MOVING FROM I THINK TO I KNOW ICON STRATEGIC WORKFORCE PLANNING & TALENT ANALYTICS SEMINAR

Transcription

1 HEALTH WEALTH CAREER MOVING FROM I THINK TO I KNOW ICON STRATEGIC WORKFORCE PLANNING & TALENT ANALYTICS SEMINAR 7 OCTOBER 2016 Maude Julien, Senior Consultant Workforce Analytics and Planning maude.julien@mercer.com

2 72% 56% 89% OF ORGANISATIONS HAVE RESOURCES INVOLVED IN WORKFORCE PLANNING, ANALYTICS AND REPORTING OF ORGANISATIONS THAT DO NOT HAVE RESOURCES TODAY PLAN TO FOCUS RESOURCES IN THIS AREA IN THE NEXT TWO YEARS OF ORGANISATIONS HAVE HR AS PRIMARY RESPONSIBILITY FOR WORKFORCE ANALYTICS ACTIVITIES SOURCE: MERCER 2014 WORKFORCE PLANNINNG, ANALYTICS AND REPORTING FUNCTIONAL STRUCTURE SPOT POLL 1

3 TOP 3 MOTIVATIONS FOR USING ANALYTICS 1. To discover and leverage factors that increase productivity/business success (52%) 2. To make data-driven decisions on human capital investments (52%) 3. To support long-term workforce planning for the organisation (45%)

4 OUTCOMES Efficiency in business processes (49%) Increased employee engagement (39%) Increased employee retention (37%) Increased productivity (32%) Increased quality of product/service (24%) Decreased legal costs and compliance fees (21%) Increased revenue (16%)

5 PRIMARY ROADBLOCKS Lack of integration among data systems (54%) Lack of analytics skills within HR (52%) Lack of management experience in effectively using analytics data (48%) 4

6 THE FOCUS IS TO USE WORKFORCE ANALYTICS AND PLANNING TO GET TO IMPACT Identify talent segments that impact revenue Myth-bust assumptions based on intuition Identify unknown structural imbalances Future-proof the organisation against risks of talent shortages Impact Impact Impact Impact Targeted interventions provided revenue gain of 25 million Savings of over 30 million Identification of a 6 million business problem Significant recruitment savings and reduced risk of shut down 5

7 MYTH- BUSTING Productivity improves with tenure Part time employees are more productive than full time employees Employee referrals are the best way to source new hires High university scores are a good predictor of a high performing employee An international mobility program is an effective way of retaining high potential talent 6

8 THE MEASUREMENT CONTINUUM What is happening? Why and where is it happening? Move from I think to I know Causation Simulations and forecasting Reactive checks Ongoing reports Segmentation and comparisons Correlations Anecdotes Less Powerful Measurement Continuum More Powerful 7

9 CHANGE THE APPROACH TO MEASUREMENT STARTING WITH THE END GOAL IN MIND The typical approach to measurement frequently fails because organisations start with the data available and end users often do not know what to do with the metrics created. Collect data Choose metrics Report metrics Analyse findings Assess impact A better approach is to first determine the impact you want the analysis to have by understanding the key workforce questions that need to be answered and the decisions this information will influence. Determine impact Choose metrics Collect data Report metrics Analyse findings 8

10 WORKFORCE STRUCTURE IS THE OUTCOME OF THREE INTERRELATED LABOUR FLOWS ATTRACTION DEVELOPMENT RETENTION Who comes into the organisation? How successful is the organisation at drawing in the kinds of people it needs to achieve its goals? How do people move through the organisation, through different assignments, jobs and levels of responsibility? How successful is the organisation at growing and nurturing the talent it needs to execute its business strategy? Who is staying and who is leaving? How successful is the organisation at retaining people who have the right capabilities and produce the highest value? Many organisations report on these three areas in silos and struggle to see how they interact. Workforce maps clearly shows the relationship between these three flows 9

11 WORKFORCE MAP This analysis describes key dynamics related to the flow of people into, through and out of an organisation over time, to help answer key questions Headcount (10.3%) (6.2%) Promotions 26 (9.5%) (19.0%) (19.0%) 78 (12.0%) (14.0%) (10.0%) 117 (5.6%) , (21.1%) (19.3%) 169 (16.9%) Career , (29.9%) (22.1%) level Hires Exits 39 (11.5%) (19.2%) (11.5%) Disguised case example. 10

12 SAY/DO ANALYSIS SAY What employees and employers say as measured through: DO How employees and employers actually behave as measured through: Senior leadership and HR interviews Employee surveys and focus groups Company policies Employer social networks (e.g., formal blogs, tweets, etc.) Individual employee records of hires, promotions, transfers, etc. Employee turnover Business performance measures such as customer satisfaction, growth, profit and productivity Understanding of the interplay between perceptions, behaviour and business outcomes 11

13 SOME EXAMPLES OF POTENTIAL GAPS FROM SAY/DO CONNECTIONS SAY Executive interviews results indicate a preference for building over buying talent for top hierarchical levels. In exit surveys, departing employees report pay as primary reason to leave. Employee survey highlights perception that performance level is not related to subsequent salary growth and promotion rates. Employee survey responses show a significant proportion of employees indicate high intent to leave the organisation. DO Workforce maps reveals historic preponderance of external hiring for top hierarchy. Turnover driver analysis indicates that career/advancement opportunities and supervisory factors are significantly more strongly associated with decision to leave than pay. Driver analysis shows that relationship between performance level and salary growth, promotion rates and retention are not only directionally appropriate but strong. Follow-up analysis six months later confirms that about half of those expressing high intention to leave in survey had already left voluntarily. 12

14 COMPARING SAY VS DO ANALYSIS OF ACTUAL BEHAVIOURS SHOW WHAT IS VALUED Percentage point reduction in turnover probability 10% market pay adjustment 1-point rise in unemployment Hire 20% more from employee referrals 0.5% 0.8% 2.2% Surveys suggested that pay elements were most valued by employees, but statistical analysis showed that career development and managerial stability had more retentive impact 10% base pay growth 2.4% 1-year decrease in current position 2.5% Increase jobs performed (from 1 to 2) 3.0% 10% reduction in layoffs 3.5% Supervisor did not leave within last year 7.5% If received incentives 8.0% If promoted within last year 10.0% Disguised case example. 13

15 PREDICTIVE ANALYTICS Using PREDICTIVE ANALYTICS is like driving your car and watching traffic through the windscreen, anticipating traffic, changing your route to avoid traffic jams and getting there faster and more safely. Using DESCRIPTIVE ANALYTICS is like driving your car but watching traffic through the rear-view mirror, not seeing what s ahead and thereby being in danger of crashing. 14

16 PREDICTIVE ANALYTICS Three conditions must be met to show that a human capital factor drives a workforce/business outcome: 1. CORRELATION The factors are related 2. TIME (Directionality) One precedes the other 3. ISOLATION (Controls) Other factors are ruled out YES YES YES Bonus Pay Employee Retention Bonus Pay Employee Retention Bonus Pay Employee Retention Time 1 Time 2 drives Employee tenure Supervisor Span The key is to analyse multiple variables, then isolate only those that directly impact the bottom line Labour Market conditions 15

17 PREDICTIVE MODELLING AN EVIDENCE- BASED APPROACH TO WORKFORCE OUTCOMES This analysis consists of a set of interrelated, statistical models that quantify the impact of particular workforce characteristics or practice on key outcomes... All else being equal* SELECTED DRIVERS Tenure LEAD TO OUTCOMES Base Pay Generation Gender Recruitment Source Higher Degree Career Level High Potential Supervisor Wellness Score Gen Y are 22% less likely to be promoted Women are 15% more likely to receive a high performance rating Those who are supervisors are 35% more likely to receive a high rating and 55% less likely to resign Promotion Pay Growth Total Pay Performance Rating Resignation Absence *The statistical model upon which such results are based accounts for a variety of individual attributes, organisational factors, and external influences for which data are available and includes all active employees in the period examined. All else being equal refers only to the factors accounted for in the model. There may be other relevant factors that are not reflected in the archival data utilised. All effects are significant at the 95% level unless otherwise noted. 16

18 PREDICTIVE ANALYTICS PROCESS OUTCOMES DRIVERS ANALYSIS CAN: Point to actions Understand what is actually driving specific outcomes. Quantify impact Place a value on specific interventions. Test hypotheses Ensure we are recommending the right actions to drive desired results. 17

19 RETAILCO: HOW MUCH DO EXTERNAL AND INTERNAL FACTORS INFLUENCE SALES? DECREASE Percentage Change In Store Sales INCREASE Human Capital drivers 5% newhired this month Employee average hourly pay increases by $1 Manager with 7 years of experience vs 3 years Mall vs frestanding or stripmall 35% More employee per square foot Average tenure 6 months higher Stores employing 10% more full-time employees than average reduce sales by 1% a month. They would be better served by hiring more parttime employees 10% increase in fulltime ees External drivers Store has one floor vs. two Southern, Mid-Atlantic or Mid-West vs. Northeast Local unemployment rate is 8% vs 5% -1.5% -1.0% -0.5% 0.0% 0.5% 1.0% MERCER The 2016 models on which these results are based control for individual attributes, organisational factors, and external influences. 18

20 GETTING STARTED Start with determining the impact of the analysis you want to undertake not what data is easily obtained Developing the knowledge and skills of HR is essential and it is key to not just focus on training one or two individuals It is essential to start small with a specific focus workforce metrics and analytics capabilities take time to build You need to be prepared to justify the investment in metrics and analytics use facts and evidence to help make the right decisions The data may reveal information that is not welcomed people may fear metrics will reveal that programmes they love are not working. This may result in resistance to the programme It is not just about data quality but equally importantly about stakeholder understanding and presentation of results 19

21 20