The joy and frustration of hiring new recruiters…
Hiring somebody with no experience and guiding him or her through the first days, weeks and months of a fledgling career can be the greatest joy of a recruitment manager – when we get it right and the person we’ve backed turns into a big league player, there’s little better. Seeing a newcomer gain skills and confidence, evolving in front of our eyes and becoming an assured, competent professional who is going to further the reputation of both our company and our industry as a whole – that’s a fantastic feeling.
However, statistically, we’re more likely to end up with a mid level player or, worse still, a money pit. Indeed, one of the greatest frustrations of any recruitment manager is the risk involved in investing in the wrong people.
The losses from a bad hire can mount up quickly. Six months of salary, six months of training, six months of operation costs. If there was an agency involved, that’s even more money that you can’t get back. Sure, the work you’ve got in return will be of some value but not an adequate return on your investment.
This is one of the reasons why many recruitment companies look to make quick decisions on newcomers in their first month or two. If there are enough red flags about either attitude or aptitude (or both), they’ll cut their losses, reclaim whatever they can from any agency involved, and look to invest their money in somebody else. The frustration with this is that it’s hard to get a genuine feel for the true upside of a newcomer in his first few weeks, so snap decisions can be made and people who might have worked out given support through a rocky start become wasted potential.
In short, hiring within recruitment is often a minefield of frustration and missed opportunities.
It’s little wonder that increasingly more managers are second guessing their own decisions at interview and looking to restructure their interview and on-boarding processes to ensure that those who join are more likely to be a good investment. The issue, however, is that the longer and more complex an interview process, the more likely it is that better candidates will be snapped up by other companies or, alternatively, lose interest in working for your company since having to jump through hoop after hoop at interview doesn’t exactly create a welcoming impression.
It’s a difficult balancing act – either move quickly on gut feeling and potentially get people who turn out to not be what you hoped, or be more deliberate and potentially lose some great people to your competition – the never-ending quandary of the recruitment hiring manager!
Something I’ve become more aware of recently is the application of science to the process.
An evolution within business since I sold Talisman (in 2008) has been Big Data and its application.
Whilst it’s an industry still in its infancy, the application of data analysis within recruitment intrigues me. I’m not talking about amateur psychology or the ham-fisted psychometric testing that many companies have hung their hat on for much of the last couple of decades, I’m talking about proper accrual of data within a company, then applying scientifically developed and proven algorithms to enable recruiting managers to have a better idea as to what they’re dealing with.
It’s the kind of thing I would have loved to have access to a decade ago. I suspect the cost of the service/product would have been more than offset by the money I would have saved in avoiding hiring people who turned out to be all mouth and no trousers (metaphorically, not literally). I might have also given others a chance instead of rebuffing them – and they might have become big hitters within my business.
Data science at work
Speaking with Steven John from PredictiveHire recently, I was fascinated to learn more about their business and the successes they’ve had already. I’m something of a sceptic by nature so found it hard to believe that something I’ve always thought would be fantastic in concept yet unrealistic has indeed become very real – and very helpful. Steve was quick to point out that it’s not a replacement for decision making, it’s an enhancement. By creating and harnessing data points within the existing workforce of the company, PredictiveHire creates an algorithm that is unique to a business and that can predict the likely performance of a candidate within that company. These are tailored to provide outputs based on what the business considers a good/likely measure of fit or performance. In short, it offers recruiting managers a pre-interview indication as to whether any candidate is a strong, moderate or weak statistical fit. The obvious bets may still be obvious (although we’ve all been disappointed by a superstar interviewee who didn’t translate into a superstar employee!) but this technology will help with all candidates, including those who fit into the grey areas where the hiring decision could go either way.
The most interesting example I found in their company communications was feedback they recently received from an early adopter recruitment client who was quoted as saying: “Our new hires who received high performance predictions from PredictiveHire are becoming productive twice as quickly against the average” – and that’s from the HR Director of a global recruitment firm. If you want to know more about any of this, give Steve a shout via his LinkedIn page, I’m sure he’ll be happy to oblige. If you’re a hiring manager in a company bigger than 50 employees, a chat with Steve is something I’d encourage. And no, I’m not on commission! It’s a genuinely interesting product and service that I think could genuinely affect businesses in a hugely positive manner, cutting down on time and money wasted in investing in the wrong people and passing up the better bets – something I would have benefited hugely from back in ‘my day’ as hiring manager of a recruitment business.
The bottom line
From where I’m sitting, it’s great news to see data science being worked more successfully into hiring processes. Don’t get me wrong, I’m definitely not an advocate of turning personnel decisions over to algorithms but the more knowledge we can be equipped with before making a decision can only lead to better decisions overall.