God cannot help you find the best hire, but Machine can

Zappos CEO, Tony Hsieh once estimated that his own bad hires have cost the company well over $100 million. Just goes to show even the successful aren’t immune!!

Talking of bad hires, here’s another statistics for you. According to Harvard Business Review, as much as 80% of employee turnover is due to bad hiring decisions. That’s quite an impressionable number, isn’t it? So what’s the catch? Why there are so many bad hires in the first place? That’s something any logical mind would ask.

Let me tell you. It’s been seen that midsize and large companies usually relegate the most critical task of screening candidates to contractors, interns or freelance part-time employees. Companies don’t realize this, but this leads to huge hiring errors. How? Read on..

It’s not humanly possible for a screener to review a large number of CVs in a short period of time keeping in mind all the relevant variables, without making errors. And these are the very errors that can cost organizations hugely. Do not forget an average cost of a bad hiring decision can equal 30% of the individual’s  first year earnings.

My God! Did you just say? No, do not invoke the God, He won’t be of much help here! However, one thing that can save you and your company from huge losses arising out of bad hiring decisions is the application of talent analytics. Analytics technology can process huge volume of data including all the variables, consistently and without getting tired. The result? You get a hiring process that is accurate and gives you the list of best candidates.

While the Analytics Technology would score against variety of variables, how does the technology understand what variables are relevant for you? Here’s when ‘Machine Learning’ comes in. Few know what Machine Learning really does. ML as its usually called, works in the background keeping track of what the Recruiter is viewing in the CV, based on the shortlist and rejects, it constantly creates a database of what the Recruiter prefers for a given role. When the ML runs through large amounts of such instances (also called ‘Big Data’), it auto builds a prediction algorithm on its own and constantly correcting the recommendation algorithm to deliver right predictions. A common example of the above is Google. Whenever you Google something, it almost understand what you are really searching for and delivers results almost 99% accurately all the time.

Recruitment Technologies are maturing almost to the level of Google today.

So what God can’t do, machine CAN! Reckrut.com, has built ML technologies that predict what’s the right hire for you and your company, eventually reducing the potential bad hires. To know more, visit Reckrut.com today.