Top 2 Ways Machine Learning can Change Recruiting

Each new year brings the topic of Recruiting or Talent Strategy to the forefront of business news. My discussions with other Directors of Talent Acquisition have shifted from strategies to tools to help us meet the needs of our businesses. The impact machine learning can have in recruiting is very exciting.

Machine Learning is becoming one of the most influential and powerful technologies in the world. Traditionally, software engineering combined human created rules with data to find answers to a problem. Machine learning uses data and answers to discover the rules or reasons behind the problem. The amount of data available to recruiters is so immense, it can cause our brains to shut down and miss valuable insights that a tool like machine learning can provide.

Here are two ways machine learning can change recruiting.

Improve Recruiter Efficiency and Candidate Experience

Companies are training machine learning algorithms to help employers automate repetitive aspects of the recruitment process such as resume and application review. Imagine the day when you arrive to find your AI Buddy had been working all night to screen and sort your top applicants. Recruiters would be able to focus more time on value added interviews compared to today’s world wrought with frantic and chaotic processes that are inefficient and detrimental to the candidate experience.

Ideal is one company that offers machine learning and AI to help transform the recruiting process. The firm claims its virtual assistant is already trained on “millions of past hiring decisions” so it can quickly adapt to each new client’s recruitment process. Examples of decisions may include advancing applicants to the interview stage and hiring candidates. 

According to Ideal’s website, one of its larger clients reported 71% reduction in cost of hire and tripled the number of qualified candidates. The case study cites Ideal’s ability to automatically review candidates based on previous hiring decisions and then notify candidates of their status. This not only reduced the amount of low-impact activities for the recruiting team members but also increased the candidate experience. You can watch their testimonial here.

Take Sourcing into Warp Drive

There are a number of startups in the recruiting space focused on how to leverage machine learning to ramp up the sourcing efforts for recruiters. Machine learning can leverage past information to help predict which candidates may be the best fit for your organization. While this is an amazing step up from our current sourcing strategies, machine learning could also identify candidates who are perfect for your organization and are more likely to take your call. This would be a game-changer for recruiting!

Entelo is one example of a talent sourcing software company reportedly using machine learning to help recruiters discover quality candidates. The company claims its proprietary algorithm, More Likely to Move™, is capable of identifying individuals who have a 30 percent likelihood of changing jobs within the next 90 days. In one case study , Entelo reports one of its clients was able to improve its discovery of qualified, diverse candidates. The client reported an increase in its diversity efforts, raising its female hires from 40 percent to 47 percent and minority technical hires from 1.5 percent to 11 percent.

Photo by Franck V. on Unsplash

A report by Glassdoor suggests that 66 percent of millennials anticipate leaving their current jobs by 2020. With unemployment being at a ten year low and the high costs of turnover becoming targets for business expense control measures, there will be even more pressure on Talent Acquisition professionals to up their game. AI recruitment tools using predictive analytics and machine learning to recommend candidates will become the differentiating factor in the war for talent.

I am excited by the changes I’ve already seen in the industry and plan to spend a lot of time exploring new tools in the AI and machine learning space in 2020.

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