Every center wants to solve this complex problem, but in the competitive context, centers often “leap” before they have fully “looked.” It may seem contradictory, but slowing down the hiring decision and comparing applicants to prior applicants or former hires can increase the quality of your hires.

Limited Past Data

Any hiring choice involves an “optimal stopping problem,” famously known as the “Secretary Problem.” Martin Gardner presented this puzzle in the February 1960 issue of Scientific American, though it existed in various forms before then. Gardner popularized it, and by the 1980s, the issue and its variations had generated so much mathematical research that mathematicians considered it a subfield.

The Secretary Problem Explained

Imagine you’re interviewing secretary candidates and want to hire the best one. In this situation, the applicants arrive in random order, you have minimal information to compare them, you can make an offer to any of them, and they will immediately accept. You only have one chance to make an offer—passing means they are out of the pool. What steps do you take to hire the best candidate?

You can fail by either stopping too early or too late. Stopping too early misses the best applicant because you haven’t seen them yet. Stopping too late means you passed them by, waiting for someone better.

The Importance of Hiring the Top Applicant

Hiring the top applicant is important, but being the best seen so far isn’t enough. By default, the first applicant will be the best you’ve seen. With each candidate, the chances that the next individual would randomly be the “best yet” decreases:  the second applicant has a 50% chance (1/2), the fifth 20% (1/5), and so on. However, these “best yet” applicants improve comparatively over time, even as they appear less frequently.

Optimal Hiring Strategy: The Look-Then-Leap Rule

Ad hoc tactics, like randomly choosing a stopping point after five interviews, rarely produce the best results. The “Look-Then-Leap Rule” is the optimal strategy. Spend time researching and gathering information without deciding. Set a baseline, then hire the first applicant who is better than those interviewed during the look phase.

Strong mathematical proof shows the look phase maximizes success when set at 37% of applications. You can find details online. A recruiter hires the first individual to exceed this benchmark after interviewing 37% of applicants. If you have 100 applications, interview 37 without making an offer. Then, hire the next “best” applicant. This strategy works when filling a single position.

Hiring Multiple Applicants

When hiring multiple applicants, such as frontline staff, you must define a look phase to set your baseline. Then, hire applicants better than “the best seen so far” until you fill the class.  Knowing how many applicants you have in the pipeline will help you decide the size of the “look” phase. If applicants reject offers, you can adjust this threshold to offer earlier and more often. Still, the main idea remains—before making offers, assess the quality of applicants and set a benchmark.

What If We Can Compare to Past Hires?

This is known as the “Full Information” variation of the optimal stopping problem. In the basic Secretary Problem, we don’t know how applicants will do. However, in the full information variant, we can compare applicants by administering a screening test. 

In these instances, recruiters replace the Look-Then-Leap Rule with a “Threshold Rule” and set a strict cutoff score for hiring. Recruiters can dynamically modify this criterion based on the number of remaining applicants, their projected quality (based on prior applicants), and the applicant acceptance rate.

Start with a high threshold, such as hiring the top 5% of applicants. Adjust the threshold based on the distribution of scores thus far and hire applicants who meet or surpass the criteria.

This lets recruiters make quick, data-driven hiring decisions, ensuring quality and eliminating uncertainty associated with the Look-Then-Leap strategy.  Recruiters can quickly hire top talent by changing the threshold based on applicant scores.

The Value of Data

In either case, accurate, relevant screening tests, data gathering, and analysis are crucial. Improving the quality of screening requires several key steps:

• Collaborate with subject matter experts to determine all the essential skills, knowledge, and abilities required for each role.
• Use a mix of question types to assess the identified competencies comprehensively.
• Develop clear questions that directly relate to job tasks and responsibilities. Ensure these questions are unambiguous and test the desired skills accurately.
• Use situational judgment questions or case studies that mimic real job challenges to assess how candidates apply their knowledge in practical situations.
• Pilot the test with a small group of current employees or volunteers to identify issues. Use feedback to refine questions and improve clarity and relevance.
• Establish a scoring system and define a clear threshold for passing that reflects the minimum competency level required for the job.
• Analyze the results regularly to ensure they accurately predict job performance and adjust the test as needed based on performance data and feedback.

By leveraging these screening tests for benchmarking, recruiters can streamline the hiring process. This allows data-driven decisions, ensuring the best fit. It also saves time and resources.

Conclusion

Quantitative data is necessary to benchmark hiring results and make strategic hiring decisions. Strong screening tests let recruiters compare prospects, allowing for better results.  When past data is scarce, deploy a Look-Then-Leap approach to set a baseline before making any hiring decisions.

Where full information already exists, use it to define the hiring threshold and dynamically update it as new information arrives. Recruiters may increase hire quality by defining essential competencies, creating clear and appropriate evaluations, and refining based on performance data. This data-driven methodology improves efficiency, selects the best applicants, and boosts the company’s chances of success.

Brent Jernigan, Director at COPC, brings over two decades of expertise in customer experience process improvement, specializing in performance improvement, quality systems and workforce management. Brent has led enhancement efforts across five continents, improving contact center performance in diverse industries such as technical support, hospitality, airlines and healthcare.

Noted for his ability to translate complex analytical concepts into everyday language, Brent is a highly sought-after training facilitator. His primary focus areas include contact center certification, business transformation and training.