Hiring a new candidate efficiently in a quick and cost-effective way is important, but in the long term, the priority for measuring effectiveness in hiring will be the value the new employee brings to the company-the quality of hire (QoH). This value can appear at first glance to be a combination of intangibles, such as how a candidate helps improve the company's work, exceeds expectations, or improves team performance-basically things that are often seen in hindsight or over the long run as contributing factors to the company's success. However, there is much that your talent acquisition team can to do predict QoH for your company, especially with the increasing prevalence of AI software.
How to calculate the QoH metric
Overall quality of hire defines factors important to the company and considers turnover rate using a simple formula. You can define particulars based on your company's goals and create standard assessments of each factor. Let's consider three specific factors important for any company:
- Factor 1: New hires' productivity levels (which may include revenue or sales or meeting project goals within certain timeframes)
- Factor 2: Surveys both for new hires and hiring managers assessing performance (creating data using a scoring metric)
- Factor 3: Retention rates and turnover
Each factor can be calculated as a percentage, added, then averaged:
QoH Index = (Factor 1 + Factor 2 + Factor 3)/ 3
Data collection in the form of surveys from hiring managers, work performance metrics, and retention rates (including reasons for leaving based on exit surveys) are important in creating a comprehensive quality of hire metric, but that number alone for a single hire tells you little. Comparing numbers year to year helps a company assess the true impact and understand the factors that increase QoH over time.
But this doesn't mean your team should break out a notepad and calculator. In the twenty-first century, it is more important to create a data collection stream. The data can be read and analyzed with AI recruiting technology to create HR analytics, predict employee turnover, and even monitor seemingly unquantifiable factors like employee perception of the workplace or job fulfillment. This AI doesn't just work by magic; it needs a lot of smart data in order to truly work for a company. HR recruiting teams must be able to evaluate and build the data they want analyzed by the system.
Can AI predict the future?
Wouldn't it be nice if acquisition teams could predict QoH before hiring a candidate? One of the key ways to utilize AI technology for talent acquisition is to create a pre-hire QoH metric that collects and builds data about potential candidates that can gather information and analyze patterns, including reading into the typical responses of quality hires.
One proponent of this pre-hire QoH practice is Lou Adler, a best-selling author and talent acquisition training expert, who developed a method of scoring candidates to predict their QoH based on a series of factors. For example: is the job a career move for the candidate? If yes, that makes the job more desirable to the prospective candidate and will likely translate to a drive to succeed. Or another factor: has the candidate demonstrated success in previous jobs with similar performance objectives? Responses to this question can be predictive of future success.
Put your research into action
But what does this ''scorecard'' boil down to? It's about collecting data from prospective hires and analyzing it in a comprehensive way. That is where AI can transform the process, helping to integrate data from multiple sources and help talent acquisition teams use it when they need that information-before the hire, rather than after the fact. Beyond the impressions a candidate makes in an interview, AI can help create a comprehensive analysis of the data you collect-saving money, creating future predictions, assessing the effectiveness of recruiting channels, determining which job boards or agency is working best, and providing recommendations for the future in the recruitment process.