Modern organizations are ramping up on data science, recruiting talents by making huge  investments for their work engagement. These firms are accelerating their digital transformation to  deploy smart technologies around AI and big data to improve their talent management systems. In a  world of work that is increasingly virtual (and perhaps even only virtual), the volume of data  available to understand and predict employees’ behaviours will continue to grow exponentially,  enabling more opportunities for managing through HR Analytics. As such, people analytics is a  deliberate and systematic attempt to make organizations more evidence-based, talent-centric, and  meritocratic, which, one would hope, should make them more effective. 

Course Objective: 

Understand how HR and analytics have evolved and are transforming people management  

around the world. 

Understand the role of HR Analytics and build skills to conduct a HR Analysis through  

measuring staffing utility. 

Course Outcomes: 

At the end of the course, the student will be able to 

CO1: Describe the role of descriptive and prescriptive analytics in HR analytics. 

CO2: Explain the recent developments in decision based framework for staffing measurement. 

CO3: Analyse the different utility models for taking staffing decisions. 

CO-PO Mapping 

CO/PO  PO1  PO2  PO3  PO4  PO5
CO1  2
CO2  2
CO3  2


Course Delivery Methods

Lecture Mode  Seminar  Case studies  Web References
CO1  √  √  √ 
CO2  √  √  √ 
CO3  √  √  √ 



Student should score 50% (30 Marks) & 80% attendance to be eligible for the certification Syllabus: (Total Hours Required – 30) 

Module 1: HR Analytics: Analytics-Nature-Evolution of Human Capital Metrics-Steps in Analytics Role of Descriptive analytics & Prescriptive analytics in HR analytics – HR Analytics Frameworks:  LAMP framework, HCM: 21 Framework& Talent ship Framework, Environmental scanning: The Big  Picture-The value of statistical analysis-The importance of risk assessment, Predictive management  

Module 2: Staffing Utility : Concept and Measures A Decision-Based Framework for Staffing  Measurement, Overview: The Logic of Utility Analysis, Utility Models and Staffing Decisions, The  Taylor-Russell Model, The Naylor-Shine Model, The Brogden- Cronbach- Gleser Model 

Module 3: Absenteeism and Separation: Cost of Absenteeism – Direct Costs and the Incidence,  Causes, Consequences, Categories of Costs, Analytics and Measures for Employee Absenteeism,  Strategies to reduce absence, positive Incentives, Paid Time Off (PTO) 

Module 4: Employee Turnover: Separations, Acquisitions, Cost, and Inventory, Voluntary Versus  Involuntary Turnover, Functional Versus Dysfunctional Turnover, Pivotal Talent Pools with High Rates  of Voluntary Turnover, Involuntary Turnover due to Dismissals and Layoffs, computing Turnover 15  rates, training cost, performance difference between separating employees and replacements, cost  of lost productivity and lost business ,Promotion and succession planning analytics, Compliance  analytics 

Module 5: Employee Health Wellness and Welfare: Logic of Workplace Health Programs (WHP),  Analytics for Decisions about WHP Programs, Measures: Cost Effectiveness, Cost-Benefit, and  Return-on-Investment Analysis, Cost-Effectiveness Analysis, Cost Benefit and Return-on Investment Analysis, Employee Assistance Programs (EAPs) Future of Lifestyle Modification, WHP,  and EAPs 


  1. Wayne F. Cascio, John W. Boudreau, Investing in people: Financial Impact of Human Resource  Initiatives, Pearson Education, New Jersey, US  
  2. Tracey Smith, HR Analytics, The what, Why and How, 1e Create Space Independent Publishing  Platform  
  3. Laurie Bassie, Rob Carpenter: HR Analytics Handbook, Mc Bassi & Company; 1st paperback  edition, Brooklyn ,US  
  4. Jac Fitz-Enz, The New HR Analytics: Predicting Economic Value of Your Company’s Human Capital  Investments. New York, NY: AMACOM.