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Most companies recruit like a sales funnel. They contact 100 people, 20 respond, and at every stage they weed out more candidates until a remainder is left to hire. The 20 who respond are not the top 20 percent of the 100. They answered at a moment when their current job was going badly for them, which makes the starting pool weak before the first interview happens.
A sales funnel works because a widget or a piece of software is relatively static and rational. Hiring puts an unpredictable hiring manager and recruiter on one side and an unpredictable candidate on the other, so treating it as a linear, transactional pass-through rarely fits. Sales has existed for hundreds of years, while recruiting as a craft is about 40 or 50 years old, and borrowing the sales model has stuck.
Hiring whoever is left at the bottom pulls a company toward the average as volume grows, even with a strong interview process. The alternative starts with a confident list of who the top 20 percent are, then uses the interviews to test that list. A manager named Jeff Daniel coined the term funnel of doom at Trilogy, an infamous company in recruiting circles.
Why do replies skew toward unhappy workers?
People who answer cold outreach have chosen to answer, and that choice carries information about them. Economists call this adverse selection, a problem George Akerlof described in his 1970 paper on used cars, where the sellers willing to sell are the ones with the most to hide. The same logic applies to anyone who volunteers to move. Akerlof shared the 2001 Nobel Memorial Prize in Economics for this line of work.
How did the sales funnel idea begin?
The ancestor of the funnel is the AIDA model, which advertising pioneer Elias St. Elmo Lewis sketched in 1898. It described a buyer moving through awareness, interest, desire, and action. Later marketers drew the stages as a narrowing shape and added a count of how many prospects drop out at each step. Recruiting borrowed that picture because both fields start with a large pool and end with a small number of decisions.
How does regression to the mean pull hiring down?
Francis Galton described regression to the mean in the 1880s, after noticing that very tall parents tended to have children closer to average height. The effect appears whenever outcomes mix skill with chance, because extreme results are partly luck and do not repeat in full. A hiring pool that begins near average and is filtered by noisy steps will produce hires who land near average too. Raising the quality of the starting pool is the only part of that process that shifts the average itself.
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