How digital tools are transforming talent acquisition

Register now

Digital technologies are shaping talent acquisition in significant ways, helping employers make better and more informed hiring decisions. Perhaps nowhere are these tools more sought after than in organizations with high turnover that are saddled with the cost of retraining legions of employees or managers.

Some of the latest applications include using data science to optimize job postings and natural language processing to identify key words or phrases that will attract candidates to employment posting, as well as chatbots to improve customer service for job applicants.

“Historically, the talent acquisition space has had a real challenge, particularly in high-volume industries where recruiters get tons of applicants they can’t personally interact with,” observes Myra Norton, president and chief operating officer of Arena.

Adapting the tools of machine learning and artificial intelligence to hiring in high-turnover industries requires precise, transparent and intuitive processes to be successful, she cautions.

The tools that fit inside of AI, machine learning being one, are incredibly powerful and transformational. However, there’s also fear and skepticism about those techniques creating a black-box effect. “We all have a responsibility to monitor the output of those techniques to ensure that we aren’t having unintended consequences,” Norton says.

Describing herself as “a recovering academic” from Temple University’s College of Science and Technology, she studied early-stage, high-growth tech businesses and ran a startup before connecting with Michael Rosenbaum a former Harvard Fellow who worked in economics and law with the White House in the Clinton administration. His Pegged Software was rebranded Arena in 2016 as part of a mission to capture and apply large amounts of data to create more accurate predictions of a candidate’s performance and, thus, make the labor market run more efficiently.

The factors that contribute to success and fulfillment are tied to a specific job or location, Norton explains, which is why algorithms must be granular and precise. She says it’s also important to make technology for talent acquisition intuitive and easy to use.

“Algorithms can augment and refine human judgment because they can surface risk and opportunity that the human mind can’t,” she notes. But in applying digital technologies to a very human and personal process, Norton suggests that they need to be introduced in an empathetic way that can be road tested.

Predictive analytics help employers forecast with some degree of accuracy whether or not a new hire will work out based on a combination of historical and real-time data, according to Norton. When layered on top of machine learning, she says it allows predictions to be refined in real time for a more accurate view of what’s most likely to happen in the future.

As with all technology-enabled platforms, Norton believes quality assurance is critical to avoid becoming just another piece of underutilized software. Having a transparent demonstration of outcomes will give hiring managers and HR leaders confidence to use these tools “because now they’re seeing this work in their own environment,” she explains.

The chief objective is for Norton to help employer clients achieve at least a 10% reduction in turnover. Arena is beating its own benchmark by averages of 21% and 43% lower turnover in the first and second year, respectively. “We know the impact that that has to the business financially,” she reports, as well as “from a culture, engagement and morale perspective.”

For reprint and licensing requests for this article, click here.
HR Technology Artificial intelligence Machine learning Recruiting