3 Ways Data Shapes the Talent Strategy at Tesla, Chevron, and LinkedIn

March 30, 2017

The head of Talent Acquisition at LinkedIn, Brendan Browne, is quick to admit that using data and talent analytics has completely changed the way his department is perceived by the business. Thanks to talent analytics, Brendan’s team went from under-resourced purple squirrel hunters to trusted and indispensable advisors.

If you’re not sure what talent analytics is or how it can benefit your recruiting or HR department, below are some examples of what companies like LinkedIn, Chevron, and Tesla have done with data to drive smarter decisions. And no, you don’t have to be a big company in order to use data to your advantage—according to the talent analyst pros, you can be dangerous just with an Excel spreadsheet and a smart idea.

As a bonus to the three awesome examples below, Will Gaker from LinkedIn’s talent analytics team and RJ Milnor, Head of Talent Analytics at Chevron also visited our Talent on Tap set where they added more context around the importance of talent analytics:

So, what is talent analytics?

In Will’s words, talent analytics strives to be “HR’s nerdy best friend.” On a more-serious note, RJ defines it as “supporting and informing business strategy through people data.” While it might seem intimidating or abstract from afar, talent analytics isn’t about arcane algorithms—it’s about supporting concrete strategies and solving real problems you’re dealing with now.

Seeing is believing, so let’s dive into three examples of talent analytics in the real world.

1. 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. But in the first quarter of 2016, the talent analytics team realized it was losing engineers faster than expected. As the first line of defense against any troubling trends, talent analytics jumped into fact-finding mode: 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) from September 2015, 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.

Crunching the numbers, talent analytics 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.

Actionable insights in hand, the talent analytics team worked with HR to develop a tactical, targeted solution. The findings kickstarted a new project that encourages managers to have more career conversations with each direct report.

The results? The attrition rate of engineers enrolled in HR’s new program was almost cut in half compared to the control group—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.  

2. Chevron forecasts its talent supply and demand with impressive accuracy

When the talent analytics team at Chevron was created in 2014, one of the first actions it took was creating a mission statement—a guiding principle that it still uses today. The team’s mission is to “support Chevron’s business strategies with better, faster workforce decisions informed by data.” Importantly, that mission makes clear that the data serves to inform decisions—it’s never data for the sake of 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 country-specific attrition models, probabilistic scenarios based on business plans, and the relationships between business drivers and staffing needs, Chevron’s system can predict future talent supply and demand across different locations. When back-testing against actual historical data, the attrition models prove to be more than 85% accurate. The entire system is a stunning achievement in cross-divisional teamwork spearheaded by the talent analytics team.

3. Tesla gains counterintuitive insights into its employee referral programs

Elon Musk’s Tesla Motors is known for thinking big, blazing trails, and pushing technology into new frontiers. Why would you expect anything less of the company when it comes to talent analytics? True to form, the team 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).

Many talent teams have heard that “A players” refer other “A players,” and “B players” refer “B players,” but Tesla’s analysis found that it’s a little more complex than that. Yes, A’s refer A’s—but B’s actually refer C’s, and C’s refer A’s, B’s, and C’s. The thinking goes that A’s are confident and intrinsically motivated enough to refer top-quality colleagues, while B’s look to hire lesser counterparts so they aren’t outshined, and C’s… well, Boryana isn’t quite sure why C’s indiscriminately refer A’s, B’s, and C’s, but the unexpected insight is still super useful. With an eye to these counterintuitive trends, Tesla is able to optimize its referral program and better evaluate incoming referrals.

Final thoughts

While analytics can seem daunting at first, it really doesn’t have to be. Yes, these companies are big and have been at it for several years, but every company can start small and work their way towards tracking talent flows and using data to set goals, track progress, uncover actionable insights, and drive more informed recruiting strategies.

If you haven’t already, kick things off by mapping out your headcount year over year, your diversity numbers (gender, age, ethnicity), and attrition rates to understand the overall shape of your company. Where are the gaps? Once you have the map, you can sit down with HR and start making changes that will take your company forward.

Talent on Tap is a weekly series where Pat Wadors and Brendan Browne break down some of the hottest topics, biggest challenges, and most enticing opportunities in the world of talent. Talent on Tap will also give you an opportunity to hear from other organizational leaders, subject matter experts, and thought leaders in the space. Stay tuned each week for the latest.

*Image from Tesla

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