Many years ago, I had an interview with one of the UK’s leading fund management companies. At the end of it I was asked to provide a sample of my handwriting. Every candidate had to have their handwritten scrawl analysed by a ‘graphologist’ in Switzerland.
Quite what insights into my stock picking ability he was supposed to spot, I’m not sure. In any case, he mustn’t have liked it, because I wasn’t offered the job.
Later on in my career I talked to another firm who were very keen on me. At the last stage of the process I met a man who chatted to me about this and that for 20 minutes.
I had been carefully considering my responses, but at the end of the discussion he revealed that he had paid no attention to what I had said. Rather, he had been watching and listening to the way I said it. This, apparently, provided insights into my character which would be reported back to my prospective employer.
Both these companies had been founded by strong individuals who firmly believed in those strange techniques. They were an attempt to introduce some objectivity and ‘science’ into the process of deciding who to hire. At least they were more colourful than those psychometric tests we have all done at some point.
I might scoff at them, but I wonder whether my own skills as an interviewer produced results that were any better. Like most people, I’m heavily influenced by my own experiences, so I tend to like people who share a similar background and outlook.
Try as I might, the whole recruitment process can feel a bit casual. And it is also tempting to rely on well-established signals of suitability. Your boss won’t criticise you for hiring someone with a good degree and a bit of relevant experience, even if they turn out to be useless.
Interview or spreadsheet?
Now that the information and the computing power is becoming available, human resources teams are starting to get more scientific. They’re analysing candidates and workers using ‘big data’ metrics.
When we think about the link between data and recruitment, we might think of people foolishly wrecking their online reputations with embarrassing Facebook photos. However, a more systematic and constructive approach is starting to be developed, which takes advantage of big data opportunities in this area.
One important thing to remember about big data is that it’s about correlation. In other words, we are not looking for a specific case where A caused B. What we are looking for are lots and lots of examples where A seems, more often than not, to be associated with B.
If the sample sizes are big enough, then we might be able to draw some useful conclusions. Teasing out correlation from new data sets is what big data is all about. It tells us things about the world we didn’t know).
US company Evolv is developing a platform that helps companies identify the best recruits from large volumes of candidates quickly and accurately. Using big data often provides an insight that runs counter to conventional wisdom.
For example, a case study of work carried out for Xerox’s call centre and customer services function showed that previous experience in a similar role was irrelevant in predicting the effectiveness of an employee.
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Yet, unsurprisingly, Xerox had previously sought out workers with a call centre background. By casting the net wider, Xerox were able to get access to a broader range of potential employees and achieve better value.
In deciding how to employ people who would stay with the company, a better thing to focus on than experience is proximity to the office. People with short commutes tend to stay at a firm 20% longer than those who travel.
This is not so surprising. But an even stronger element in retaining staff is the proximity of the office to local amenities. When employees can carry out errands and tasks by walking from their place of work, average tenure goes up by 58%.
If a company doesn’t retain staff, the resources invested in them are wasted. It also costs money to recruit their replacements. As well as location, Xerox found two other predictors of staff retention. People who participated in one or more social networks – but fewer than four – tended to stay put.
Creative personalities also lasted longer. We could guess at the reasons. Sociable people are probably better in customer-facing roles, but those who spend all their time online might not be well-adjusted. Creative people might have more stamina and patience to last through the training programme than other personality types.
Trying to see why these relationships exist, however, is not the point. What matters is that they do exist. And that they can be used to help recruiters focus on what matters rather than what they think matters.
Once people are in jobs, big data is being used to analyse what makes some people more productive than others. A study at MIT has given workers badges to record their interactions with their peers. It is possible to score how people talk, listen and interrupt each other. We can see how many personal contacts an individual has and judge how effective these are.
The data can provide clues as to how productive an employee is, whether they are having the right amount of contact with their colleagues and people from other teams. From this it should be possible to identify the common style of natural leaders and what makes for an effective worker.
All this can sound a bit like big brother. And I admit to feeling uneasy at a computer crunching my data and deciding whether I am right for interview or promotion.
However, intelligent use of data now available is surely preferable to the often random decision making we have all experienced at one time or another. As with a Yelp! or Trip Advisor score – imperfect information is far better than no information at all. Prepare to be judged!
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