5 Reasons Candidate Rediscovery Is Recruiting’s Next Big Thing

Glen Cathey calls it the #1 mistake in corporate recruiting: the failure to leverage candidate rediscovery.

Candidate rediscovery is the practice of mining your existing resume database to find candidates for open reqs.

Watch our video on how recruiters are using AI to rediscover candidates in their ATS:

 

Although most ATSs have had a rudimentary form of internal search for a while, it’s only recently that advances in software have allowed recruiting teams to find and match previous candidates to an open req easily, quickly, and accurately.

In 2017, there are 5 reasons why this type of talent rediscovery is poised to become the next big thing in recruiting.

Reason #1: Employers state one of the biggest mistakes recruiters make is failing to look at candidates in their own database

A recent CareerBuilder survey found that one of the biggest complaints employers have about recruiters is the failure to look at candidates in their own database.

This complaint is a bit unfair when you consider that a typical ATS just wasn’t designed to have this type of internal search functionality.

Although your ATS may allow you to use keywords and Boolean searches to search through your existing resume database, they are usually limited and error-prone.

This makes it difficult or even impossible to match previous applicants to an open req even if those applicants were matched to a similar role previously, unless you’re using a dedicated candidate rediscovery tool.

Reason #2: 95% of recruiters predict the job market will remain or get more competitive

The improving economy means nearly everyone predicts it will become even tougher to attract talent in 2017.

In today’s competitive candidate-driven market, finding talent will depend on a recruiter’s ability to leverage new sources of candidates.

What makes more sense than sourcing candidates from a pool who have already expressed interest in your company and role in the past?

The savings in both your time and in the employer’s recruiting costs is a win-win.

Reason #3: 65% of resumes received for a high volume role are completed ignored

In Jobvite’s 2016 survey, recruiters report receiving more than 250 applicants for high-volume roles on average.

For the typical time-constrained recruiter, that means the majority of resumes received get collected into an ATS but are are never looked at.

candidate rediscovery helps solve the resume ignore problem

This “cost of ignore” problem represents hundreds, thousands, and even millions of resumes that the recruiting department has invested considerable amounts of money, time, and effort collecting only to sit neglected in an ATS.

Instead of a keyword or Boolean search, candidate rediscovery uses a machine learning algorithm. You enter a job description of a current req and the algorithm will automatically screen every resume in your ATS to find the most qualified matches.

Reason #4: A recruiter spends 6 seconds scanning a resume on average

TheLadders conducted a study that found the average amount of time a recruiter spent scanning a resume is 6 seconds. This is pretty close to what recruiting experts such as Matt Charney recommends, which is 8 seconds per resume.

But however efficient you might be at scanning resumes, some qualified candidates will inevitably slip through the cracks into the ATS black hole.

A tool like candidate rediscovery means you don’t have to spend more time manually scanning resumes in order to prevent these qualified candidates from slipping through your fingers.

Reason #5: Only 36% of candidates are active

According to LinkedIn’s 2016 Global Talent Trends, although 90% of working professionals would be open to hearing about new job opportunities, only 36% of candidates actively search for a new job.

That means a typical job posting will only attract about a third of the potential talent pool who might be interested in your open role.

With hiring volume predicted to increase but recruiting teams predicted to remain the same size or shrink, recruiters need additional resources like candidate rediscovery to find qualified candidates in 2017.

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Ji-A Min

Ji-A Min

Head Data Scientist at Ideal
Ji-A Min is the Head Data Scientist at Ideal. With a Master’s in Industrial-Organizational Psychology, Ji-A promotes best practices in data-based recruitment. She writes about research and trends in talent acquisition, recruitment tech, and people analytics.
Ji-A Min

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