics Function. Bersin by Deloitte CEB. Talent Management Report in The Times. October 2013.

19 Common obstacles have been hard to overcome High-impact Talent Analytics: Building a World-class HR Measurement and Analytics Function. Bersin by Deloitte CEB. Talent Management Report in The Times. October 2013.

20 Predictive Analytics at the French Ministry of Defense 20

21 Predictive Analytics at the French Ministry of Defense SAP PA enabled the 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. 21

22 Customer Case Study

23 Customer Case Study SAP predictive analytics 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

24 Finding the holy grail better decisions on strength of data Insight Wawa finds a high correlation between terminating employees that are younger than 30 and have <3 months tenure. Action Wawa educates their manager on policies for newhire scheduling and workings hours. 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. 39 With annual turnover cost savings estimated at: 1M Wawa now plans to revisit recruitment, onboarding, training and other engagement practices to ensure employees receive full cultural immersion.

25 Predictive Analytics Historical data Typical and recurrent behaviors Predict and act Salary Evolution Working hours Interactions Number of years Churns Performances Retention Program Career Paths Training Offer

26 Predictive Analytic Concepts Explore and prepare your data Deploy processes and actions Design and validate your model solution Analyze and optimize the results 3

27 Demo 4 27

28 Demo 4 clicks = model Screenshots Here. 28

29 Model Industrialization (Each Step has been Automated) In-database Automated Dataset Production Automated model creation Classification Regression Clustering Forecasts In-database Deployment Deployment in other apps Model productionization Control Recalibration Batch production

30 Predictive Analytics in HR: 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 becoming pervasive 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 Human Capital Analytics Capabilities for every step in data-based workforce decision-making from HR & Operational to Business & Strategic Workforce Reporting Standard and ad-hoc reports Embedded intelligence Report and dashboard builder 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

33 3 Key Take Aways: Digitalize, DIGITALIZE, even your Human Workforce! Predict your employees in 4 clicks. Make decisions based on data.

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35 Philippe nemeryphilippe More Info about SAP Predictive Analytics 2.4: Here Help Predictive Analytics Trial Available Download free version and try for yourself

36 Appendix: Screenshots Demo

37 Back to Presentation 37

38 Specify your data. 1 Back to Presentation 38

39 Data is processed. 2 Back to Presentation 40

40 Define Target. Back to Presentation 41 3

41 Choose output. 4 Back to Presentation 42

42 Model is generated. Back to Presentation 43

43 What explains the target? Back to Presentation 44

44 What explains the target? Back to Presentation 45

45 What explains the target? Back to Presentation 46

46 What explains the target? Back to Presentation 47

47 How reliable is the model? Back to Presentation 48

48 Take action: cost - benefit? Back to Presentation 49

49 How to deploy the model? Back to Presentation 50

50 Choose the deployment. Back to Presentation 51

51 Example Direct Export Back to Presentation 52