How to Measure Local AI Visibility with a Geographic Grid

A chain may receive AI recommendations near one store and disappear a few streets away from another. A question about the whole city cannot reveal that difference. Teams deciding where to correct information, investigate a branch or study competition need a sample within the city.

A geographic grid is a set of observation points defined in advance. Comparable local tasks are repeated at each point under documented conditions. The result is a matrix of presence, eligibility and quality by area, not a universal ranking or an automatic estimate of people reached.

This article proposes an original Mentio operating protocol for local marketing teams and networks of establishments. It starts after market and language selection, covered in our guide to measurement by country and language. The question here is what changes within one area when the origin point, time and businesses able to serve the customer change.

Record three different locations

Separate the study's target point, the place written in the prompt and the location the system may have used. Writing “I am at point P4” does not prove the platform received that position as a device signal.

The Google Search location documentation describes estimates using device, account, activity and IP information. The ChatGPT search help page distinguishes approximate IP-based location from optional device location sharing. Record what you can actually verify on each surface.

Use three evidence labels: location verified through a documented tool control; location stated only in the text; and unknown or contradictory location. Retain all three, but calculate their results separately. An answer that repeats the prompt's address does not independently certify where the query ran.

If a provider cannot set or verify coordinates, publish a neighborhood scenario study. It can still teach you something, but should not be presented as a GPS test. A VPN or private browser window alone does not document an exact observation point either.

Draw the area around the real service

Start with a concrete business question: “Which branch appears when someone needs bicycle repair within walking distance?” Then fix the area, category, language, surface and commercial unit. This prevents a citywide study of a service that only covers part of the city.

Draw a polygon representing the area of interest. It might cover a service territory, several neighborhoods or selected shopping routes. Identify barriers: a river without a nearby crossing, railway lines, highways, closed parks or steep slopes can make two nearby points very different in practice.

Keep three sizes separate: study extent, spacing between points and the radius requested in the prompt. A three-by-three grid with 500 meters between points spans one kilometer between its outermost points on each side. That does not mean the business serves a one-kilometer radius or that every recommendation respects that distance.

Save the geometry and inclusion rule before reading responses. Document excluded points and reasons. Do not remove areas later because the brand performs poorly there.

Choose points and resolution without inventing precision

A regular grid makes a study easier to repeat. Sampling by zone may better represent a city with very different densities and access conditions. Choose the design for the decision and document why each point belongs in the sample.

Nine points can be a useful pilot for finding execution problems and estimating cost. They are not a universal statistical sample size. Add resolution where decisions require it while retaining a fixed layer for subsequent waves.

Assign stable IDs such as P1 through P9, reference coordinates, neighborhood and inclusion reason. If a point moves because it is inaccessible or incorrectly mapped, create a new version. Avoid private homes; use public reference locations and the precision needed for the work.

Do not interpolate nine observations into a continuous map and present every street as measured. A point describes its recorded scenario; the space between points may remain unknown. Make those gaps visible.

Build comparable local tasks

Start from an established prompt bank. For the grid, add the conditions driving local choice: category, need, budget where relevant, distance or access time, time window and availability.

A declared scenario might be: “Find a bicycle workshop within a 15-minute walk of [public reference location] that can inspect the brakes this afternoon. Include the address and source.” Another may ask for a comparison without urgency. Keep wording and intent equivalent across points.

Do not place “best workshop in the city,” “workshop near me” and “reviews of my brand” in the same denominator. The first has a different geographic scope, the second depends on location context and the third already introduces the brand.

Also define what qualifies as a recommendation. For example, an establishment included among suggested options with enough identity to distinguish the branch. A corporate website citation without a local recommendation is a separate observation. Asking for three options does not require the system to return three; retain the actual count.

Freeze time, opening hours and availability

Time is part of the scenario. “Open now” and “available Saturday at 10:00” can produce different candidate sets. Record local time, time zone, requested date and execution timestamp in UTC.

Separate at least three facts: published opening hours, opening status claimed in the answer and verified availability for the task. An open shop may have no appointment or may not provide the requested service. A mention does not certify that the user can buy or book.

Create a branch eligibility record for each window before evaluating results: category, service, service area and known restrictions. Mark conditions you cannot verify as unknown. Do not turn unknown into closed or unavailable.

A nighttime wave and a midday wave are comparable only when the time difference is what you intend to study. Otherwise, retain equivalent windows and record holidays, exceptional closures or incidents that may change the available supply.

Run the matrix and retain failures

Calculate the workload first: points times intents times windows times repetitions times surfaces. Nine points, two intents, two windows and three repetitions on one surface produce 108 planned observations.

Use documented sessions and configurations. Alternate or randomize point order so that one neighborhood is not always measured before a time transition and another afterward. Retain the actual order. Apply the answer variability protocol within each scenario to choose repetitions and control noise.

Keep the full response, sources, timestamp and available location evidence. Include protocol, grid, prompt and wave identifiers. Record request completion separately from observation validity.

A valid answer without the brand counts as absence. An execution error or contradictory location is a coverage failure. When a surface does not generate an AI answer, record that as its own state and explain whether the indicator is conditional on an AI answer being present.

Resolve the branch before counting brands

A network can gain corporate visibility while still failing at the relevant branch. Use a stable establishment key alongside the parent brand. Compare names, public addresses and branch URLs when available.

When the AI mentions the chain without identifying an establishment, record “brand with unresolved branch.” If it recommends another branch outside the requested area, retain the corporate mention and separately flag the local condition failure. Do not automatically treat it as operational success.

Deduplicate aliases within an answer while preserving evidence. Maintain an identifier for each local competitor too. A national competitor and a neighborhood shop may compete for the same task, but their commercial units are not interchangeable.

This complements the actions in our restaurant and hospitality GEO guide: it helps locate where a problem appears before deciding which information, listing or process needs attention.

Read point-level rates with visible denominators

First calculate execution coverage: valid observations divided by planned observations. Then, within comparable valid observations, calculate the share recommending the branch or brand under the declared rule. Always show numerator and denominator.

Do not directly combine surfaces or location evidence states. To compare waves, retain a panel of common points and conditions. You may also show each wave's full sample, clearly labeled.

The table below is fictional, not a Mentio result or an industry benchmark. Each point had 12 planned observations on a single surface. Nine failed validation, leaving 99 valid observations.

Total coverage is 99/108, or 91.7%. There are 44 recommendations among 99 valid observations, or 44.4%. That figure describes valid executions in this sample, not residents, demand or market share.

Suppose an operating rule was set before measurement: at least nine valid observations per point and a recommendation rate of 50%. Four of the eight points with sufficient coverage meet it; P8 remains unclassified. These are decisions about observed points, not evidence that the brand covers 50% of the city. The illustrative rule does not establish statistical significance.

Design a map that shows uncertainty

Each point should open its record: planned and valid observations, rate, period, location status, branches and sources. Use an explicit visual category for insufficient coverage. Do not color those points as confirmed absence.

Keep the same scale across waves. Separate result color from data-quality signals so that 1/1 does not look more conclusive than 9/12. Avoid drawing exact commercial boundaries from a handful of responses.

Include a table by zone and branch alongside the map. If you weight zones by population, sales or another signal, retain the date, source and weighting scheme. The weighted result is still a designed indicator; it does not independently reveal the actual distribution of AI users.

Turn local patterns into specific tests

If a branch disappears only when a particular service is requested, check whether sources explain the service and whether it is available. If the wrong establishment appears, inspect branch identity. If a difference occurs only at a particular time, review hours and eligibility before changing content.

A competitor change between points can be consistent with the task and distance. Review the actual recommended options and sources before calling it a visibility drop. Spatial proximity alone does not demonstrate causation.

Assign a test and owner to each relevant pattern. Repeat under the same design after a correction. The grid helps decide where to investigate; causes require additional evidence.

What a tool must demonstrate for this study

Ask for exportable evidence of points, location context, windows, responses, branches and failures. Use a small trial to verify which controls each surface actually offers. A field named “city” is not equivalent to verifiable coordinate-level execution.

Mentio can be part of brand visibility analysis, but this guide does not announce a GPS grid or real-time availability feature. To apply the protocol, verify your tool's capabilities and complete the necessary external records. Keep limitations with the report.

The useful deliverable is a list of zones and establishments with reproducible observations, explicit uncertainties and next tests. An attractive map without traceability is not enough to decide where to invest.

FAQ

What is a geographic grid for AI visibility?

It is a versioned sample of points within an area where comparable local tasks are executed. It records recommendations, conditions and observation quality by point without assuming that it represents every street or user.

Does naming a neighborhood in a prompt mean I measured from there?

No. It defines a textual scenario but does not prove the platform used that location as a device signal. Record target, declared and verified location separately.

How many points does a study need?

It depends on the area, barriers, density, decision and repetition cost. A nine-point pilot can test the protocol but does not guarantee representativeness or statistical precision.

How should I handle errors or unknown location?

Retain failures and location status. Exclude noncomparable observations from the relevant indicator, report coverage and distinguish insufficient data from a valid absence of recommendation.

Does a mention mean the branch is available?

No. Recommendation, published opening hours and availability for a task are different facts. Verify each condition with evidence and preserve unknown cases without turning them into confirmed availability.

Can I compare maps from two weeks?

Yes, when they share points, prompts, windows, surfaces and validity rules. If the design changes, version the grid and show the common panel separately from new observations.

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