EFFECTIVE WAYS OF USING HR ANALYTICS to DESIGN and TRACK REWARD PROGRAMMES. DR JACLYN LEE- PhD SENIOR DIRECTOR-HR & OD

Transcription

1 EFFECTIVE WAYS OF USING HR ANALYTICS to DESIGN and TRACK REWARD PROGRAMMES DR JACLYN LEE- PhD SENIOR DIRECTOR-HR & OD 1

2 AGENDA Introduction to HR Analytics and its definitions Introduction to Reward Analytics. What is the current state? Role of Big Data in Reward Strategies Various types of analytic tools for tracking your reward strategy How to start and overcoming challenges 2

3 Analytics is the discovery and communication of meaningful patterns in data 3

4 What is HR Analytics? + People Data To Improve Employee Performance & Get Better Business ROI 4

5 Stages of Analytics 5

6 Descriptive vs Predictive HR Analytics 6

7 Analytics has a huge potential to increase return on investment when the numbers are tied to business outcomes and when predictions point to a range of possibilities that companies can plan for. It s not Trust me, I ve been here for a while. It s This is causing this, and the downstream capabilities are huge. 7

8 Current State of Rewards Analytics? Data-savviness The rewards function has always been more data driven than the rest of the other functions. This data-savviness makes it more sophisticated that in the rest of the HR functions in terms of analytical complexity TECHNIQUES USED In a recent survey by Mercer, it was discovered that within the compensation function, organizations are more likely to use ongoing reports, internal and external benchmarks to guide their decisions as opposed to more sophisticated techniques such as projections, simulations and predictive modelling. 87% - Ongoing Reporting 95%- External Benchmarks 90%- Internal Benchmarks 80%- Projections 64%- Simulations 43%- Predictive Modelling 8

9 Metrics and analytics In the same survey by Mercer, they found out that majority of people are interested to learn which elements of their reward strategy most effectively motivate their best-performing employees. They include: Critical drivers of employee retention Sources of talent drivers pay? Benefits? Environment? How to re-allocate business cost without diminishing output How to tailor rewards to adjust to changing demographics or generational trends Segment workforce to identify critical talent segments 9

10 This big data offers the possibility of applying analytic techniques to uncover stable correlations within particular populations, which will help refine reward design to better achieve its objectives. In some reward departments this is already beginning to happen. The University of Lincoln has implemented a new integrated HR and payroll system which has allowed it to incorporate a far more comprehensive reporting suite. There is a passive understanding that data to inform decision-making is the correct approach and that the data becomes more powerful when it is overlaid with other information, explains Ian Hodson, reward and benefits manager. Our new system has fed data triggers in to populate other systems, and we have a much more cross-function approach to data collation and production than ever before. Hodson says one of the exciting enhancements of having the new system is ensuring that he and his team can draw a correlation between base pay and consistently good performance: The aspect of the link between loyalty and base pay is an interesting one as statistically we have seen a competitive marketplace where those changing jobs have had a powerful negotiating position, often creating a significant differential against longer-serving staff. 10

11 Reward Strategy and Impact on Organizations Rewards influence who we recruit, how they behave and thus impact on the talent strategy, which in turn impact business results Effectiveness of rewards depend on alignment with four perspectives: Employer (strategy, operations) Employee (preferences, engagement) External (market, regulatory) Cost (ROI) Organizational Strategy Talent Strategy Reward Strategy Market Employee Cost Employer 11

12 Current types of reward measurements Ongoing reports External benchmarking Correlations and projections 12

13 TYPES OF ANALYTIC TOOLS FOR TRACKING REWARDS 13

14 Data Analysis (Business Analytics) Correlations, Regression, Data Mining, PRESCRIPTIVE ANALYTICS What should we do? Why should we do it? Scenario planning and decision modelling that optimises decision making DESCRIPTIVE ANALYTICS What happened? What is happening? Slice and dice data insight into what happened in the past PREDICTIVE ANALYTICS What will happen? Why will it happen? Tangible insights into data and projections of what is likely to happen in the future Optimisation Simulation and Decision Modelling Tools: Custom Made Business Reports, Dashboards, Score cards Tools Excel- Qlik Sense 19/5/

15 What s next for Reward Analytics Moving from Correlation, projections, costing to ISOLATION Bonus Payout CORRELATION Leads to Retention ISOLATION = Correlation + Analysis + Analyzing multiple variables and isolating only those that directly impact the bottom line or strategic goals Eg: Factors such as Employee Tenure Market Conditions Performance, Etc 15

16 Causal Analysis ISOLATION CAN BE DONE BY Causal Analysis through some examples such as: A) Engagement Driver Analysis influence of pay and other rewards on employee engagement B)Pay/Reward Analysis What variables determine pay, and the type of talents it will attract C)Turnover Driver Analysis to identify critical job groups or turnover groups, role of pay in retention D)Employee Benefits Analysis data to analyse the type of benefits that best help to retain and engage employees 16

17 A) Engagement Driver Analysis In a study done with 6,300 rewards professionals from across United States, Canada and Western Europe on the relationship between rewards and employee engagement, it was discovered that: Majority of reward professionals do not consider how total rewards programs affect employee engagement in the design of reward structures, policies and programs Base pay and benefits had the overall weakest relationship with the organisation s ability to foster high levels of employee engagement Quality of leadership had the strongest relationship. As a result compensation professionals should: Use pay packages to attract leaders who have demonstrated their ability to engage employees Think in terms of total rewards and not just financial rewards. This could include work environment, work life balance and nature of job and quality of work as well as career opportunities. 17

18 CORRELATION MATRIX of the STUDY 18

19 B) Pay Reward Analysis Understanding employee mix and type of talents that will drive profitability Segmenting salary levels and pay according to market competition Reviewing internal cost and review benchmarks Calculating the value of a top performer. This can be done through correlations with Product and employee output, sales, productivity indicators, etc. You can also use external data as representative of the performance multiplier of a top performer over the average employee at your firm. Some historical data points of multipliers such as the follows may be useful. 10 times higher value than average GE, Yahoo, and the U of Indiana study by O Boyle and Aguinis 25 times higher value than average Apple 28 times higher value than average Bradford Smart at Top Grading 300 times higher value than average Google 19

20 C) Turnover Driver Analysis Another important area is to use analytics to review turnover for critical jobs or high turnover groups 20

21 D) Employee Benefit Analysis Data driven metrics such as engagement measures, attrition, absenteeism and employee surveys are monitored together with benefit take up rates over time so that the impact of benefits can be monitored and benefit spend justified. Benefits Data Analysis LEADS TO Decreased cost Eg: - Calculating cost of sick leave - New Benefit linked to employee retention - Employee Satisfaction correlate with types of benefits offered Increased employee engagement Improve Communication Strategies 21

22 Google Reward Analytics Linking benefits to employee satisfaction The PiLab Google s PiLab is a unique subgroup that no other firm has. It conducts applied experiments within Google to determine the most effective approaches for managing people and maintaining a productive environment (including the type of benefits that makes employees the happiest). 22

23 Building Analytics Muscle 23

24 Equipping HR folks with Analytics Skills Sign them up for training courses in everything from Excel to big data systems. E.g.: Coursera or other online learning platforms Enroll them in seminars and webinars on metrics and data analysis hosted by vendors Set up in-house training programs to learn how to run regression models and use other analytics tools Arrange for them to get data analytics training from the company s IT department Ship them off to school! E.g., The University of Michigan has a big data boot camp for HR professionals, as well as executive progs in strategic workforce planning. 24

25 Science & Art future of HR departments will depend on having more staff comfortable with data. That said, it s also critical for HR to remain attuned to the passions of people within the organization. 25

26 Which level do you think your organisation is at the moment? 26

27 Three levels of Analytical Maturity 1. Analytically Challenged Traditional organisations that rely on experiences/ descriptive analytics 2. Analytical Practitioners Working towards more complex approach and some predictive applications 3. Analytical Innovators Predictive analytics and prescriptive analytics 27

28 How to start 28

29 Key questions to ask What is the most critical business issue facing your managers? What are the main people related challenges that affect your company s ability to execute on corporate goals and strategies? 29

30 Critical Issue Analysis The BIG ISSUE What s the IMPACT? Productivity Performance Safety Quality Analysis? What data avail? Definitions Evidence of Impact Quantify Trends/ Patterns Assess Strengths & Weaknesses Systems Culture Processes Policy 30

31 STATISTICAL TOOLS 31

32 Basic Statistical Analysis In order to do HR Analytics, it is necessary for you to understand basic statistical techniques There are some examples of such analysis such as correlation analysis, regression and ANOVA

33 1. Correlation Analysis How two variables relate to one another. When there is correlation, it mean the two variables are related. When the value of one changes, the other one is also expected to be different It is descriptive and does not predict anything Let s see an example

34 Employee Gender Rank Perf Rating 1 M Senior 90 2 F Middle 90 3 M Middle 85 4 F Junior 60 5 F Junior 40 6 F Junior 80 7 F Middle 65 8 M Senior 88 9 M Senior F Junior 75 Case 1: Small company with 10 employees measures performance year. One of the first things you notice is that males seem to score higher compared to females. In order to prove this holds true, we can run a correlation analysis to find out if your eyes are playing tricks on you. A correlation coefficient of +1 indicates a perfect positive correlation. As variable X increases, variable Y increases. As variable X decreases, variable Y decreases. In this case, the correlation analysis shows a factor of 0.64 which is considered a moderate correlation.

35 Regression Analysis More complex statistical technique. It can be used to analyse an outcome using one or multiple predictive variables. Can be used both as descriptive or predictive analytics. Case 2: In a company of 500 people, Jill has long suspected that many employees take sick days when the weather outside is nicer, but she could not prove it. She started collecting data (On the right) She ran a regression and to predict the number of absentees by using the temperature as a predictor Temperature Day F #sick People Taking Sick leave Temperature Red Line Line of best fit and the regression line. From the line derived, you can see that there is positive relationship between temperature and people taking sick leave. As the temperature rises, number of people taking sick leave rises.

36 Project Marvel

37 CONCLUSION: MOVING FROM ON-GOING REPORTING TO PREDICTIVE.. 37

38 THANK YOU 38