Announcing Self-Serve, Automated Reference Assessments for Finding Top Job Candidates
Head of Marketing
We’re thrilled to announce the release of new self-serve capabilities for talent and recruiting teams looking to find the best candidates for their open positions. Using self-serve and automated reference assessments, recruiters can now get peer-validated insights within 36 hours that help them see who’s most likely to be a good fit, and make a faster, better hire.
Hundreds of Candidates. One Job.
With tens of thousands of job seekers on the market, it’s become more difficult than ever for recruiting teams to find the best candidates. For a single job, it’s not uncommon to see hundreds of applicants – and recruiters have to sift through these to determine who’s worth a conversation. Add onto that the advent of generative AI and recruiters end up with lots of resumes and cover letters that increasingly look alike.
So how do they break through the noise? The answer is one we’ve all known all along: tapping into the human element. Better data given to recruiters from a candidate’s peers help a candidate break through the noise and make it easier for recruiters to identify potential top candidates. Instead of relying just on quantitative highlights on a resume, recruiters can see how soft skills and working styles might impact a candidate’s post-hire performance. While reference checks are typically used at the end of the hiring process, it also provides tremendous value to recruiters sifting through all the applicants to find the best ones to talk to, and fast.
But there’s a problem inherent in that. This level of data has historically only been available at the tail end of the interview process using reference checks. And when is the last time any recruiter has had a negative reference call?
Evaluating Soft Skills & Working Styles in the Hiring Process
Even though reference phone calls tend not to provide much useful information, they’re often a requirement to verify a candidate is who they say they are, and sometimes it’s required for compliance. At the same time, sometimes references are so onerous that companies have to have a full-time role just to do them.
But what if the script was flipped? In a world where everyone can use AI to improve their resumes and cover letters, what if peer-validated insights could be brought into the hiring process? Research has repeatedly shown that soft skills and working styles are more predictive of whether a candidate will be a good hire than hard skills assessments. Other research also backs up how gathering ratings from observers is far more predictive of someone’s performance than a self-report. Compare this to the status quo, where the traditional interview and reference process at best predicts post-hire performance 40% of the time – that’s worse than a coin flip.
This brings us to two problems that need to be solved:
- How can a recruiting team assess soft skills and working styles sooner?
- How can reference data be less tedious so it can be used for all candidates?
In an ideal world, recruiting teams would be able to use real human insights to break through the hundreds of look-alike candidate applications. After all, hearing from people who have worked with a candidate for years is going to be the best indicator of who the candidate really is. It’s that human element that needs to be used to differentiate between someone that just looks good and someone who is a superstar.
Automated Peer-Validated Insights & AI-Powered Predictions
Automation and AI can help to cut through the noise and help identify the right hires for any role – and more quickly than ever. Searchlight Reference Checks can be used in a self-serve manner, and within 15 minutes any recruiter will be set up to send any candidate a request to provide reference data. They can also optionally request a candidate self-assessment to see how a candidate’s view of themselves aligns against what peers had to say.
Once the candidate’s references are complete, recruiting teams and hiring managers get structured, quantified data about the candidate’s soft skills and working styles. This data becomes an invaluable part of making a hiring decision. What’s more, Searchlight uses AI trained on over 100,000 post-hire outcomes that can predict with 80% accuracy whether or not a candidate will be a top performer at the organization. That’s at least a 2X increase over the predictive ability of the traditional process, though many customers are finding it’s improving their ability to make great hiring decisions by 4X.
How To Start Collecting References Automatically
Getting started with Searchlight’s self-serve product is easy. All you need to do is sign up for free and follow the wizard. You can also follow the video at the top of this article which will walk you through the entire process.