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
|Canadian HR Reporter|Last Updated: 10/20/2015

In 1954, the great management theorist Peter Drucker wrote, “The days of the ‘intuitive’ manager are numbered.” He could not have been more prescient — looking at how finance, supply chain, marketing and sales now systematically use data and analytics to improve performance, it is clear every department needs to rely on objective methods. Reporting, benchmarking, statistics and forecasting methods now help executives make informed decisions. 


Using data in human resources is not new — but its business purpose is. After all, HR has been measuring certain talent indicators for awhile, such as employee engagement, satisfaction with training, headcount, turnover and performance management distributions. 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 satisfaction or optimizing cost, then we are not contributing to the performance of the organization.  


Moreover, HR data should not only help achieve business results, it should also help anticipate rather than react — knowing in advance who to hire, who to promote and how to help employees be successful. 


We can’t drive an organization forward by looking in the rearview mirror. The value of analytics thus resides in its ability to recognize trends, predict outcomes and anticipate risks. 


Workforce analytics — or the use of analytical methods for comprehensive workforce performance measurement and improvement — is a commitment to three fundamental values: 


• the use of evidence, instead of intuition, in HR decision-making


• the direct alignment of HR programs with business outcomes, rather than a validation of HR’s own effectiveness


• the willingness to invest in forecasting 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, performance 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 indicate they do this. For the companies that take the time and effort 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 business would be top performers in terms of gross margin percentage by looking at employee engagement and performance excellence (a measure of continuous improvement practices). This is respectively a two- and four-percentage-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 characteristics 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 training costs and increase profitability — all notable increases in an industry where small changes in margin have a major impact on the organization as a whole.


Sometimes, workforce analytics insights lead to unexpected discoveries. Just to cite one example, 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 organization earlier — twice the rate compared to those who scored a nine and lower. 


It concluded the highest scorers 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 analytics identified the most statistically significant drivers toward attrition to identify those at risk of resigning. 


The number one predictor was salaries lower than the average among their peer group. This factor was found to be the most significant and was used as the primary predictor of employee attrition and main factor in identifying candidates for retention payments. This program has resulted in net savings of $131 million. 


Turnover is also an opportunity to upgrade talent. A retailer that replaced employees who had below-average test scores in pre-hire assessment with new hires who had above-average scores actually 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.  


There 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 people issues to business success. Be clear about the scope of analytics in your organization — for greater impact, emphasize advanced analytics, such as predictive capability — and identify a project that will deliver a quick win to demonstrate early success.


• Understand the cultural and legal perspectives on data privacy in your region.


• Identify executive stakeholders and understand their key business 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 accessing data and select the appropriate tools for analyzing the data.


• Consider the benefits of cloud technologies for delivering analytics software as a service and minimizing capital 

expenditures.


Phase III (60-90 days): Grow your capability


• Ensure the HR analytics team has a balance of skills — HR expertise and business savvy, in addition to analytical skills.


• Complete a business case and think about the analytics function as an internal consultancy.


• Link analytics work to business outcomes.


• Spread the word about your analytics projects and the insights generated.


The 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 interventions undertaken as a result of insights from workforce analytics.


Workforce analytics has the power to challenge the received wisdom, influence managerial behaviour and help leaders use evidence in decision-making. Building 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 Business in Toronto. He consults with HR leaders to bring innovative practices, evidence-based techniques and workforce analytics in the workplace. 

Add Comment

  • *
  • *
  • *
  • *