That was a brewery franchise with an acclaimed product and no visibility in a saturated local market. Restaurant marketing lives or dies on discovery, and discovery now happens in three places at once: local search, maps, and an assistant answering where should we eat.
| Measured across 30 days | Count |
|---|---|
| Buying questions tracked in this sector | 2,199 |
| Brand mentions recorded on them | 43,699 |
| Different companies the engines named | 18,403 |
Rankxa production data, 30 days to 11 September 2026, across ChatGPT, Claude, Gemini and Google AI Overviews. Where to eat and where to drink questions, asked at the moment a decision is being made.
Nobody visits your website to decide where to eat. They ask something, get a short list, and pick from it. Your job is to be on the list.
The highest intent question in the sector, and the shortest list.
Reviews are the proof layer for both people and models.
Operational data is the most common reason a recommendation fails.
Menu content is retrievable content, and most of it is trapped in images.
Two local discovery engagements with numbers attached to the outcome rather than to the activity.
5,802 local searches in 30 days and 745 tracked customer actions.
Reached 10,000 people in 7 days.
Across 2,199 restaurant and dining questions we track, the engines named 18,403 different venues and brands in the last 30 days. That is a very crowded field, and the answer lists are short.
Sector figures are counted over the prompts in our set that ask about restaurants. The agreement range is measured across all comparable prompts, not this sector alone.
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Local discovery work suits single sites well, because the competitive set is small and the fixes are concrete. The brewery result came largely from Google Business Profile optimization and local search work rather than from a large media budget. We would rather do that properly for one site than spread a thin campaign across ten.
Constantly. Where to eat is close to the ideal assistant question: local, subjective, and tedious to research manually. We track 2,199 of those questions and the engines named 18,403 different venues on them in 30 days. The lists are short, so the difference between being named and not being named is most of the outcome.
Reviews, for discovery. Ads buy reach and stop the moment you stop paying. Review presence and consistent local data feed both the maps result and the assistant answer, and they keep working. We run ads where there is a specific job for them, such as a launch or a slow midweek, rather than as the whole program.
It matters more than almost anything else on the site. A menu inside a PDF or an image is close to invisible to the systems answering dietary, price and cuisine questions. Publishing it as real text with structured data is usually the single highest value change we make on a restaurant site.
Faster than most sectors. The brewery figures were measured over 30 days. Local data fixes and profile work take effect quickly because you are correcting information the platforms already want, rather than trying to build authority from nothing.
Pages that cover the next question people usually have after this one.
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