Announcing: data-driven talent matching for employers

The average job opening gets 250 applications. But 50% on average aren’t even qualified. How do employers cut through the noise?

About 6 months ago, we set out to solve a problem. Thousands of job seekers were using TransparentCareer every month to research career paths and explore companies, but then…nothing. We had no way to connect them with firms, or help them land their dream job based on the data on our platform. And when we talked to them, they were, on average, incredibly dissatisfied with the way they applied to jobs elsewhere. They reported a noisy, impersonal experience — submitting resumes into black-hole job boards, spending hours tailoring cover letters, etc.

So we set out trying to connect the dots between the use of data to research careers (which our users loved) and actually getting a job (which our users hated). First, we had to talk to the employers hiring these folks to see if they suffered from similar problems. After talking with dozens of recruiters from firms large (Goldman Sachs) and small (boutique consulting firms), over hundreds of hours of research, we found out that recruiters typically suffer from 1 of 2 pain points:

  1. They get too many applications and spend countless hours sifting through them to find the right candidates
  2. They get too few quality applicants and have to resort to posting their jobs all over the place (or hiring expensive search firms)

The common thread? There’s too much noise. If you’re one of the Googles of the world, you get thousands of resumes, and have to go through them. If you’re Anonymous LLC, you don’t get many applications, so you have to go “spray and pray” methods — spending precious time you could be using to sell the right candidates on your firm.

I don’t want to post to 200 job boards…I want to get relevant candidates.

So then we were left with the question: how do we fix the problem for employers (applicant noise) while maintaining our data-driven career research approach for users?

Netflix recommends TV shows and movies, out of millions of titles — using data. Amazon tells you which books (and really, which everything) you might like — by using data. Match and eHarmony go as far as to recommend you potential mates — with data.

The lesson? Better use of data helps these industry leaders cut through the noise. It’s about time we put data to work in recruiting tech.

Current recruiting platforms, though, use data that’s inherently limited, because it’s mostly based on a candidate’s attributes— static, past-looking resume bullets that tell the story of what they’ve done, not who they are. Not to mention opening itself up to other issues, like inherent bias.

We decided to take a different approach, adding richer, forward-looking data like a job seekers preferences (locations, job functions, industries) and values (what they feel is most important to fit well in a role or company).

An example search: “find me people in product management, with 4–6 years of experience, located in Chicago, with an MBA degree preferred.” This might yield you 200+ individuals. Where to begin?

Now you can add, “Only show me those actively looking for a job. And prioritize those interested in working in my industry (e-commerce). Also, our company does really well on work-life balance but our compensation bands are just average — show me people who care most about what we’re good at.” Now you’ve whittled it down to maybe 20 candidates.

The goal? Save recruiters time looking at hundreds of unqualified resumes, and help companies assess both interest and culture fit before even interviewing candidates.

Early results have been compelling. During our private beta this spring, our partner employers generated:

  • 80% response rate from applicants invited to interview
  • 40% interview rate from TransparentCareer applicants (vs. 15% of all other applicants)
  • ~10 hours/week saved sourcing candidates, visiting campuses, posting jobs to multiple job boards, and sifting through unqualified resumes.
  • An average of 25 pre-qualified applicants per job posting — with demonstrated interest — vs. ~220 from other methods

So how do we do it?

After a few months of research and mining our data, we developed our Match Score algorithm, which shows how likely a candidate is to fit with your organization.

We take what the job seeker cares about, combined with what your employees have said about your company, and our algorithm gets to work. You get one, simple, easily digestible number, showing you how the candidate matches with your values on a 100-point scale. After you’ve had a look at their profiles and resumes, invite them to apply directly.

Search candidates using attributes (past work experience, degree obtained), or just filter to ones interested in your company.

If you’re getting too many applications, you’ll be able to use our Match Score and interest filters to identify the best candidates instantaneously. If you’re one of us smaller companies, you’ll be able to expand your reach, identify the right candidates, and save yourself a ton of time and money making the right hire.

To learn more about our data-driven approach to recruiting, get in touch and try it risk-free. We don’t bite :)

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