Human capital
It combines candidate-specific information with current employee data, essentially representing a beefed-up screening process that not only looks at CVs, but also trends among would-be co-workers. Among other things, that can be achieved through candidate surveys. More to the point, there’s evidence that CEOs and hiring managers are deeply aware of the importance of successful hiring. As far back as 2013, for example, a study by Harvard Business Review found that 43% of business leaders said investing in people was a priority, while 71% claimed human capital was the leading factor in ensuring sustainable economic value. Today, with businesses’ on-going skills shortage, those figures will likely be even higher, which is why companies as varied as Microsoft and Shell are exploiting analytics in recruitment and talent management to improve their enterprises. Examine the specifics, and the benefits of analytics are just as clear. At Microsoft, for example, managers use data to profile employees with the greatest potential of leaving, allowing them to intercede early and encourage retention through career development, remuneration or other measures to promote satisfaction. Shell, for its part, uses analyses to the betterment of the company in even more sophisticated ways. A good example is when the oil giant asked employees to play video games – and then conscripted psychologists, neuroscientists and data scientists to identify which in-game ideas had the best chance of success. Despite these examples, however, Cappelli argues that many companies don’t have the tools or the big data to really benefit from the technology. That’s especially true for smaller ones, he stresses, which may not have access to the special software to parse vast information sets. At the same time, recruitment analytics clearly has some downsides too. Privacy and cybersecurity concerns are perhaps most obvious here. But from a business perspective, more serious is the risk that an employer misses talent by leaning too heavily on AI – or even uses a platform that unfairly discriminates on the basis of race of sex. It goes without saying that these theoreticals could quickly become a legal nightmare if not kept in check. That is shadowed by broader challenges, for instance the fact that some candidates may feel uncomfortable applying or working for a company that uses AI and data.
The ball is in their court Despite these worries, however, the impact HR analytics is having on the recruitment market and people management is obvious. A 2019 study by the Corporate Research Forum found that 69% of
Finance Director Europe /
www.ns-businesshub.com
mid-sized organisations now had a team responsible for people analytics. That’s even as skill and labour shortages continue to ramp up recruitment pressures. To put it another way, the ball is increasingly sitting in the court of candidates themselves – with potential employees able to be hyper-selective about who they work for and what they do.
“The biggest change in a tighter labour market is to identify and contact candidates directly, so-called ‘passive candidates’ who might be interested if we contact them.”
“That is why there is this focus on the candidate experience,” Cappelli stresses. “The biggest change in a tighter labour market is to identify and contact candidates directly, so-called ‘passive candidates’ who might be interested if we contact them.” To do that, Cappelli says, recruiters need to be clear on what sort of candidate they actually want as well as how they plan to find them. They then need to figure out an approach that would make them apply or accept a position. Conversely though, casting the net too wide is almost as dangerous. As Cappelli says, having too many inappropriate candidates can be a hindrance too. The search for talent has changed significantly throughout the past few years – and so too have the tools to do it. “If your goal is to hire good people, using data is the only way to get better,” Cappelli summarises. “If you do this poorly – typically relying on criteria that you like but for whom there is no evidence that it can predict – you will spend time and money getting candidates who quit early, who cannot do the job well and who will otherwise fail.” ●
46
The typical number of days it takes to fill a vacancy in financial services in the US.
LinkedIn Workforce Insights 71
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Shutterstock.com
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