Hiring managers in search of qualified job candidates who can scale with and contribute to their growing businesses are facing a crisis today. They’re not finding the right or in many cases, any candidates at all using resumes alone, Applicant Tracking Systems (ATS) or online job recruitment sites designed for employers’ convenience first and candidates last. These outmoded approaches to recruiting aren’t designed to find those candidates with the strongest capabilities. Add to this dynamic the fact that machine learning is making resumes obsolete by enabling employers to find candidates with precisely the right balance of capabilities needed and its unbiased data-driven approach selecting candidates works. Resumes, job recruitment sites and ATS platforms force hiring managers to bet on the probability they make a great hire instead of being completely certain they are by basing their decisions on solid data.
Many hiring managers and HR recruiters are playing the probability hiring game. It’s betting that the new hire chosen using imprecise methods will work out. And like any bet, it gets expensive quickly when a wrong choice is made. There’s a 30% chance the new hire will make it through one year, and if they don’t, it will cost at least 1.5 times their salary to replace them. When the median salary for a cloud computing professional is $146,350, and it takes the best case 46 days to find them, the cost and time loss of losing just one recruited cloud computing professional can derail a project for months. It will cost at least $219,000 or more to replace just that one engineer. The average size of an engineering team is ten people so only three will remain in 12 months. These are the high costs of playing the probability hiring game, fueled by unconscious and conscious biases and systems that game recruiters into believing they are making progress when they’re automating mediocre or worse decisions. Hiring managers will have better luck betting in Las Vegas or playing Powerball than hiring the best possible candidate if they rely on systems that only deliver a marginal probability of success at best.
Betting on solid data and personalization at scale, on the other hand, delivers real results. Real data slices through the probabilities and is the best equalizer there is at eradicating conscious and unconscious biases from hiring decisions. Hiring managers, HR recruiters, directors and Chief Human Resource Officers (CHROs) vow they are strong believers in diversity. Many are abandoning the probability hiring game for AI- and machine learning-based approaches to talent management that strip away any extraneous data that could lead to bias-driven hiring decisions. Now candidates get evaluated on their capabilities and innate strengths and how strong a match they are to ideal candidates for specific roles.
A Data-Driven Approach To Finding Employees Who Can Scale
Personalization at scale is more than just a recruiting strategy; it’s a talent management strategy intended to flex across the longevity of every employees’ tenure. Attaining personalization at scale is essential if any growing business is going to succeed in attracting, acquiring and growing talent that can support their growth goals and strategies. Eightfold’s approach makes it possible to scale personalized responses to specific candidates in a company’s candidate community while defining the ideal candidate for each open position. Personalization at scale has succeeded in helping companies find the right person to the right role at the right time and, for the first time, personalize every phase of recruitment, retention and talent management at scale.
Eightfold is pioneering the use of a self-updating corporate candidate database. Profiles in the system are now continually updated using external data gathering, without applicants reapplying or submitting updated profiles.
