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Real-Life Case Studies of HR Analytics at Work

Advanced HR analytics is reaping benefits for companies at an accelerated rate, if deployed correctly. They are paving way for recruiters to hire smarter, manage better, and seek smart opportunities for themselves to speed up all processes. Here are some real-life case studies where companies undertook HR analytics to override a pressing organizational issue.

Retaining Key Talent @ Nielsen

Nielsen wanted to address the issue of its top talent leaving the company. Its predictive model included 20 variables, including age, gender, tenure, and manager rating. The analysis of various factors exhibited patterns including that the first year of an employee mattered the most. Based on this, the first-year employees were checked whether they’ve had their critical contact points. For example, the first check-in with their manager had to happen within a certain period after hiring, otherwise, it would trigger a notification. A significant outcome of the process was that the people at the highest risk of leaving in the next six months were approached and the company moved 40% of them to a new role. These analytics-based decisions increased an associate’s chance of staying with the company by 48%.

Simple Restructuring Turned Around Performance

The overhead costs of a global logistics company were significantly higher than average, attributed to a complex, oversized organization with a large regional staff and complicated processes requiring multiple handovers. Consulting giant McKinsey reduced this organizational complexity over a period of 5-months through restructuring and achieved new efficiencies that help deliver $2 billion in incremental profit. This was possible based on high-level HR analytics employed undertaken to address the situation.

Accomplishing an Optimum Staffing Level

A large mining company in Zimbabwe was concerned about losing money because of unevenly staffed departments. The experts took an interesting approach in analyzing the under and over staffing situation. They took the number of employees of a business unit and compared this to the business activity of this same business unit, measured over 17 quarters. The relationship between the number of employees and business activity was strong with an R squared of 70.34%. This means that 70.34% of the business activity could be explained by the number of employees. By plotting these two dimensions, the team was able to identify the departments that were overstaffed and understaffed.

Engagement Analytics @ Shell

Shell deployed analytics study to understand the effect of engagement on business performance with better safety standards. The results interestingly revealed that a 1% increase in employee engagement resulted in a 4% decrease in ‘recordable case frequency’, a key industry safety standard. Safety performance was found to be directly related to business performance.

AI-Powered Analytics with Automated Listening @ Unilever

During a hostile takeover bid, the Unilever team analyzed networks in the organization and created models to come up with potential cost reductions. With automated listening, they were able to track the employees’ moods and attitudes. This enabled them to gauge employees’ response to Unilever’s defense strategies. These insights directly helped decision making during the crisis.

Flight Risk @ IBM

At a time, when turnover was high for certain business-critical roles in IBM, the workforce analytics team built an algorithm that included sources like recruitment data, tenure, promotion history, performance, role, salary, location, job role, and more. The company also included employee sentiment, measured through their Social Pulse. The hypothesis here was that engagement with social media might fall when employees are thinking about leaving. The exercise yielded $ 300,000,000 over four years and turnover for critical roles saw a fall by 25%. According to the reports, productivity has also improved while recruitment costs had fallen.

  • Posted Date: 24-MAR-2020