The 37% rule came into existence as a result of the most famous puzzle, the secretary problem. The setup of the problem is as follows. Imagine you are interviewing candidates for the position of a secretary, and your goal is to maximize the chances of hiring the best candidate in the pool. Even though you cannot assign scores to the candidates, between any two, you have a preference. Therefore, you know their relative ranking once you interview each one. The order in which they come in is random, and they come in one at a time. You can offer the job to an applicant at any point and they cannot refuse it (or in other words, are guaranteed to say yes). Once you offer the position and the candidate accepts it, the whole process is terminated. So, if you make an offer, you don’t get to interview the remaining. Furthermore, once you pass over an applicant, he/she is gone forever.
The secretary problem asks the question: for how long should I look for the ideal candidate, and when should I stop and make an offer? Somehow, this problem got to the ears of Merrill Flood, a renown figure in the mathematical world. In 1958, it was him that made the discovery of the 37% rule.
The 37% rule basically states that when you need to screen a range of options in a limited amount of time – be it candidates for a job, new apartments – it should not involve more than 37% of your time. This is termed as the exploration stage.
After you’ve explored your options, you transition to the exploitation stage. Exploit in this context doesn’t mean to take advantage of or misuse, but to seize the opportunity and capitalize on your knowledge.
In the case of the secretary problem, if you give yourself 90 days, spend the first 33 days and then pick the best choice immediately after that. You’ll find the optimal candidate 37% of the time, which is an odd coincidence.
Startups also use this technique all the time. For example, an early-stage startup spends a subsequent amount of its life span figuring out the right sales strategy. It might take a few months or a few years to identify the correct playbook, ideal customer profile, and product to sell but once you do, you’ll find that the business starts to scale.
The same dynamic comes in play during a management team meeting while debating over continuing with the current strategy or migrating to a new one. When is the right time to switch? When do you have enough information? And how do you pick among a group of options? In short, it’s all about figuring out which option has the greatest upside.
This rule has even changed the way investors perceive startups and advising them. It provides a framework for thinking through difficult decisions, the kinds of questions we’ve asked computers to solve for decades. This strategy, however, is the optimal one. Once you grow familiar with this pattern, you’ll automatically see it appear across many places.