Good profiles are graded on a curve

Advice about local profiles used to arrive as a fixed number. Get onto 75 percent of the long-tail directories, fill 90 percent of your core fields, answer 85 percent of your reviews. A location would reach the number, mark it done, and move on. The trouble is that a fixed number says nothing about the businesses a customer actually sees beside you, and those businesses do not hold still. While one location rested, its neighbors added photos, gathered reviews, and refreshed their hours, so the same profile that cleared the bar in October was behind by April without a single field changing.

So this study set aside fixed targets and asked a local question instead. In your own neighborhood, do you have more photos than the businesses around you, more reviews, a higher rating, fresher updates. The answer defines a location as strong or weak against its real competitors rather than against a national rule of thumb, and it is a moving answer, recomputed as the field around each location changes.

We are not claiming a single win-rate number tells a location what to fix on a Monday morning. It is a coarse read, closer to a smoke alarm than a repair plan, useful for telling a strong profile from a weak one at a glance. The specific work always comes back to the exact signal a nearby competitor is beating you on.

The point of a profile is what customers do when they find it, so the study measured Google customer actions rather than search rank, which swings day to day. To compare a small brand against a large one fairly, each location's actions were scored against the average location in its own brand, in standard deviations. Read across the deciles, the line rises. An average location sits around the sixth decile, and the distance from the worst tenth to the best tenth is about 1.37 standard deviations of customer actions.

Rolling every signal into one win rate is useful for a glance, but the individual signals do not all behave the same way against a location's neighbors. Three of the most visible ones show three different curves, which is why matching a static maximum on all of them wastes effort.

The same profiles that draw more customer actions also fare better in AI answers. Splitting 310 brands into five win-rate groups, the study read how often each group's locations were mentioned in AI answers and how often they were spoken about positively, across four models from October 2025 through March 2026. Both rise with standing, though this is a cross-brand correlation rather than proof of cause.

The harder question is whether a weak profile is stuck. To answer it the study took each brand's poorest quarter of locations at the October scan, then measured their Google actions over the year before that scan and the year before the April one, so seasonal swings cancel out. Sorting those locations by how much their win rate moved over the six months, the ones that improved grew their actions and the ones that declined fell.

The turnaround story holds everywhere, but the payoff is larger in some industries than others. The gap in yearly action growth between the big improvers and the decliners among weak locations runs from about three points in food and hospitality to more than six in healthcare, so the same effort buys more in a field where a full, current profile is scarce.