What Actually Moves the Needle on Restaurant Search Performance

Before a guest ever opens a menu, checks a price, or decides tonight's the night, they run into a listing first: hours, a photo, a star rating, how far away it is. That first impression is happening on Google, on Apple Maps, and now increasingly inside AI assistants that will just answer "where should we eat?" in a single sentence. The listing either wins the visit or loses it, and most operators never see the ones they lose.

Two things have raised the stakes. Local search is now the default way people decide where to eat, and generative AI (ChatGPT, Google's AI Overviews, Perplexity, Apple Intelligence) has started answering that question directly, often pulling from structured data operators don't realize they control.

Both reward the same thing: accurate, complete, well-structured information about a location. And both punish the same thing: outdated hours, missing menus, details that don't match from one place to the next.

This report answers a practical question: of everything an operator could spend time on, what actually moves search performance? We pulled from two sources: Marqii's own location-level platform data, and a survey of 54 restaurant operators.

The gap between those two datasets, between what works and what operators spend time on, is the real story.

Hours are the single highest-impact field on a listing, and the effect isn't subtle. The effect also compounds with frequency: if we sort locations into tiers by how often they update hours, engagement rises at every step.

Search engines and AI assistants read frequent, accurate updates as a signal that a location is active and trustworthy, and they surface it accordingly.

Hours are also the field guests punish hardest when they're wrong. A locked door at a posted open time is the fastest way to lose a guest permanently.

Accuracy protects the visit. Frequency earns the visibility that creates it.

• No hours activity
  45
• Updated at least once
  177
• Top quartile of update frequency
  291

To break this down, we scored each location's Google Business Profile completeness into four tiers, from Starter through Optimized. Engagement rises cleanly with completeness, and it rises on the actions that actually fill tables, not on vanity metrics.

A complete profile is the sum of many small fields: photos, menu items, attributes, categories. Each one gives search engines and AI assistants another reason to surface the location, and another answer to a guest's question.

Photos in particular remain underused. Across the platform, only 39% of menu items carry a photo, even though photos are among the first things a guest looks for and are among the assets AI results now pull directly.

Most of the gap between Starter and Optimized is closable with fields operators already have on hand.

Structured menu data (items, prices, descriptions, and photos in a machine-readable format) is what lets a listing answer a question before a guest has to click. Its impact is large, and the pattern contains a genuine surprise.

However, engagement does not rise in a straight line with completeness. The jump from Low to Medium is enormous; the step from Medium to High gives some of it back.

What this means: a well-structured, focused menu reads more cleanly to both guests and AI parsers than an exhaustively detailed one. At some point, more fields may add noise rather than signal.

This reframes the goal from "complete everything" to "structure the essentials well."

The essentials themselves are unevenly covered today. Price coverage is strong. Descriptions and photos, the fields that most help a guest decide and feed AI answers, are missing on more than half of items.

Review response is one of the few presence actions with a double payoff: it signals engagement to search algorithms and it signals care to the guest reading it.

In our operator survey, 72% claim a 90-100% review response rate. But in practice, less than 30% of brands respond to at least half their reviews. 

And that number drops sharply as location count grows, which points to a scale problem rather than a will problem. Operators want to respond; at 20, 50, or 200 locations, doing it consistently by hand becomes impossible.

of operators believe they respond to 90-100% of reviews.

That belief holds at a handful of locations: less than 30% of brands are responding to at least half of all their reviews. 

Action lags well behind awareness. While 89% of those surveyed are aware of AI's role in restaurant discovery,  only 49% are folding it into their current strategy.

What's next for restaurant search

These are the problems that Marqii is built to solve. 

Marqii helps operators update hours, photos, menus, and attributes once and push them accurately to every platform that matters, turning the actions in this report from an impossible manual checklist into a maintainable system.