Module 1: Introduction to HR Analytics

Objective: Establish a foundational understanding of HR Analytics and its importance in modern organizations.

• Overview: Definition and scope of HR Analytics.
• Importance in HR Decision-Making: How data-driven insights can transform HR practices.
• HR Analytics Framework: The basic structure and components of HR Analytics.
• Key HR Metrics: Introduction to essential HR metrics such as employee turnover, retention rates, and employee engagement.

Module 2: Workforce Planning and Recruitment Analytics

Objective: Utilize HR Analytics to optimize recruitment and workforce planning processes.

• Recruitment Funnel Effectiveness: Analyzing the recruitment process through various stages, from sourcing to hiring.
• Quality of Hire: Measuring and improving the quality of new hires.
• Selection Ratio and Cost Per Hire: Understanding and optimizing the selection ratio and associated costs.
• Offer Acceptance Rate: Addressing issues related to offer acceptance and compensation through data insights.

Module 3: Performance Management and Employee Evaluation

Objective: Leverage HR Analytics to enhance performance management and employee evaluation systems.

• Performance Metrics: Key performance indicators for individual and organizational performance.
• Forced Ranking Method: Using normalization techniques like the Bell Curve in performance evaluations.
• PMS Bell Curve Examples: Analyzing the impact of business outcomes on performance distribution.
• Improving Performance Reviews: Using data to make performance reviews more equitable and impactful.

Module 4: Employee Engagement and Retention Analytics

Objective: Apply HR Analytics to improve employee engagement and retention strategies.

• Engagement Metrics: Tools for measuring and analyzing employee engagement.
• Retention Analysis: Identifying factors that contribute to employee turnover and developing strategies to retain top talent.
• Predictive Analytics for Retention: Using data to predict and address potential turnover risks.
• Engagement Initiatives: Data-driven approaches to designing and implementing employee engagement programs.

Module 5: Compensation and Benefits Analytics

Objective: Integrate analytics into compensation and benefits to drive organizational success.

• Total Rewards Framework: Understanding and applying the total rewards concept through analytics.
• Compensation Structures: Analyzing and optimizing pay structures based on performance and market data.
• Cost-Benefit Analysis: Evaluating the impact of various compensation and benefit programs.
• Long-Term Incentives: Using data to design and justify long-term incentive plans.

Module 6: Advanced HR Analytics and Predictive Modelling

Objective: Explore advanced HR Analytics techniques and predictive modelling for strategic HR planning.

• Predictive Modelling in HR: Introduction to predictive analytics tools and techniques.
• Workforce Planning with Analytics: Connecting business strategy with talent strategy using data.
• Case Studies: Real-world applications of advanced HR Analytics in organizations.
• Future Trends in HR Analytics: Emerging technologies and methodologies shaping the future of HR.

Duration: 6 Classes
Contact Hours: 12 Hours
Method: Activity-based online sessions with tools, exercises, group work, peer-group learning and case studies.
Assessment Method: Ongoing assessments through quizzes, assignments, and presentation.