After tracking the 10-year performance of 100s of candidates I placed in mid-management roles from 1980 to 2000, I finally figured out how to measure Quality of Hire. It’s described in detail in Chapter 16 of Hire with Your Head (4th edition, 2021, Wiley). The other day I asked ChatGPT if the approach was still accurate or now flawed. Here’s the full dialogue. Below is the short summary.


In the modern hiring landscape, predicting the quality of hires is crucial for organizational success. Utilizing a combination of innovative tools like Performance-based Hiring can significantly enhance this predictive capability.

Firstly, the performance-based job description offers a clear and detailed outline of what success looks like in a given role. Instead of just listing skills or qualifications, it delineates the tasks, projects, and outcomes a candidate is expected to achieve. This shifts the focus from mere qualifications to actual performance capabilities, allowing companies to target candidates who can deliver tangible results.

Complementing this is the performance-based interview (PBI). Traditional interviews often skim the surface, focusing on generic questions. In contrast, PBI delves deep into a candidate’s past achievements, challenges, and behaviors. By evaluating how a candidate performed in previous roles and situations, companies can gain a richer understanding of their potential future performance.

The Hiring Formula for Success further refines the process by emphasizing the critical relationship between a candidate’s ability to do the job and their fit within the company. It underscores the importance of intrinsic motivation and alignment with the company’s culture, pace, and managerial style. By ensuring that candidates not only have the skills but also the drive and cultural fit, companies can significantly increase the likelihood of a successful hire.

Lastly, the Quality of Hire Talent Scorecard acts as a powerful tool to consolidate these insights. By providing a structured framework to evaluate candidates both pre- and post-hire, it offers a consistent and objective measure of quality. Companies can use the scorecard to predict a candidate’s performance during the hiring process and then validate these predictions once the candidate is on the job. This continuous feedback loop, bridging pre-hire predictions with post-hire outcomes, allows organizations to refine their hiring processes continually.

In essence, by integrating these four tools, companies can create a robust, evidence-backed hiring process that not only predicts but also validates the quality of their hires, ensuring sustained organizational growth and success.


Lou Adler is the CEO and founder of Performance-based Hiring Learning Systems – a consulting and training firm helping recruiters and hiring managers around the world hire more leaders. Lou is the author of the Amazon top-10 best-seller, Hire With Your Head (John Wiley & Sons, 4th Edition, 2021), The Essential Guide for Hiring & Getting Hired (Workbench Media, 2013) and LinkedIn Learning’s Performance-based Hiring video training program (2016). His new course on LinkedIn Learning – Integrating Performance-based Hiring in into the Performance Management (2023) – in now available. Make sure you check out his Hire with Your Head Book Club for the latest hiring ideas and trends.