Can You Hire Better Than Google?

As long as I can remember, everyone in HR and Talent has wanted to be like Google.  Every conference you attend, every article you read, it all comes back to how great Google is at being an employer.  You begin to believe that they have it all figured out, and the rest of us are just playing catch-up.

I was no different.  I wanted the recipe for the secret sauce.  I sought out contacts in HR and Talent at Google, attempting to build relationships so I could get the inside scoop on how they actually pull off being “the chosen one.”  Then, I saw this one little tweet from Glen Cathey:

Glen Cathey tweet

Now, it all makes sense.

Google is just like you and me. They have no freaking clue! 1% better than a 50/50 chance!?  Are you kidding me? A chimp could have those odds!

Basically, what Google is saying is we didn’t even have to really interview any candidates over a five-year period of time, and our success of hiring would have been virtually the same.  We could have just brought in two candidates who applied for each position, sat them down at a table together, have them play Rock-Paper-Scissors for who gets to call it, then flip a coin.  Heads! You win!

So, right now, you feel pretty good about yourself.  Hell! We are at least as good as Google!

But, you’re not.

That’s the problem with Talent Acquisition everywhere.  We don’t truly listen to what all the data is telling us.  We gather all of the information. We get assessment scores, we get personality and cognitive measures, we do in-depth background screening, we perform behavioral-based interviews, we check the candidates’ professional references, we stalk their Facebook page, we check informal references the candidate doesn’t know about, we do just about anything to get as much knowledge as we can on a candidate.

Then we screw it all up.

After all this gathering of data we do what Google does.  We make a decision based on a gut feel. We tell ourselves it’s not our gut!  We tell ourselves it’s based on the totality of data and information we’ve gathered, but we (you) are lying to yourself.  Your gut is lying to you.

Don’t blame yourself. You’re just stupid.  The science is way smarter than you, but you can’t even see it.  It’s like in the movie Searching for Bobby Fischer (the chess player) when Max reaches across to offer the kid a draw early on in the match. The kid declines. Max can see the win (you can’t see it, it’s nine moves away), but by the time you see it you’ve already lost.

We are the kid who can’t see it.  Hiring science is already smarter than we are, but we want to continue to believe we can do it better. Here’s a clue. If Google can only do better than 1% better than 50/50, you shouldn’t hold out hope you’ll be better than that.

Can you hire better than Google?  Yeah, but not if you’re going to keep making gut decisions.

FOT Note:  We here at FOT like to think we get talent and HR at a different level. At the very least, we are probably going to have a different take than the norm.  That’s why we recently onboarded HireVue as an annual sponsor at FOT, where they’ll sponsor posts like this one, allowing FOT contributors to write, without restriction, on all things related to predictive analytics and how it impacts talent acquisition. Most of us will never get the science behind all of this, and to help, HireVue is also signed up to sponsor a FOT video series aptly named “Weird Science.” Be on the lookout for Episode #2 of the Weird Science video series later this month. 

FOT Background Check

Tim Sackett
Tim Sackett SPHR, is the ultimate Mama’s Boy!  After 15+ years of successfully leading HR and Talent Acquisition departments for Fortune 500s and smaller technical firms, Tim took over running the contingent staffing firm HRU Technical Resources in Lansing, MI. Serving as the Executive Vice President, Tim runs the company his mother started over 30 years ago, and don’t tell Mom, but he thinks he does a better job at it than she did!  Check out his blog at Because he's got A LOT to say, and FOT just isn't enough for him.


  1. Kris Dunn
    Kris Dunn says:

    It’s true – they’re just like us.

    Except their employment brand and general situation (making early admins millionaires) means that those two candidates are a lot better than what the average company would have as finalists.

    Your candidates play rock/paper/etc. Their candidates developed an algorithm in their time together as finalist and created more wealth than the value of most small to medium size businesses.

    So they’re like us, but their not.

    Still better to be them. Feels good to think about them being average, though…


  2. Matt says:

    Ok.. first comment here.. so will try not to be too confrontational.

    However, primarily I call “bull shit” on this thesis…show me the data. I mean, what does it mean that they are 1% better than 50/50. Did 51% stay after five years, and 49% get fired / leave? There overall premise you talk about it being a “crap shoot” I can somewhat buy. But part of the reason it is a “follow your gut” world is after checking off basic criteria, there is NO algorithm for “good or not”.

    So I guess my challenge is what exact data are you defining that should be driving the process. 3-5 discreet measures (finance guy here so if its really data driven than you should be able to give me concrete hr examples that equate to $, ROI, EBITDA, inventory turns etc.). And then if you find them I would hazard to say that Goodhart’s law may snag you

  3. Matt says:

    I get the irony of “bull shit” not being too confrontational.. nonetheless… 🙂

  4. Companies where software isn’t important to the bottom line are less selective, can often do well enough sending jobs places where engineers earn 80% less, and aren’t regarded as being so great.

  5. Sean Kelley says:

    It’s hard to ignore your gut. Amiright? It’s the human factor. Working with widgets is easy, but predicting people can be tricky. I used this article in my comments to Mike Elgan’s write up on where he writes about predictive analytics in HR – using it to determine flight risk staff and ones that may jeopardize the org from a risk perspective. It’s good, but again, there’s a human element. Link to his article:

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