MATTHEW KOSINSKI | November 17, 2016 | While working in the executive recruiting field, Chris Daniels ran into an all too familiar problem.
“We’d source about 100 people for a role, and when we got down to the last two or three candidates, it was always frustrating,” Daniels says. “We had a good recruiting team, but I’d do a lot of interviews where the resume and LinkedIn profile looked good, but when I would get on the phone with someone, I knew in the first five minutes they wouldn’t be a fit with either the hiring executive or the company.”
Often, when faced with cultural fit issues, recruiters and hiring managers turn to assessments – but Daniels and his team were already using assessments. The problem was those assessments were coming too late in the process.
“It’s great to have assessment tools at a much later stage of the process,” Daniels says. “But I wondered if there was any technology we could use to assess a large candidate pool for culture fit so that we could prioritize and rank them. That way, the chances were better that when I got on the phone with someone who looked good on paper, they would actually be a fit.”
And so, Daniels drew on his previous experience in the enterprise software and big data field to create Candidate.Guru, a “culture fit prediction engine” that uses artificial intelligence and big data to determine candidates’ potential culture fits without requiring every single applicant to go through an assessment process.
Culture Fit at the Top of the Funnel
Daniels stresses that Candidate.Guru doesn’t exactly compete with or replace more traditional assessments, which come at the end of the recruiting process when the field has been narrowed down significantly. Instead, Candidate.Guru acts as a sort of first line of defense against bad fits by evaluating cultural alignment at the top of the funnel. That way, recruiters and hiring managers can spend more time with potentially qualified candidates and less time with candidates who turn out to be total mismatches.
“Candidate.Guru is an early-stage tool,” Daniels explains. “When a company posts a job and they get a whole bunch of people applying – or when internal talent acquisition sources a whole bunch of people – they can do some vetting based on skill, but then they can run all of those candidates through our prediction engine. We’ll rank them for fit, and then [companies] can start [the recruiting process] at the top of our list.”
Candidate.Guru works by analyzing publicly available data. The company has access to an attribute database, which was created by one of its partners for completely different reasons.
“We looked at the database and began to experiment with it to see if it was predictive of how people would work together,” Daniels says. “Lo and behold, we found that it was.”
That attribute data comes from a variety of sources, including consumer data and workplace data. It touches on things like personal interests, hobbies, types of technology people use, and average tenure at a company.
“It’s the type of data, which, ultimately, you can get from perhaps some resumes and profiles, but the depth of the data – which is all publicly available data – will still be quite predictive,” Daniels says.
As we know, many candidates abandon application processes when they grow too lengthy. That’s why Candidate.Guru’s choice to forgo traditional assessment forms in favor of a prediction engine that requires no extra effort on the candidate’s part is so important.
“We are in a day and an age where it is very difficult to get people to fill out stuff,” Daniels says. “Yes, you can use gamification to make it more interesting, but the reality is that there is a rich amount of publicly available data that can be used and leveraged to help us get the right people to the right boss and the right spot, and that’s something we think is really important.”
How a technology like Candidate.Guru will impact the wider industry remains to be seen, but so far, all signs indicate that good things are on the horizon. For example, some organizations are already reaching out to Daniels about the possibility of teaming up to make better matches between applicants and opportunities.
“We have a lot of interest from some of the job boards around trying to proactively map people to the right bosses,” Daniels says. “We’re in a tight labor market, and it’s hard to get the best people in the right jobs.”
That task might be difficult, but hopefully, tech like Candidate.Guru will help make it a little easier for us all.