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The Cost of a Bad Hire

Taking a Data-Driven Approach to Eliminating Bad Hiring Decisions

  • A bad hiring decision can cost two to three times the position’s salary and up to a year of lost productivity according to recent interviews with Chief Human Resource Officers in North American technology companies.
  • 81 percent of HR professionals, recruiters and hiring managers admit to having made mistakes leading to bad hiring decisions according to Robert Half.
  • 49 percent of HR professionals and recruiters believe hiring managers underestimate the complexity of making a good hiring decision and often rush the hiring process to get someone onboard quickly, based on a Robert Half Survey.

The candidate looked perfect in every way. From the résumé that matched what you were looking for, to impeccable interviewing and social skills, all validated by great references. Less than 90 days into the role, they‘re struggling. Long, introspective email chains develop between HR recruiters and the hiring manager, wasting valuable time and further adding to the cost of making a bad hiring decision. Steps are taken to define a performance plan while HR attempts to find a replacement, and the cycle begins again.

The Lack of Quality Data Drives Bad Hiring Decisions

HR recruiters and hiring managers, blinded by the urgency to fill a new position and their own biases, rush decisions on new hires, making mistakes on who gets hired for high-demand positions. In essence, every recruiter and hiring manager has a unique decision-making process or personal algorithm they use for making hiring decisions. Instead of relying on all available data about each candidate and pattern matching their skills to the needs of the open jobs, HR recruiters and hiring managers rely entirely on personal experiences, conscious and unconscious biases, and approaches that worked in the past. Relying on approaches and frameworks that worked in the past intuitively makes sense yet increases the probability of making a bad hiring decision as no new data gets considered. Even in the organizations that excel at recruiting, only one in three hires is a good one.

Let’s look at the example of a company needing to staff their engineering and marketing teams. Requisitions are created and signed off, the jobs are advertised and posted on LinkedIn, and résumés begin pouring in. Internal company referral programs are updated with the new positions, and a few referral résumés come in. Engineering and marketing candidates have carefully crafted their résumés to include every keyword in the job description using SEO techniques in the hopes the Applicant Tracking System (ATS) will add them to the queue. Marketing candidates provide portfolios on their websites showing the projects they’ve completed, and many have videos of keynotes given. Recruiters and hiring managers review the ATS queue, select candidates for screening using their judgment which includes conscious and unconscious biases, and schedule interviews. Skype interviews are scheduled, and the process begins with intensity and urgency, as both recruiter and hiring managers know it’s a cutthroat market for good engineers with JavaScript, R, Python, and machine learning skills. Marketing candidates are most often screened based on who they worked for before, where they attended college, with the previous employer’s reputations being weighted as much or more than accomplishments based on recruiters’ and hiring managers’ biases. The urgency of wanting to find a candidate fast reduces and sometimes eliminates time available to consider additional data that could lead to hiring the best candidate. With no data to provide contextual intelligence on candidates, bad hiring decisions keep happening, and the cycle continues.

Breaking the Cycle of Bad Hiring Decisions With Machine Learning

Talent management and recruiting are in need of a makeover. Consider the fact that as far back as 1482, over 530 years ago, one of the greatest geniuses the world has ever known, Leonardo da Vinci, relied on a handwritten résumé to get new work. His résumé lists his ability to build bridges and support warfare, not reflecting the genius who provided the world with a myriad of scientific discoveries and inventions that modernized the world. Nowhere does his résumé reflect his artistic genius that would lead to the Mona Lisa, Last Supper, Vitruvian Man and many other priceless works of art being produced. Imagine not hiring Leonardo da Vinci because his résumé didn’t reflect the artistic dimensions of his skills, realizing after the fact he could have revolutionized an entire arts business. It’s the same with every company today who relies just on résumés alone. Many are not finding the right candidates because they are relying on an over 500-year-old process that hides a complete, unbiased picture of the candidate. Getting beyond résumés, gaining contextual intelligence of candidates and quickly seeing which skill sets are strengthening over time is essential for reducing the probability of making bad hiring decisions.

The quickest path to reducing and eventually eliminating bad hiring decisions is to use machine learning to seek out candidates who most closely resemble high performer’s profiles. Using comparative analysis that goes far beyond the constraints of résumés, machine learning algorithms can in seconds find a pipeline of potential candidates that are most comparable to the digital personas of the highest achieving profiles for each position. Taking this data-driven approach to hiring also removes the potential for personal biases, both conscious and unconscious, from the decision making process. The more data-driven the hiring process, the greater the diversity every company will be able to achieve. Evaluating candidates based on their ability to excel in the role given their proven skills and strength levels the playing field for everyone to compete for the most desirable jobs any company has.

By integrating publicly available data, internal data repositories, Human Capital Resource Management (HRM) systems, and ATS tools, eightfold.aihas created a single Talent Intelligence Platform (TIP)™ shown below. Machine learning algorithms parse all public and enterprise data on candidates, looking for the optimal match of career growth, recent publications, and professional overlap with other colleagues to validate achievements mentioned on their résumés, searching for candidates who are the best fit and have the greatest potential. Hiring decisions become data-driven, alleviating the potential for conscious and unconscious biases to influence which candidates are selected and interviewed. Fine-tuning and personalizing hiring decisions at scale become possible for the first time, and the risks of making bad hiring decisions are reduced.

diagram2 - The Cost of a Bad Hire

Conclusion

Bad hiring decisions begin a domino effect in any company, dragging down company-wide performance and morale. Reducing the risks of making bad hiring decisions and follow-on costs needs to start with a unified data strategy that provides actionable insights and contextual intelligence to guide hiring decisions. Résumés are an anachronism. Their limited view of candidates’ abilities and unique skills is illustrated by comparing Leonardo da Vinci’s résumé with the detailed insights possible using the Talent Intelligence Platform™. Reducing employee churn, eliminating the wasted months to recover from a bad hiring decision, and the tendency to keep putting good money after bad in a difficult employee situation costs companies millions of hours a year in lost productivity. Getting new hires onboard fast who will excel and help drive growth matters most. It’s time to move beyond the limitations of an outdated process and embrace a more data-driven, accurate and diversity-enabling approach to making hiring decisions.

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