According to Gregory Lewis, the head of Talent Acquisition at LinkedIn, Brendan Browne, confirms that using data and talent analytics has changed the way his department is perceived by the business.
So, what is talent analytics?
RJ Milnor defines it as: “supporting and informing business strategy through people data.”
3 examples of talent analytics in the real world
LinkedIn taps into talent analytics to increase employee retention
As a tech company with a user base of nearly half-a-billion professionals, LinkedIn relies on software engineers to build amazing online experiences.
The first task of the talent analytics team was to find out what was driving attrition among engineers.
If you asked the managers, they’d guess compensation—but the data pointed to a different reason. Turning to an employee viewpoint survey (EVS), LinkedIn’s talent analytics team saw that about one in four workers hadn’t had a meaningful conversation with their manager about career development in the last six months.
It also revealed that the engineers who left rated “manager effectiveness” significantly lower than their counterparts who stayed. In short: engineers were leaving in part because they didn’t feel like managers gave enough support or helped advance their careers.
The talent analytics team worked with HR to develop a tactical, targeted solution.
The results? The attrition rate of engineers enrolled in HR’s new program was almost cut in half and participants also reported significantly higher manager effectiveness.
With the help of HR, the analytics team was able to quickly translate data-driven insights into action.
Chevron predicts its talent supply and demand with 85% accuracy
When the at Chevron’s talent analytics team mission is to “support Chevron’s business strategies with better, faster workforce decisions informed by data.”
As Head of Talent Analytics at Chevron, RJ sees an industry-wide shift from talent metrics to analytics.
“Not very long ago, we were looking at what are the key metrics we need to track, and then getting that out,” he says. Metrics are important, but analytics takes things to the next level, connecting those numbers to real strategies and problems: “With analytics, we’re transforming data to provide insight that informs a decision,” says RJ, “including decisions that may involve multiple questions and require various scenarios or sensitivities.”
Decisions, for example, like choosing how many workers are required with the right skills, at the right place, and at the right time to achieve business objectives.
To inform these critical decisions, RJ’s team worked closely with HR, strategic workforce planning, and other business units to create an incredible solution. Together, they built a system that forecasts Chevron’s talent supply and demand over the next 10 years.
By using probabilistic scenarios based on business plans, and the relationships between business drivers and staffing needs with 85% accuracy.
Tesla gains insights into its employee referral programs
Elon Musk’s Tesla Motors is known for thinking big and pushing technology into new frontiers. In a similar fashion, Tesla realized its employee referral program represented “a rich dataset that could be mined for meaningful trends,” wrote Boryana Dineva, then Tesla’s Head of HR Systems, Operations, and Data Analytics.
The research confirmed some common sense assumptions (e.g., referred employees tend to stay longer), while also revealing more counterintuitive truths (e.g., that referring employees also stay longer—and employees whose referrals get hired stay longer still).
Start tracking talent flows (diversity numbers (gender, age, ethnicity), and attrition rates) and using data to set goals, track progress, uncover actionable insights, and drive more informed recruiting strategies.
Once you have the map, you can sit down with HR and start making changes that will take your company forward.