Hr analytics 2
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The document discusses HR analytics and predictive modeling. It defines key concepts like metrics, analytics, and business intelligence. Analytics uses data to understand past trends and predict future outcomes. The document outlines areas where predictive modeling can be applied in HR, like attrition, recruitment effectiveness, and talent forecasting. It also provides examples of companies like Oracle, Sprint, Starbucks, and Dow Chemical that have successfully used analytics to retain top performers, predict attrition, measure engagement impacts, and do workforce planning.
Hr analytics 2
- 1. HR Analytics Shubham Singhal 80303120053 PGDM NMIMS, Hyderabad
- 2. Primer The core of HR Analytics is the "metric“ Metrics can be said as data that conveys meaning in a given context Metric is to be distinguished from numbers Example: - Employee turnover is 13.5% p.a. Data - There is a 4 percent point rise in attrition rate on a year to year basis Metric - Inappropriate Leadership styles of select managers resulted in higher attrition of 3% on a comparable basis Analytic 2
- 3. Primer – Contd. Checklist, Dashboard, HRIS - All of these are tools to collate and display information Hypothesis: u0 & u1 Variables: Dependent and Independent Statistical Models - E.g. Regression, ANOVA 3
- 4. HR Analytics Analytics is not so much about numbers, as it is to do with logic and reasoning Analytics is different from Analysis, which is the equivalent of number crunching. Analytics uses analysis but then builds on it to understand the 'why' behind the figures and/or to predict decisions. Analytics is the methodology of logical analysis Analytics requires the use of carefully constructed metrics HR Analytics is data based; it uses past data to predict the future It is not about the quantity of data churned; it is about the logic used to link metrics to results 4
- 5. Core concepts and terminologies Analytics Decision =Business Intelligence 5
- 6. Business intelligence (BI) is a set of theories, methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information for business purposes. Business analytics (BA) refers to the skills, technologies, applications and practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. 6 Core concepts and terminologies
- 7. Past to future Tera bytes of data of information being generated every single day which is being used to answer, fairly accurately, what will probably occur in the future Analytics is shifting emphasis from trend analysis based purely on internal data to presenting scenarios of the future 7
- 9. Background Need of HR analytics & reporting Many organizations have high quality HR data (residing with a multitude of systems, such as the HRMS, performance management, learning, compensation, survey, etc.) but still struggle to use it effectively to predict workforce trends, minimize risks and maximize returns. The costs of attrition, poor hiring, sub-optimal compensation, keeping below par employees, bad training & learning strategies are just too high Data-driven insights to make decisions are always better than judgmental (subjective) HR practices in terms of how to recruit whom to hire how to onboard and train employees how they keep employees informed and engaged through their tenure with the organization Hence regular tracking and prediction of crucial HR metrics is indispensable 9
- 10. Why HR Analytics? “What gets measured, gets managed; What gets managed, gets executed” - Peter Drucker “ To clearly demonstrate the interaction of business objectives and workforce strategies to determine a full picture of likely outcomes” HR Dashboards - SAP Measure & Manage Linkage of Business Objectives and People Strategies Return on Investment Performance Improvement “The business demands on HR are increasingly going to be on analysis just because people are so expensive“ - David Foster “Global organizations with workforce analytics and workforce planning outperform all other organizations by 30% more sales per employee.” - CedarCrestone 10
- 11. Objectives11 Predict attrition especially amongst high performers. Forecast the right fitment for aspiring employee Predict how compensation values will pan out. Establish linkages between Employee engagement score and C-Sat scores(Work in progress)
- 12. What should/could be measured? Recruitment Organization effectiveness HR Matrices Workforce Comp & Benefits Retention Performance & Career Management Training 12
- 13. Critical areas for HR Predictive analytics 1. Turnover modeling. Predicting future turnover in business units in specific functions, geographies by looking at factors such as commute time, time since last role change, and performance over time. 2. Targeted retention. Find out high risk of churn in the future and focus retention activities on critical few people 3. Risk Management. Profiling of candidates with higher risk of leaving prematurely or those performing below standard. 4. Talent Forecasting. To predict which new hires, based on their profile, are likely to be high fliers and then moving them in to fast track programs 13
- 14. Trendwise Analytics – HR analytics capabilities •Reporting of basic metrics, their frequencies & percentages by various cuts followed by key highlights. These can be monthly, quarterly, half yearly tracking reports • Tool: SAS/REPORT • Techniques: frequencies , means, percentages etc. Level-1 Descriptive analysis •Derivation of some HR operational metrics which will help us in tracking the efficiency of HR functions • Tool: SAS • Techniques: means, variance, control limits, ratios, percentages etc. Level-2 Operational metrics •Predictive analysis based on historical HR data. Attrition forecasting, performance management, compensation analysis, survey analytics, new hire strategies etc., • Tool: SAS BASE, SAS E-miner, Excel • Techniques: Regression analysis, Time series analysis, cluster analysis etc. Level-3 Predictive analysis Three levels of HR analytics and reporting 14
- 15. Stages of Analytics Predictive Analytics What can happen? Analysis & Monitoring Why did it happen? What is happening now? Reporting What happened? Complexity 15
- 16. Types of Analytical Models PREDICTS PREDICTS PREDICTIVE ANALYTICS Current Predictive Analytics Data PREDICTS Future INFERENTIAL ANALYTICS Analysis & Monitoring Past Data Reporting REPORT Drawing Conclusions or Inferences DESCRIPTIVE ANALYTICS Representation of Data and Summarizing 16
- 17. Critical areas for HR Predictive analytics Turnover modeling. Predicting future turnover in business units in specific functions, geographies by looking at factors such as commute time, time since last role change, and performance over time. One can accelerate hiring efforts accordingly, reducing lead time time and panic hiring, which can lead to lower cost, higher quality hiring. Recruitment advertising /HR Branding effectiveness: HR Branding efforts based on Response modeling for advertising jobs. 17
- 18. HR – Predictive analytics Targeted retention. Find out high risk of churn in the future and focus retention activities on critical few people Risk Management: profiling of candidates with higher risk of leaving prematurely or those performing below standard. Talent Forecasting. To predict which new hires, based on their profile, are likely to be high fliers and then moving them in to fast track programs 18
- 19. Tools & Software Used Typical tools / software: • Microsoft Excel (max used) • BI reporting tools • ERP reporting tools, dashboards 19 • Statistical software like SAS, SPSS etc.
- 20. Social media impact Predicting the future sounds mystical Predictive ANALYTIC is touching every human on Earth who accesses internet Day to day existence is now being exploited by social media and then the analytics 20
- 21. Executives; Corporate Strategy Craft and guide long term workforce plan based on given information Finance; Controlling; Budgeting Give input regarding financial figures and receives insights for midterm financial planning regarding the workforce €$¥ HRBP HR Business Partner Consult with Business Units based on workforce intelligence and drives action plans as final deliverable from the process HR HR HR HR Administration; HR Functions Recruiting, Staffing, Talent Management and other HR functions support fulfillment of workforce action plans HCM Analytics consumers by role Stakeholders across the organization √x Middle Managers; Line Managers Execute on strategic plans and manage organizational performance to assure strategic objectives are reached timely and efficiently MGR Employee Needs contextual HR data to better perform HR Analyst Needs ad-hoc capabilities to do sophisticated analysis and planning
- 22. Real world case studies Starbucks, Limited Brands, and Best Buy—can precisely identify the value of a 0.1% increase in employee engagement among employees at a particular store. At Best Buy, for example, that value is more than $100,000 in the store’s annual operating income. Many companies favor job candidates with stellar academic records from prestigious schools—but AT&T and Google have established through quantitative analysis that a demonstrated ability to take initiative is a far better predictor of high performance on the job. Employee attrition can be less of a problem when managers see it coming. Sprint has identified the factors that best foretell which employees will leave after a relatively short time. In 3 weeks Oracle was able to predict which top performers were predicted to leave the organization and why - this information is now driving global policy changes in retaining key performers and has provided the approved business case to expand the scope to predicting high performer flight . 22
- 23. Dow Chemical has evolved its workforce planning over the past decade, mining historical data on its 40,000 employees to forecasts promotion rates, internal transfers, and overall labor availability. Dow uses a custom modeling tool to segment the workforce and calculates future head count by segment and level for each business unit. These detailed predictions are aggregated to yield a workforce projection for the entire company. Dow can engage in “what if” scenario planning, altering assumptions on internal variables such as staff promotions or external variables such as political and legal considerations. 23 Real world case studies
- 24. Thanks!
Editor's Notes
- #25: References:1. Business Intelligence Success Factors: Tools for Aligning Your Business in the Global Economy. Hoboken, N.J: Wiley & Sons. ISBN 978-0-470-39240-9.2. Beller, Michael J.; Alan Barnett (2009-06-18). "Next Generation Business Analytics". Lightship Partners LLC