HR Analytics- Driving Return on Human Capital

Published by MBA Skool Team, Published on July 06, 2016

Analytics is no longer an arena open to only marketing, sales and product development. HR has now embraced the fact based approach to prove its worth. Pioneering companies such as Cognizant, Best Buy, Xerox, Sysco etc. have all adopted big data analytics in HR as a saviour to ensure highest levels of performance, productivity, engagement and retention of the top talent to gain a competitive edge. 71% of CEOs view human capital as top factor contributing to growth and people costs often approach 60% of the corporate variable costs. No longer can the HR rely on trust and relationships and thus companies have started to give more weightage to analytics rather than gut instincts.

If analytics can help me estimate that by 0.1% increase in engagement, I would add $100,000 in my store’s annual income (as analytics suggested in the case of Best Buy) or points out to the fact that blogging helps my employees feel more engaged, improving their performance by an additional 10% (as in the case of Cognizant) or that, an ability to take initiatives warrants much higher performance than any other criteria or helps me identify the factors that predict which employees would leave in a relatively short span of time (Sprint) and then helps me cut the attrition rates by 20% (Xerox) or that the turnover rate was higher due to the employees being paid salaries at lower end of pay grade even though salaries were market competitive (Juniper) then I can surely develop and deploy a highly competent and responsive workforce with increased profits and maintain cost as well as operational efficiency. It would imply that my actions would be no longer reactionary but calculated. And this is why, a 2013 SAS study predicts that 6,400 organizations with 100 staff or more will have implemented big data analytics by 2018.

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Now how does this panacea work? Big Data Analytics is primarily based on: external market forces and internal organizational factors scanning; workforce planning; producing i.e. applying statistical analysis to uncover cost effective combinations of input; and predicting causal and correlational aspects of strategic, operational and leading factors. Thus, in short this Big Data Analytics not only needs to record the current huge amount of data which is enormous in volume and variety but also relate it to the organization’s mission, benchmark results, understand past behaviours and outcomes by descriptive analysis and predict future likelihoods by predictive analysis. Big data analytics drives return on human capital by workforce forecasting which ranges from short term demand for hourly employees to demand after a decade, selecting the right hire by selecting on the basis of traits that distinguish high performers, job response optimization and analysis for cost per placement. These are known as recruitment analytics. There also exists analytics for employee attrition and retention which accurately predicts the attrition risks of current employees.

Churn Prediction, which is one of the most widespread big data use case in business, creates analytical models which identifies employees who could leave organisation early, so managers can rapidly change behaviour and work conditions to keep top performers from leaving. Also, correlating pay increase, promotion wait time, performance and attendance with resignations; comparing how resignation rates vary across functions, locations and tenure; analysing past data to identify factors responsible for attrition and tweak the company’s retention policies are its other implications. In a similar manner, data can be analysed for performance, compensation and incentive analysis and workforce alignment. Another method which could help an organization determine actions that could have a major impact on the business performance is Human Capital Investment Analysis. For e.g. Sysco began this analysis with three measures for each of its operating unit which included retention; productivity; work climate and employee satisfaction. Sysco further increased these measures to cover seven other dimensions. It was thus revealed by the analysis that the operating units with highly satisfied and engaged employees have higher employee retention, greater revenues, superior customer loyalty and lower costs. This helped Sysco efficiently identify what actions will thus have the maximum impact on the performance of business. By this, Sysco saved nearly $50 million in hiring and training costs for new associates.

Thus, if Big Data Analytics are used in the right manner and direction by organizations it can create a magnanimous amount of tangible and intangible value for them and help them grow immensely in the globalized and dynamic business world.

This article has been authored by Aakanksha Tanwar from Goa Institute of Management



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