This seems obvious but yet even today this is the key data source used in screening and hiring. For grad recruitment, your degree, your university and your uni results are key filters used in screening.
It’s already been four years since Ernst & Young removed university degree classification as an entry criteria as there is ‘no evidence’ it equals success. Students are savvy and they know how competitive it is to secure a top graduate job. In the UK, the Higher Education Degree Datacheck (Hedd) surveys students and graduates about degree fraud. The annual results are pretty consistent – about a third of people embellish or exaggerate their academic qualifications when applying for jobs. Read more here >
We analysed ~13,000 CVs, received over a 5 year period, all for similar roles for a large sales-led organisation. From this data set, 2660 were hired and around 9600 rejected. We wanted to test how meaningful the CV is as a data source for hiring decisions.
Can you pick which group was hired?
Look at these two word clouds. One represents the words extracted from the CVs of those who were and the other from those who were rejected. Which would you pick?
A word cloud depicts the relative frequency of words appearing in the set of resumes by the size of the words in the word cloud, i.e. words in larger font size appears more than the ones in smaller font size. Given that the two word clouds show no significant differences in the words in larger or smaller font sizes means that the two groups are indistinguishable based on the words used within CV’s.
The Bottom Line? The CV is not a reliable data source to guide hiring decisions.
P.S If you had picked Group 2 you would have been right.
Josh Bersin, the premier topic expert in our space, articulates how hard it is to predict performance through traditional testing in this way .
“Managers and HR professionals use billions of dollars of assessment, tests, simulations, and games to hire people – yet many tell me they still get 30-40% of their candidates wrong.”
And now the definitive publication for all things HR, leadership etc. the Harvard Business Review, has shared research that prior experience is also a poor predictor of performance. Read more >
So what signals DO tell you something about whether a person is a good fit for your role, or for your organisation?
Whether their background is similar to yours or the person in your team who is a star? Whether they have played a competitive sport at a senior level (because that’s a good indicator of drive and resilience)? Or maybe whether they are a different ethnicity, gender, educational background to the rest of your team because, you know … diversity is meant to be good for business!
The list of performance ‘signals’ are as many as the number of people (interviewers) you have interviewing new hires. It’s a deeply personal decision like who you choose as a partner and we all feel like we know what to look for. But we don’t.
And no amount of interview or bias training or even interview experience is ever going to make us better at these decisions.
But experience does matter, but it’s a different type of experience. It’s the experience that comes from doing something 10 x, 10,000 times, a million times, with feedback on what worked, what didn’t, under what context etc. And of course, if one could remember all that.
Think of a different context- the grading of an exam. If you ask your teenager or university aged son/daughter what would make them trust an exam result, they would likely say
3. Data driven, i.e some kind of formula for assessment, that assures consistency and fairness.
4. The experience of the assessor.
The fact is … just as no human driver will ever match the learning capability and velocity of a self driving Tesla car, no assessor will ever be as good as a machine that’s done it a million times. The same applies for AI in recruitment.
No human recruiter will ever match the power, smarts and anonymity presented by a machine learning assessment algorithm.
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