Most digital ad platforms still treat every impression as if it's equal. Reaching a CEO or a rocket engineer often costs about the same as reaching a random teenager scrolling late at night. But advertisers don't see those audiences as equal, far from it. Many are willing to pay 10–20x more for high-value audiences like decision makers, software engineers, or passionate sports fans.
This gap presents a massive opportunity: pricing based on user value.
Instead of simply charging for reach, platforms can let advertisers bid more to reach the users they really want. That means higher revenue without increasing ad load, while also making ads more relevant to the people who see them.
Today, most auctions factor in historic interest and engagement signals. But what if the system also accounted for the relative value of each user to an advertiser? By incorporating richer inputs, like job role, professional influence, or depth of fandom, the auction could surface ads that are not just relevant, but economically aligned with advertiser goals.
Technically, this isn't a stretch. Ad auctions are already designed to weigh multiple variables; they just need the right signals. With advances in classification and machine learning, platforms can build user-level scoring across jobs, interests, and demographics at scale.
From the advertiser side, this shift is long overdue. Ask any major brand, and they'll confirm: they don't just want impressions, they want the right impressions. And they're willing to pay for them.
Pricing based on user value could unlock one of the biggest untapped growth opportunities in digital advertising.