HR Analytics module one PPT CHALLENGES In HR

analytics

1. HR Analytics

2. Can you answer this question? Which is the most important asset on balance sheet of any company ? & which one is NOT ?

3. HR Story •CEO: Let us invest more in our people. •CFO: That is a risk! Their marketability will increase. What if they quit? •CEO: What if we don’t invest in them and they don’t quit ? Is it not a bigger risk ?

4. Challenges in HR •Recruitment •Training & Development •Retention •Motivation •Employee engagement •Performance appraisal •etc. What does these require ?

5. Worries of every company’s HR head 1.What are the Challenges of a HR Manager ? 2.Why is it so difficult to get budget approval for HR ? (For marketing department it is slightly easy.) 3.So how to prove ?

6. HR Analytics

7. What is HR Analytics ? • HR Analytics is defined as a systematic collection, analysis, and interpretation of data to improve talent management decisions. • HR analytics or people analytics or workforce science, uses a mix of understanding : patterns based on data & algorithms a nd intuition in making people decisions across an employee life cycle; typically,80% data-based analysis and 20% intuition seem to be the rule of thumb. • HR Analytics provides evidence linking investments in human resources of the organization to either top-line or bottom-line performance .

8. What is NOT HR analytics ? •HR analytics is not about simply preparing metrics or dashboards, or analysis of reports of headcount or employee engagement score or attrition data. These are simple and general metrics . Not HR Analytics •HR analytics should include element of predictive and prescriptive analytics.

9. Why everyone is dancing about HR analytics NOW ? •Computational & data capturing Technology •“3D data” -Data volume, Data velocity, and Data variety. •Cheaper technology •Peer learning from using analytics in Finance , marketing etc •Data Visualization tools •Clarity on impact of HR investment on bottom line

10. 5 key dimensions for applying Analytics in HR

11. Understanding KPIs under each dimension Recruitment and retention Time to hire Most productive recruitment channels % vacant posts Number of years’ service by department Attrition rates( turover ) Reason for attrition Absenteeism Reason for on-going absenteeism Succession planning

12. Productivity and Managing Objectives % achievement of targets Profit per full-time-equivalent (FTE) Human capital return on investment Employee productivity index Performance differential rate Unscheduled absence rate

13. Employee engagement Attrition Rates Reason for attrition Compensation level % achievement of development goals Learning and development opportunities

14. Remuneration Cost per full time equivalent Average remuneration % of employees satisfied with remuneration % of employees above average salary of their position % of high performers who resign for compensation related reasons

15. Benefits Benefits cost per employee Healthcare cost per employee % utilization per benefit Benefit satisfaction rating Benefit as % of salary Benefit revenue ratio

16. That’s too much of theory Lets experience HR Analytics in action

17. Case Study –I: Employee Retention Amazon India Case Study Amazon India, one of the top ecommerce company was facing a business challenge. The strength of the company was its Human Resource but the real challenge was to effectively manage the HR and retain talent as the competitors try to pouch it’s best talent. The attrition rate in the company was high in recent years ,and this was an ew challenge to behandled by a recently recruited HR head Miss.Pallavi . Pallavi asked Miss.Taniyaa newly recruited HR Analyst in a team to conduct an employee survey and found that lack of incentives and low salary hike was one of the major reason that employees are quoting for high employee turnover. However, HR head Pallavi disagreed, her view was, most employees were note ligible for incentives and salary hike as their performance was not as satisfactory.

18. Continue.. •Puzzled Pallavi & Taniya wanted to know what are the factors which impact the performance and how performance of the employees can be improved. •Amazon has decided to hire you as an Analytics expert to provide insights and recommend strategies. •As an HR Analyst, you need to address following issues: 1.What impacts performance? Set priorities for factors. 2.How to predict employee performance before even hiring–assessing job applicants.

19. Case study –II: HR impact on stock price

20. Story of HR Manager •CEO&CFO: Mr.HR, we are cancelling the Rs10 Crore HR Budget. •Mr.HR: Oh My silly Gosh, why? •CEO&CFO: We think we are wasting Rs10 crore every year on training, motivation blah blah . •Mr.HR: Wrong, HR is our key asset. We need to invest on them so that the company keeps growing with profits.

21. •CEO&CFO: Wed is agree, if we save that Rs10crore,we can increase EBITDA and then it increases EPS which results in Increase in stock price. So our investors will be happy. •Mr.HR: Sorry Sirs, actually investing Rs10crore on HR increases EBITDA, EPS and stock price in longrun . (instead of saving10crores, I can offer better strategies) •CEO&CFO: Prove it Open Excel file saved as Data Case study and solve this HR Analytics Problem.

22. Types of Analytics Descriptive Analytics: Answers the Question:  What happened and why? Primary Tools: Data aggregation and data mining Limitation: A snapshot of the past may have limited ability to guide future decisions Best Use: Summarize results for all or part of your business

23. 2. Predictive Analytics : Answers the Question : What might happen? Primary Tools : Machine learning, statistical models, and simulation Limitations:  Estimation of the future Best use:  Backing of an educated guess for the results of low complexity decisions

24. 3. Prescriptive Analytics : Answers the Question : What should we do? Primary Tools : Optimization and heuristics Limitations:  Most effective where you have some control over what is being modeled Best use : When you need to make important, interdependent, complex or time-sensitive decisions