Case study
London-based wine and restaurant chain
Getting found for dinner in Canary Wharf
The reported share of AI answers in the Canary Wharf where-to-eat question slice that named the restaurant branch.
ChatGPT
Google AI Overviews
Google Gemini
ClaudeDuring the programme
10.77%Local where-to-eat visibility
Up from the agreed 1.4% baseline and above the 3% target.
Published across the restaurant group during the programme.
12Programme guides live
Practical guides published across the wider restaurant group, a delivery count rather than a guest result.
Published work, not a before-and-after outcome.Local where-to-eat visibility in Canary Wharf reached 10.77%, up from the agreed 1.4% baseline and above the 3% target. The programme published 12 practical guides, then measured the branch-level questions guests use for dinner, drinks, groups and booking decisions.
The results
What changed?
From 1.4%
The reported share of AI answers in the Canary Wharf where-to-eat question slice that named the restaurant branch.During the programme; local dining-question comparison.| Measure | Starting point | Result |
|---|---|---|
| Local where-to-eat visibilityThe reported share of AI answers in the Canary Wharf where-to-eat question slice that named the restaurant branch.Up 9.37 percentage points; above the agreed 3% targetDuring the programme; local dining-question comparison. | 1.4% | 10.77% |
| Programme guides livePractical guides published across the wider restaurant group, a delivery count rather than a guest result.Published across the brand during the programmePublished across the restaurant group during the programme. | Not reported | 12 |
Measurement window
What exactly was measured?
- Agreed baseline
- Agreed starting point: 1.4% for local Canary Wharf where-to-eat questions.
- Latest comparison
- 10.77% for local Canary Wharf where-to-eat questions during the programme.
Work
What changed during the programme?
Mapped branch and occasion questions
Guest questions were separated by branch and occasion, including dinner, after-work drinks, groups and booking decisions in Canary Wharf.
Published practical venue guides
Twelve practical guides were live across the wider brand during the programme, giving assistants clearer material for location-led recommendations.
Measured the Canary Wharf slice
The local result was kept within its Canary Wharf question slice rather than presented as a city-wide result.
Inside the programme
The work behind the result
Three kinds of work shaped the programme. Each is described in more detail below.
- Step 1Mapped branch and occasion questions
- Step 2Published practical venue guides
- Step 3Measured the Canary Wharf slice
Inside the programme
The result focused on a local dinner decision
The programme focused on Canary Wharf decisions, where place, time and group needs shape a guest's choice of where to eat.
The agreed target for the local where-to-eat slice was 3% from a baseline of 1.4%. This was a narrow local goal, not a promise that every dining question across London would rise with it.
Inside the programme
Content followed the questions guests use
The work focused on decisions a guest can act on: dinner, after-work drinks, group plans and whether to book. One guide brought those Canary Wharf considerations together without turning the page into a list of search phrases.
During the programme, 12 practical guides were live across the brand. The wider library created useful context for different venues and occasions while the local guide supplied branch-specific evidence.
Inside the programme
The local slice moved beyond its target
The latest reported local reading was 10.77%, up 9.37 percentage points from the agreed 1.4% baseline and above the 3% target. The chart shows only this local where-to-eat measure; it does not represent visitors, bookings or revenue.
The programme tracked 55 questions across London and Canary Wharf in ChatGPT, Google AI Overviews and Gemini. The record does not establish that all 55 remained one fixed cohort, or that 55 questions belonged to this branch alone.
Inside the programme
What another multi-location group can take from it
Measure the guest decision at the level where it happens. A location-specific question set can show whether one branch is gaining relevance for the choices guests make nearby.
Build guides around real choices and review each location against an agreed baseline and target. Use the result to prioritize the next evidence to develop, rather than treating one movement as proof of commercial impact.
Prompt set
Did the question set stay fixed?
The programme covered 55 questions across London and Canary Wharf. The source does not establish one unchanged 55-question cohort across every comparison, and it does not assign all 55 questions to the Canary Wharf branch.
Engine coverage
Which engines were included?
The programme monitored ChatGPT, Google AI Overviews and Gemini. The local 10.77% figure is an AI-visibility reading, not a count of diners or website visits.
Definitions
How are the measures defined?
- Local where-to-eat visibility
- The reported AI-visibility reading for the Canary Wharf where-to-eat question slice.
- Agreed target
- A programme benchmark used to judge movement, not a forecast of bookings or revenue.
- Local question slice
- The subset of tracked where-to-eat questions associated with Canary Wharf decisions.
Interpretation
What are the limits of this result?
- The comparison covers local dining questions and does not establish a city-wide result.
- The result is an interim observation during the programme, rather than a final evaluation.
- The record supports visibility reporting only. It does not support claims about bookings, traffic, revenue or causation.
Your questions
What does this mean for your business?
How much did local Canary Wharf visibility improve?
The local where-to-eat measure rose from the agreed 1.4% baseline to 10.77%. That is a gain of 9.37 percentage points and sits above the agreed 3% target.
What did the programme publish?
Twelve practical guides were live across the wider restaurant brand during the programme. The content addressed dinner, after-work drinks, group plans, booking decisions and local Canary Wharf considerations.
Which AI surfaces were monitored?
The programme monitored ChatGPT, Google AI Overviews and Gemini. The 10.77% result is an AI visibility reading for a local question slice, not a count of diners or visits.
What should a multi-location restaurant measure by location?
Create a defined question slice for each branch around neighbourhood, occasion, group size, timing and booking needs. Compare each branch with its own baseline and target instead of relying only on a group-wide average.
What content helps a restaurant appear for local dinner decisions?
Publish accurate, branch-specific guidance that answers the choices guests make, such as dinner, after-work drinks, group plans and whether to book. Keep venue facts current and make the next action easy to verify.
Next step
Measure the local questions that lead to a visit
The free audit separates brand-wide visibility from the location and occasion questions guests use when deciding where to go.