New research has discovered a way of telling which CV’s are most likely to be picked out from a large pile of job applications by recruiters.
After analysing a staggering 441,769 CV’s, Colin Lee from Rotterdam School of Management, Erasmus University (RSM) has written an algorithm that uses Big Data to predict who will be invited for an interview ‒ with an accuracy of between 70 and 80 per cent.
All of the 441,769 applications had been judged by real company recruiters and, in instances where there was no cover letter available with the CV therefore viewed in isolation, Lee’s algorithm was accurate 81% of the time. Where a cover letter was available, the accuracy was 69%.
Lee used software that automatically scans digital CVs for a wide range of attributes, including experience, age, distance from the workplace and education. Contextual factors were also taken into consideration, such as ‘did the candidate apply in time’ and ‘was the candidate already employed by the company?’
He then designed a very detailed model of the job market that described every occupation in terms of the most common work activities performed in that occupation, before matching up the characteristics of the applicants receiving interview invitations to the occupations the applicants applied for.
Lee states, “The results show that recruiters are, as expected, concerned with how many years of work experience the candidate has. Unexpectedly, it was shown that recruiters care very little about whether or not the applicant’s skills and education are closely related to the job in question.
He adds, “This model will be useful for screening large numbers of CVs. It can help recruiters in companies that have to sift through thousands of applications distinguish between applicants that should be invited for an interview and those borderline applicants that need more careful consideration.”
Lee says his model can also predict which candidates are suitable for newly created occupations and that this use of big data to model the job market will become even more valuable once former applicants’ job performance is added to the database. “This would make it possible to predict a candidate’s future performance simply by scanning their uploaded CV”, Lee concludes.
Jeremy Tipper, Consulting and Innovation Director at global talent acquisition and management firm, Alexander Mann Solutions, says that these types of predictive analytics are the future of the industry.
“Predictive analytics – including these types of algorithms – are the future of talent acquisition and shouldn’t be feared by hiring managers or recruiters. As it stands there’s too much wastage in recruitment that is both damaging to the candidate experience and potentially costly for businesses when it comes to identifying the right person for the role at hand. Indeed, we found in our 2016 Global Recruiting Survey that a staggering three quarters of those considered for a job do not even meet basic role requirements and, on average, 282 candidates are being considered for every role. This is a huge number of applications to sift through and with such a vast number to analyse, human error and unconscious bias are likely to come into play – which could cause the perfect candidate to slip through the net. By using predictive analytics, the hiring process can be streamlined to the benefit of all involved.”