How to predict your next hire Viewpoint careers advice blog

How to predict your next hire What’s the secret sauce? Last month I had the pleasure of attending the annual HR Tech Conference held in Las Vegas, and aside from being blown away by the sheer size and scale of the event, what struck me most was the buzz around the growth of predictive analytics. In a piece titled “Predictive Analytics Dominates My First HR tech Conference” Bill Kutik writes “Batten down the hatches for the greatest tsunami of hype we’ve seen since ‘social recruiting’….This time, it will be about predictive analytics, especially for making hiring decisions and identifying employees ready to quit” Since the Vegas conference, the momentum has continued to build, with a number of new announcements from Workday’s focus on their new Insight Applications at the recent Workday Rising event, to Gild’s launch of their ‘Intelligent Hiring’ platform, to LinkedIn’s pilot of a search program “aimed at detecting patterns within a person’s profile that would predict whether they were likely to be a good long-term hire” What does Predictive Analytics really mean? Wikipedia says “Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events”. In the world of recruitment, this means using data about past recruitment activity and outcomes to support better decision-making in the future, as employers search for the guaranteed high performing hire. Or, to quote Gild CEO Sheeroy Desai “…helping companies to hire the right people, faster…” As a closet geek, I am attracted to the idea of using data and algorithms to take some of the subjectivity out of recruiting. However, it will be interesting to see how the use of these sorts of tools will play out, and what they might mean to the recruiting function going forwards. With many different solutions trying to tackle the same issues from different angles then it will be fascinating to see who can claim the high ground in terms of establishing credibility and validity. For example, if we think about trying to predict ‘cultural fit’, then already there are different solutions emerging based on textual analysis of candidate CVs, questionnaire-based personality profiling, gamified behavioural profiling or analysis of social media behaviour. And that is assuming we are clear on what we mean by ‘cultural fit’ in the first place â€" is it alignment to overarching corporate goals and values, or more to do with the ability to fit in with a particular team or hiring manager ? Either way, trying to understand the most appropriate solution for any given scenario looks challenging just now. How could we actually use it in the hiring process? On the one hand we can see how the tools may be used to widen the funnel of potential candidates, by removing bias and prejudice, or by challenging the long-held ‘truisms’ that exist in some organisations. As an example, in their work within Xerox’ call centre recruitment function, Cornerstone Selection (formerly Evolv) used data to prove that previous call centre experience had almost no impact on job performance and tenure, thereby allowing Xerox to “consider a broader group of potential employees while reducing the higher payroll costs of experienced workers”. Alternatively there is the prospect that predictive solutions can be used as providing yet more reasons why any particular candidate should not considered for one role or another. Follow this thinking to the extreme, and do we end up with an increasingly ‘homogenised’ function, with more and more recruiters trying to court the select few candidates that the machines deem as ‘desirable’? And where does that leave those ‘outlier’ candidates who may not quite fit the stated mould, but have the potential to make a real difference to an organisation? Fad or foundation? The use of predictive analytics in recruitment is still a relatively nascent field, but it seems it is here to stay. No doubt, the available solutions will continue to evolve and strengthen in terms of their predictive power, yet in order to make the most of these tools, it seems there will still be a need for experts who can interpret and apply their outputs in the most meaningful way. Finishing where we began, in Vegas, in his keynote talk “Making the Right Choices in the Second Machine Age” MIT professor, Andrew McAfee, argued that as technology advances so “…we need to re-examine the boundaries between technology and humans”, and that by opening up to a more data-driven approach then “…if we can find new ways to combine human and digital intelligence, then the sky’s the limit” Join our LinkedIn Group Join our LinkedIn Group to share your thoughts and stay up-to-date with the latest on business, employment and recruitment news in the IT industry. Join our Group