Store quality explains 0.7% of who gets recommended. Public fame explains 1.2%. Web traces explain 0.2%. Intent, the one factor that separates recommended stores from the rest, explains 1.2% of frequency once you are already in. Four independent signals, four numbers within a rounding error of zero. Then we asked a different question: does what the model recommended last month predict what it recommends this month? That number is 61.4%, and it still has not moved after 15 days.
By this point in the series we had tested store quality, public fame, and how intent separates recommended stores from stores that never get picked. All three landed close to zero: 0.7%, 1.2%, and 1.2%. One layer was still untouched: web traces, meaning how visible a brand is across the open web, not just its own store.
We measured it using distinct domains mentioning the brand, whether the brand's own site turns up at all, and mentions on review sites, comparing our most reliably recommended brands, the 301-brand core that survives every model and search setting we test, against everything else.
Put the four correlations next to each other and the pattern is not subtle. Store quality, fame, web presence, and intent all sit within a point of zero. Then look at what happens when you stop asking about the brand entirely and ask about the model instead.
We took June recommendation data and July recommendation data, two genuinely separate periods pulled from different tables, spanning a model change in between, and matched them on 1,082 brand-intent pairs built from the same 50 intents. Then we correlated June's position and frequency against July's, using Pearson correlation on the raw values.
June's position predicts July's position at r = 0.784, R squared = 61.4%. June's frequency predicts July's frequency at r = 0.737, R squared = 54.4%. And June's position predicts July's frequency at r = negative 0.565, R squared = 31.9%, negative because a better position number, lower is better, lines up with a higher frequency, which is exactly what you would expect if position and frequency are two views of the same underlying stability.
A signal that predicts itself a month out is only useful if it also holds steady in between. We already knew that comparing the same sweep against itself produces about 44% turnover from ordinary noise, nothing to do with real change. We extended that same comparison out to 1 day apart, 14 days apart, and 15 days apart, on 234,283 scans across 57,242 domains.
Put the two findings together. Recommendation is not explained by anything measurable about the brand in the world, not its store, not its fame, not its web presence. It is explained, at 61.4%, by what the model already did last time. And that state does not drift for at least 15 days. That is not a weak signal buried in noise. It is a stable, self-consistent, internal property of the model, and external brand attributes barely touch it.
The practical consequence is blunt: there is nothing to monitor day to day, because nothing changes day to day. A single, well-timed scan tells you what will still be true weeks later. Continuous tracking would be selling reassurance about a number that was never going to move.