A hiring manager typically selects a candidate based on the information gathered through his/her resume and the interaction during interviews. Given the subjective nature of these methods, the interviewer typically does not gather sufficient information for an intelligent match leading to a rather costly error in judgment.Coupled with the growing number of applicants for each role and the myriad of skills required,
In a typical recruitment scenario, the absence of data driven insights generally leads to two types of errors -
1. Type 1 error: The organization ends up hiring a candidate who will eventually not perform well.
2. Type 2 error: The organization rejects a candidate who could have been a good performer.
Whether you’re hiring poor talent or missing out on potential star performers, the hiring misses in both cases have an adverse impact on an organization’s productivity, coupled with additional costs of training or replacement.
While it might be stretching the argument too far, I can’t help but sight the example of Brian Acton, founder of Whatsapp, who has proved to be probably one of the most costly hiring errors ever.
He was rejected a job at Facebook only to be later bought on board with the $19 billion acquisition! However, not all recruiting errors may be as expensive.
So how can companies reduce Type 1 and Type 2 errors?
Hiring involves assessing many aspects all of which cannot be accurately measured by a human interviewer.
To gauge a candidate’s potential objectively, he/she needs to be assessed for not just functional skills but also aptitude, behavioral and cognitive skills.
While some of these can be measured effectively through artificial intelligence and machine learning based assessment tools, there are certain aspects wherein a human interviewer can assess better. Hence, the idea is to build hiring practices that can combine a data driven approach to the power of human evaluation.
The scientific assessment tools available today are able to objectively evaluate a candidate’s potential to help companies identify high performers who would have been missed during a traditional
Let us take an example of recruitment for sales personnel to shed more light on the complexities of the hiring process without adequate data. Hiring a sales professional requires an effective evaluation of soft skills but the intrinsic competencies of sales people cannot be judged simply by a resume or just any general aptitude test available in the market.
Also, with the process of hiring a sales force being fairly unstandardized, companies often struggle to understand what is required to hire the ‘right’ salesperson. The result! 1 out of 3 sales personnel in most organizations fail and only 1 out of 3 actually perform up to potential. A selection error in this case is a loss of both cost and opportunity for the organization.
Situational Judgment Tests or SJTs which measure a candidate on various soft skills and practical intelligence can bring forth objective insights in hiring sales professionals. For example, Aspiring Minds’ Sales SJT is designed to test candidates on real life scenarios and how they would react under varying circumstances.
Instead of relying purely on a candidate’s knowledge, SJTs helps measure skills/competencies that are crucial for job success with situations which measure a combination of cognitive, behavior and domain understanding. This makes recruitment for salespersons less susceptible to hiring errors as discussed above.
Hiring will continue to be complex but the right combination of data science and human evaluation can go a long way in identifying the right talent. We see the need for recruitment mechanisms to evolve towards leveraging the power of data and science for organizations to build a productive workforce – simply put to avoid a bad hire and reduce the chances of missing a potential high performer who might get recruited by your competitor!
(Himanshu Aggarwal is co-founder and CEO of Aspiring Minds)
(Image: Thinkstock)