The quality of hire is widely regarded as the single most important (and elusive to measure) aspect of the recruitment process. However, with the growing capabilities of data analytics, HR professionals are getting a clearer picture of the factors that link information gathered during the hiring process to the performance of the company after the hire has been made. A recent analysis of workforce data has revealed some unexpected connections.

Quality of hire has long been recognized as an industry standard for measuring a company’s ability to attract and retain top talent. It’s the single most important indicator that human resources can use to boost output, retain talent, and accomplish strategic objectives. Still, it’s been a struggle to get a handle on.

Hiring Better Employees Using Data Analytics

Hiring quality employees has always been a challenge. As a rule, it has traditionally been decided by an individual using an Excel spreadsheet and basing their decision on subjective criteria such as performance and length of service. There was no standardized process or machine precision behind it, so each company had its own take on things.

It is challenging to put a monetary value on the intangible factors that contribute to a good hire. Most of the time, the lack of, or inadequacy of, a reliable measuring system is to blame.

This is essentially a data issue. Data sources in most businesses are disjointed and isolated, with the applicant tracking system (ATS) and other pre-hire platforms existing independently from the HRIS (human resources information system) and the performance management system.

Yet another difficulty is of a more human nature. How can a busy company evaluate potential employees? When it comes to making improvements, whose responsibility is it, if anyone at all? Butts in seats and time-to-fill have been the primary KPIs [key performance indicators] for talent acquisition for decades.

Determining Quality of Hire

Determining quality of hire with objective accuracy has proven to be much more challenging, if not impossible, than measuring time-to-fill or cost-per-hire without analytics technology.

An automated, real-time, and trustworthy feedback loop between recruiting and employee outcomes is essential for using data analytics to comprehend the merit of a given hiring decision. In the past, this has been determined through a lengthy and costly process of trial and error, and some people still don’t succeed. Recruiting and people management teams are often kept separate, it can be difficult to determine which criteria should be used to evaluate candidates, and there is often not enough time or resources to properly manage the data or draw any conclusions from it.

Using Machine Learning

Businesses use machine learning to make sense of the massive amounts of data they process. We create and analyze employee profiles that cover their entire life cycle, from their first interaction with the company as a candidate to their exit interview, by combining data from various sources, including human resource information systems (HRIS), applicant tracking systems (ATS), payroll and benefits platforms, assessment and interview tools, and outcome data from performance management systems, culture and engagement platforms, sales management tools, and others. Colleague and supervisor survey responses supplement the profiles.

In addition, technological advancements can help shift the focus from reactively reacting to insights to making proactive recommendations based on the data. Predictive analytics that operationalize quality of hire are most valuable when they lead to accurate forecasts and subsequent interventions that improve the hiring process and employee satisfaction.

The key to making a good hire for your company is to start with the end goal in mind. To wit: what contributes to the company’s ability to realize its vision down the road? Now that we know what factors contribute to success, we can use reverse engineering to figure out what kind of person would be most likely to achieve those results.

Recently published research based on 24 million hiring decisions challenges some long-held beliefs about the hiring process. Some of the findings in the report concerning internal referrals, interview procedures, and pre-employment tests may come as a surprise.

When Referrals Fail

It’s generally accepted that word-of-mouth among current employees is the best way to find qualified candidates. However, the data showed that the quality of hire for internal referrals was 26% below the mean, so this isn’t always the case.

Many things work together to cause this. The hiring process may be less rigorous for some teams when considering an internal referral compared to when considering an external candidate. Beyond the potential influence of referral bias, the referrer’s financial motivations should also be taken into account.

The data demonstrated that the quality of hire increases while the number of referrals decreases when financial incentives are removed, such as referral bonuses.

Quality of hire was also lower than average for candidates found through job boards, while it was higher than average for those found through recruiters.

As there are better analytical and numerical indicators that can determine the metric, employee referrals should be given a very low weight when measuring hire quality.

Lack of Training for Most Interviewers

The data shows that there is only a 10% correlation between interview scores and quality of hire, suggesting that interviewers’ ideas about who should be hired are not congruent with the company’s aims.

Although interviews are given significant consideration during the hiring process, they may not be the most accurate predictor of a candidate’s performance or fit once hired. Based on the findings, it appears that the vast majority of interviewers lack the necessary expertise and experience. Higher interview scores were more strongly correlated with higher post-hire performance evaluations among the small subset of interviewers who conducted 12 or more interviews annually.

Interview results might not be able to foretell performance once hired, but they can reveal trends. It’s possible for a job candidate to shine during an interview but fall short of expectations once hired. However, adjustments need to be made if this keeps happening frequently. For companies to improve their quality of hire, they must return to the basics and revamp their interview techniques. Interview questions can be tailored to a greater extent after identifying the specific knowledge, skill sets, or personality traits that current or former employees possess that lead to success in the role being filled.

Numerous Employment Tests Lack Predictive Value

The data demonstrated that many of the commonly used assessments in the hiring process are not good predictors of new hire quality, much like interviews. It was even found that certain factors had a negative relationship to employee quality. To evaluate the evaluations is of utmost importance. Many candidate assessments are not only not predictive, but also have the potential to negatively impact the candidate experience and lengthen the time it takes to hire. It’s wise to research assessments that will actually streamline the hiring process.

A number of tests used during the hiring process have been shown to be more indicative of future success. High correlations have been found between test scores and the quality of hire in roles where the tests are tailored to the job, such as in the case of technical assessments for engineers or mock sales pitches for account executives.