Canadian HR Reporter

CANADIAN HR REPORTER October 19, 2015 20 FEATURES HR METRICS Workforce analytics about looking forward, not back Examples of employers that are doing it right, and tips on how to get started By Benoit Hardy-Vallée I n 1954, the great manage- ment theorist Peter Druck- er wrote, "e days of the 'intuitive' manager are num- bered." He could not have been more prescient — looking at how finance, supply chain, mar- keting and sales now systemati- cally use data and analytics to improve performance, it is clear every department needs to rely on objective methods. Report- ing, benchmarking, statistics and forecasting methods now help executives make informed decisions. Using data in human resources is not new — but its business pur- pose is. After all, HR has been measuring certain talent indica- tors for awhile, such as employee engagement, satisfaction with training, headcount, turnover and performance management distri- butions. Advances in technology made this wealth of information more manageable. All this data, however, will not serve a larger business purpose if it is not used to acquire or delight a customer. If we are not reducing risk, improving customer satisfac- tion or optimizing cost, then we are not contributing to the perfor- mance of the organization. Moreover, HR data should not only help achieve business re- sults, it should also help anticipate rather than react — knowing in advance who to hire, who to pro- mote and how to help employees be successful. We can't drive an organiza- tion forward by looking in the rearview mirror. The value of analytics thus resides in its abil- ity to recognize trends, predict outcomes and anticipate risks. Workforce analytics — or the use of analytical methods for comprehensive workforce per- formance measurement and im- provement — is a commitment to three fundamental values: •the use of evidence, instead of intuition, in HR decision-making •the direct alignment of HR pro- grams with business outcomes, rather than a validation of HR's own effectiveness •the willingness to invest in fore- casting capabilities rather than reactive and descriptive ones. Overall, it is still a work in progress. IBM's latest C-suite study indicated less than 60 per cent of organizations are truly tackling workforce analytics. Employee engagement, perfor- mance management and talent retention top the list in the use of historical data. And the most common area for the use of predictive analytics is workforce productivity — but only 16 per cent of companies in- dicate they do this. For the com- panies that take the time and ef- fort to improve analytical abilities, the return on investment is clear. Take KPMG, for example: An IBM analysis revealed it could predict which parts of its busi- ness would be top performers in terms of gross margin percentage by looking at employee engage- ment and performance excel- lence (a measure of continuous improvement practices). is is respectively a two- and four-per- centage-point difference, which means millions of dollars added to the bottom line. At an entertainment company, leaders recognized training alone wouldn't be enough to generate higher profitability. Concessions sales are a key part of its business model. So the company developed a model to identify the character- istics of its highest performers in terms of concessions sales and incorporated the findings into its applicant-tracking platform. As a result, the company was able to decrease turnover by 43 points (from 127 per cent to 84 per cent), decrease overall train- ing costs and increase profitabil- ity — all notable increases in an industry where small changes in margin have a major impact on the organization as a whole. Sometimes, workforce analyt- ics insights lead to unexpected discoveries. Just to cite one exam- ple, a financial services call centre found the highest-performing new hires who achieved a score of 10 out of 10 in a job simulation were in fact leaving the organiza- tion earlier — twice the rate com- pared to those who scored a nine and lower. It concluded the highest scor- ers were overqualified for the job for which they were hired. So the organization changed its hiring criteria to reduce attrition costs and ensure longer retention in the job class for which it used the simulation, steering away from those people who appeared to be overqualified. Turnover is typically a metric HR leaders tend to minimize. At IBM, for example, in-depth ana- lytics identified the most statisti- cally significant drivers toward attrition to identify those at risk of resigning. The number one predictor was salaries lower than the aver- age among their peer group. is factor was found to be the most significant and was used as the primary predictor of employee at- trition and main factor in identify- ing candidates for retention pay- ments. is program has resulted in net savings of $131 million. Turnover is also an opportunity to upgrade talent. A retailer that replaced employees who had be- low-average test scores in pre-hire assessment with new hires who had above-average scores actu- ally saw a six per cent increase in stores' controllable profit. If turnover is "functional" (the leavers are not high performers), then it can be an opportunity to enhance talent and improve performance. ere are no simple recipes for creating these capabilities in an organization. We have identified, however, a few steps to guide an employer in the first 100 days of its analytics journey. Phase I (first 30 days): Set your direction • Develop a vision that links peo- ple issues to business success. Be clear about the scope of analytics in your organization — for great- er impact, emphasize advanced analytics, such as predictive ca- pability — and identify a project that will deliver a quick win to demonstrate early success. • Understand the cultural and le- gal perspectives on data privacy in your region. • Identify executive stakeholders and understand their key busi- ness challenges. Phase II (30-60 days): Define your approach • Identify available data that can be analyzed to address business challenges. • Do not wait for perfect data, but do rely on subject matter experts to assess data quality. • Agree on procedures for access- ing data and select the appropri- ate tools for analyzing the data. • Consider the benefits of cloud technologies for delivering analytics software as a ser- vice and minimizing capital expenditures. Phase III (60-90 days): Grow your capability • Ensure the HR analytics team has a balance of skills — HR exper- tise and business savvy, in addi- tion to analytical skills. • Complete a business case and think about the analytics func- tion as an internal consultancy. • Link analytics work to business outcomes. • Spread the word about your an- alytics projects and the insights generated. e ongoing journey (Day 90 to 100 and beyond): Implement • Analyze the links between the different identified data sources. • Guide analytical models to link HR practices to workforce and business outcomes. • Ensure that actions are taken based on insights delivered from workforce analytics. • Evaluate the impact of interven- tions undertaken as a result of in- sights from workforce analytics. Workforce analytics has the power to challenge the received wisdom, influence managerial be- haviour and help leaders use evi- dence in decision-making. Build- ing the capability requires some effort at first but, over time, the value provided to the organization eclipses this initial investment. Benoit Hardy-Vallée is the practice lead and thought leader for IBM Smarter Workforce & Social Busi- ness in Toronto. He consults with HR leaders to bring innovative practices, evidence-based techniques and work- force analytics in the workplace. HR data should also help anticipate rather than react — knowing in advance who to hire, who to promote and how to help employees succeed. Credit: cifotart (Shutterstock)

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