Taking Recruiting to the Big Data Level

In the end, recruiters are sales people.  They find prospects (potential candidates), qualify them, and hopefully get the best into the hiring pipeline.  Before job posting websites, to find candidates recruiters had to have great personal networks.  There is still no substitute for that for specialty jobs, but access to candidates has never been easier than it is today.

Of course, when candidates come to you they are going to be of varying quality, which gives recruiters the big task of sifting through online resumes, etc.  What if there was a way to set your skill based qualifications and then search for them online to identify candidates?  Gild claims to do that for programmers.  They have an algorithm for searching the web for programmers’ code, reputation among other programmers, and other job related (merit, as they put it) information.

I am intrigued and interested by this approach.  In essence, football teams just got done doing this for their draft.  Teams looked at every piece of game film available on every college player, organized workouts for those who showed the most promise, then held private workouts and interviews for the best of the best.  Of course, the universe of eligible college football players is much smaller than that of computer programmers, and someone still had to look through the film.  Guild suggests a process that is completely done by machine.

One criticism I can see is people questioning whether or not this kind of process captures the personality issues which can be important to success.  I don’t think anyone is proposing that such a process be the only part of recruiting and hiring.  However, if you could scour the internet for all of a person’s writing, blog posts, etc, I think you can get a pretty good idea about their personality.  You’d better believe someone is going to figure out a way to get a machine to score those things.

My concerns are around how what Gild looks for would be validated.  Is the algorithm built based on what should, or what has been shown, to predict performance?  Clearly, finding someone who wrote good code on a similar project should be a good predictor (a work sample of sorts).  But, does a programmer’s reputation among other programmers really matter?  Big data approaches are very good descriptors of what’s out there.  In this application, it needs to be a good predictor as well.

Any approach that brings data to the recruiting process is a good one.  If I hear another recruiter say, “I have a good feeling about this person” I’ll be ill.  If we can demonstrate the relationship between tests and interviews and performance, we should be able to do the same for the background and resume screening tools that recruiters use.  The only problem is that the history of research on these past experiences does not provide one a lot of optimism.  Perhaps we haven’t been looking at the right experiences, or enough of them.  Or, maybe it’s only now that the digital footprints of our candidates provide enough data for meaningful analyses.

Regardless, the amount of data publicly available on all of should be a treasure trove for recruiters.  While the information that big data will bring will have valuable insights about candidates, it is still incumbent on HR to ensure that this information helps us make more accurate and informed choices about candidates.  Just because some data seems like it is valid doesn’t mean that it is.

For more information on skills assessment and talent management, please contact Warren at 310 670-4175 or [email protected]

What’s New in Industrial and Organizational Psychology?

I had a chance to present on innovations in assessment at the annual industrial and organizational psychology conference (SIOP) in Houston a couple of weeks ago.  The panel was fun and engaging, I had a good time and learned a lot during those 90 minutes.  The rest of the conference left me with some concerns and hope.  It felt a bit more like an industry trade show than a conference.  It also left me with two main concerns about the field:

1)  We are way too dependent on statistical techniques.  Don’t get me wrong, correctly interpreting data is how every scientific and business field moves forward.  However, I was listening to a newly minted Ph.D. present on manager assessments.  His statistical analysis completely clouded the results (even a young tenured professor in the audience couldn’t figure it out) and it was completely divorced from reality.  There was no way to get from what he was explaining to something meaningful in the use of the assessment.  That might get the presenter a job teaching others about convoluted statistics, but nowhere else.  And it just wasn’t this presenter.  I saw several presentations and papers where the statistical techniques took precedent over the ideas (sort of like in our peer-reviewed journals).  The value of using these analyses was lost on me.  I’m guessing that every new statistical breakthrough was met with similar skepticism, and maybe these techniques will eventually be the norm, but I doubt it.  Let’s teach graduate students how to think and come up with creative new ideas rather than twisting numbers.

2)  Speaking of new ideas, this was not the place to find them.  Lots of papers and presentations on slicing what we know into smaller and smaller pieces, but nothing anywhere close to a breakthrough.  This was a theme that came up in my conversations with others at the conference.  Some companies presented on “big data,” which translated to their plans to link their recruiting, assessment, talent management, performance, and salary databases.  If they had some interesting findings they weren’t sharing them (yet).  Most disconcerting is the lack of progress on defining successful leadership outcomes.  In most ways, we’re still in the, “I know it when I see it” phase of understanding leadership, which makes it seem very complex.  On the other hand, consistent studying of it always comes back to basically the same conclusions (see this article on Google’s study of effective leadership, brought to my attention by Dennis Adsit [@DennisAtKombea]), which makes leadership seem simple.  We can quibble about the difference between good management and good leadership, but what’s obvious is that there is nothing new under the sun regarding the behaviors required to make these things happen.  This is reassuring from an assessment point of view, but a bit troubling when thinking about the field as a whole.

Having said that, there is great interest in the field all over the world and the study of people at work is growing and keeping pace with changes in technology, connectivity, and how we live our lives.  The gap between science and practice in many areas is closing, to the benefit of both.

For those in the field, I’m sure you had different experiences and I would love to hear about them.  For the users of testing/assessment services, you can rest assured that the underpinnings of evaluating talent are still solid.

For more information on leadership and talent management, please contact Warren at 310 670-4175 or [email protected]

Does Paying More Get You Better Talent?

As politicians talk about raising the minimum wage, free agent football players are getting new contracts, and there being some focus on retailers who pay a significantly higher wage than their competitors, it’s as good of a time as any to talk about the relationship between pay and performance.

At the high end, companies clearly think that there is a relationship between pay and performance.  The players with the best statistics (regardless of sport) generally get the biggest contracts.  When asked why their executives get paid so much, companies will frequently respond that they need to pay to attract and retain top talent.  Oh, and having friends on the board of directors’ compensation committee doesn’t hurt.  The thinking here is that people who make a lot of money will switch companies/teams for even more money, so paying more will retain top talent.  Unfortunately for these companies, higher CEO pay tends to lead to lower company performance.

As it turns out, in sports management really ends up paying for past performance rather than future performance.  That’s OK if you are a team resigning a player.  Not so good if you’re a team that signed a player away from another team.

For entry level and low end positions, research on the relationship between pay and performance is hard to come by.  It mostly focuses on larger macro-economic effects rather than at the individual level.  Companies will generally argue that supply and demand keeps wages down and that the investment (e.g. paying more) in low wage workers isn’t a good one because of high turnover rates.  This makes sense because finding a $.50/hr difference in pay for someone making $10/hr or less is significant and would likely lead to them leaving.  Also, companies that hire a lot of low wage employees (think retail, call centers, and hospitality) have an incentive to keep labor costs low in order to maintain profitability.

My feeling is that companies should consider their low wage talent in a different light and pay them accordingly.  I was working on a project for a retail client and was involved in a discussion about their delivery drivers.  The meeting was about whether they should contract that work out to save money or keep it in house.  The point I made to them was that the customer’s last impression of them is based on the quality of service they receive from the delivery driver.  Why wouldn’t they want to have more control over that interaction?

To take this the next step, your customers’ experience with you is not going to be based on how well a division president does her job.  Rather, it will be based on the interaction they have with your low wage employees.  If they are so critical to your business, why wouldn’t you want to compete to attract, select and retain the best workers available in that pool the same way you would for managers and executives?

For more information on talent management, please contact Warren at 310 670-4175 or [email protected]

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