The volume of real buyer intent flowing to a category or intent cluster through AI systems.
Recommendation Demand is the volume of buyer intent directed at a category or specific intent cluster through AI systems. It is the AI-era equivalent of search volume: how much buyer attention actually flows through a given set of prompts. High Recommendation Demand intents are where recommendation visibility is most commercially valuable.
Not all intents are worth the same. Winning a high-demand intent like "best protein powder" matters more than dominating a niche one. Recommendation Demand tells a brand where to concentrate by sizing the buyer attention behind each part of the Buyer Intent Graph.
"Best running shoes" carries far higher Recommendation Demand than "best shoes for plantar fasciitis in wide sizes." A brand prioritizes coverage of high-demand intents first, then expands into the long tail.
Every buyer prompt is attention. Watch it split unevenly across intents the moment it arrives.
Red means enormous buyer demand. Green means almost none. Hover a row for the exact level.
Picture a river of buyer attention. The bigger the island, the more of that river an intent pulls in.
Winning one high-demand intent
can outperform
ten low-demand intents.
Buyer attention moves through related intents in sequence. Watch it travel.
Plot Coverage against Demand and the biggest opportunity finds itself: high demand, low coverage.
One high-demand intent branches into dozens of smaller ones. Each carries a fraction of the attention.
Recommendation Demand is not static. It moves with the calendar the same way search volume does.
High demand and low share is the biggest opportunity on the board. Low demand and high share is nice, but it does not scale.
Get a free AI Commerce Score first, it maps your category's demand. Then see exactly which intents carry the most buyer attention.
Illustrative example · single-site signal for atomfoundry.dev.
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