Let me let you in on a little secret: Most hiring managers talk a good game about how they want “an all-star in every position,” but deep down they don’t really mean it. If you probe a bit more, they will admit that to maximize the value their team produces, they focus on solid hires, the foot soldiers of the corporate world. On the surface, this approach makes sense. All-stars are expensive and hard to find, while foot soldiers look like value hires. But in almost every setting, hiring managers would produce better results by spending more resources on finding rare, star performers rather than filling their open positions with average performers.
The most common defense of the incremental approach to talent acquisition assumes that most of a team’s value is created by solid, slightly above-average workers. Some hiring managers tell me that if they want to keep up with their company’s growth vision, they need to focus most of their energy on filling a large number of vacant spots. These hiring managers then typically explain that they have a large volume of work, and if they can just hire enough people, the work will get done.
The view above is often rooted in a belief that work output has a linear correlation with the number of hours worked. For example, if a worker can produce five shoes an hour, a shoe factory needs to employ 10 workers to produce 50 shoes in an hour. This type of staffing model might be a wise strategy for procedural work. In high-complexity jobs, however, each person needs to tackle a wide variety of ambiguous challenges and navigate in unchartered territory. Most managerial positions and information-age jobs, from marketing to computer engineering, fall in this category. In this context, the value created by each person is more important than the volume of work they cover. To go back to the shoe example, the highest-selling signature basketball shoe sold about 10 times more than the fifth highest-selling shoe. The team of professionals for the top-selling shoe probably did not have 10 times as many staff members compared to those who brought other shoes to the market, but the value they created was worth that much more.
This example is not as unique as it may look at first glance. Rather, in my experience, it’s fairly representative of the broader relationship between talent and results. To illustrate, consider competitive gaming and sports. I analyzed the lifetime prize money won by over 450,000 poker players. Here, the dollar value produced by each person can be precisely tracked. Since the dataset is quite large, it’s more likely to be representative of the real-world differences of value produced by thousands of individuals in each profession. The results are clear; there are hundreds of thousands of middle-of-the-pack poker players who produce, relatively speaking, similar results. But would you rather have hundreds of these middle-of-the-pack performers on your team, or would you rather have one or two stars? The latter would be by far the better bet. According to my calculations, 85% of the lifetime prize money collected in poker was won by the top 10% of the players. I collected similar data for 25,000 ATP tennis players. The results I found were even more remarkable here: 98% of the lifetime prize money in tennis was collected by the top 10% of players. The results are clear. In statistical terms, the number of average versus star performers may follow a normal distribution. The value created by these individuals, however, follows a power distribution.
Multiple studies suggest that the same forces operate in corporate offices as on tennis courts and at poker tables. A study that examined the performance of over 600,000 professionals across 198 samples found that top performers are about 400% as productive as average performers. A separate study estimated high performers to be 800% as productive as average performers in high-complexity jobs, such as managerial or software development roles.
This brings me to the core of the argument against the common misconception that since star performers are so few, they only make up a small share of the entire value created. Yes, star performers are rare. However, they have an outsized influence on performance. Think about it this way: A star computer engineer is unlikely to write 100 times more code than an average performer, but the quality of the code this star writes can be the difference between whether billions of people use Google versus Bing or other search engines.
This is why winning companies are making bigger bets on their talent strategies. They have realized that a disproportionate amount of the value in their company is produced by star performers, and they are willing to make the required investments to hire more of them. When I explain this approach to hiring managers and they remain skeptical, I often pose this question: Apple created its operating system iOS 10 in two years using 600 engineers; Microsoft, by contrast, took five years and 10,000 engineers to produce (and then retract) Vista. Which staffing model do you prefer?
My question to the hiring managers reading this article is: Do you know how much of the value at your company is created by star performers? Answering that question might help you understand how much you should be willing to invest in bringing the next set of stars on board. To borrow a line from Nextdoor CEO Sarah Friar, “You never increment your way to greatness.” And unless your company plans to make just incremental progress going forward, you might want to start by understanding how much you are willing to bet on recruiting stars.