Local SEO Geo-Grid Tracking: How to Measure Maps Visibility

The method is useful because location matters in local results. Google currently describes local ranking as mainly based on relevance, distance and prominence. A business can therefore appear strongly near one point and more weakly a few miles away, even when the keyword and business have not changed.

Why one rank is not enough

Imagine a service-area business based near the centre of a town. You search its main service from the business premises and see a strong Maps result. That tells you something useful: the business has visibility from that location at that moment.

It does not tell you:

• how the business appears from the other side of town;
• how it appears from nearby villages;
• whether a competitor dominates one specific neighbourhood;
• whether visibility drops sharply with distance;
• whether a later change improved the whole area or only one point.

A manual search from one postcode is one observation. It is not a map of the local market.

Google does not publish a geo-grid score or an official grid-tracking system.

What a geo-grid actually does

A geo-grid tool sets a group of geographic points around an area and repeats a selected local search from those points. The tool records the observed position of the selected business or profile and displays the results on a map, commonly as numbered or colour-coded points.

A typical scan contains:

• a keyword;
• a selected business/profile;
• a centre point;
• a grid shape, such as 5x5 or 7x7;
• spacing or a radius that determines how far apart the points are;
• an observed local position at each point;
• a date/time and tool configuration.

Different vendors implement this in different ways. BrightLocal, for example, describes its Local Search Grid as map-based local ranking observations across multiple grid points and provides its own calculated summary metrics. Local Falcon similarly describes scan points that record the business's position at each geographic point.

Those vendor metrics are useful within their own systems, but they are tool methodology, not data exported from Google's internal ranking algorithm.

points and visualise the observations. The grid, colours and vendor scores are created by the tool.

Choose a keyword that answers a business question

A grid is only as useful as the query you choose.

Start with a commercially meaningful non-brand query that customers might genuinely use for the service.

Keep separate scans for distinct services when the search intent differs.

For example, a business may want to know:

• Where are we visible for our main service?
• Do we have any visibility in the town we have recently begun serving?
• Has a specific service improved across the area after a website/profile change?
• Are we strong near the base but weak at the edges?

Do not mix brand and non-brand interpretation.

A brand query answers something closer to "can people who already know this business find it?" A generic service query answers a discovery question. Both can be useful, but they are not interchangeable.

Choose the centre, size and spacing based on the question

There is no universal "best" geo-grid configuration.

A 5x5 grid is not inherently more correct than a 7x7 grid. One-mile spacing is not automatically right. The appropriate setup depends on what you are trying to measure.

Centre

The centre should be chosen deliberately. It might be:

• the business location;
• the centre of a town being studied;
• the centre of a customer catchment;
• a consistent reference point used for repeated scans.

If you move the centre between scans, you are no longer comparing the same measurement area.

Grid size

More points provide more geographic detail but also increase scan volume, cost and the amount of noise you need to interpret.

A small grid may be useful for a compact town centre. A larger grid may be useful for a wider service area, but only if the outer points still represent meaningful customer geography.

Spacing

Tighter spacing gives more resolution over a smaller area. Wider spacing covers more territory but can hide local variation between points.

The correct trade-off is resolution versus coverage, not "bigger is better".

Establish a baseline before you change anything

A geo-grid becomes much more useful when it can be compared with a documented baseline.

Record:

• date and approximate time;
• exact query;
• business/profile tracked;
• grid centre;
• dimensions, such as 5x5;
• spacing or radius;
• tool used;
• device/platform settings where relevant;
• any major website or profile changes that happened recently.

If you later alter the keyword, centre, size or spacing, note that the scans are no longer directly like-forlike.

A baseline is not proof of why the business ranks as it does. It is simply the starting measurement.

Read patterns, not one coloured square

The most useful information in a grid is usually the shape of visibility.

Strong centre, weaker edges

This is a common pattern and may be consistent with distance playing a role. It does not prove distance is the only cause.

Asymmetry

If visibility is strong north of the business but weak south, investigate the local competitive landscape, geography, relevance and the distribution of competitors rather than assuming the grid is "wrong".

Competitive pockets

Some grid points may show a different group of businesses competing strongly. This can help you identify where the local market changes.

Broad improvement or broad decline

If several points move in the same direction on repeated like-for-like scans, that is more interesting than one point changing once.

If a vendor provides an "average map rank", "share of local voice" or another summary score, label it accurately as that vendor's calculation. Do not write "Google gave us a score of 82" unless Google actually did - and geo-grid vendors are not Google.

Compare like for like over time

A useful follow-up scan uses the same:

• query;
• business/profile;
• centre;
• grid dimensions;
• spacing;
• tool;
• relevant configuration.

Where possible, avoid comparing a weekday baseline with a radically different setup months later and attributing every difference to your SEO work.

Look for persistent directional change rather than a single dramatic scan.

A practical record might be:

BASELINE: 1 September - 5x5 grid, one commercial query, centre fixed at agreed point.

CHANGE: Service page revised and profile category checked on 5 September.

FOLLOW-UP: Same grid repeated later.

OBSERVATION: Several points improved, some did not.

LIMITATION: Competitor activity and normal local result variation were not controlled.

INTERPRETATION: Visibility changed after the work, but the grid alone cannot establish which change caused the movement.

Correlation is not causation

Geo-grids are particularly tempting for before/after storytelling because the colours are visually persuasive.

Suppose you add a location page and the next scan contains more green points. You can say:

Visibility improved on the follow-up scan after the page was added.

You cannot automatically say:

The location page caused the geo-grid improvement.

Other possible contributors include:

• normal ranking variation;
• proximity effects;
• category or profile changes;
• reviews;
• links or wider prominence;
• competitor changes;
• website changes elsewhere;
• changes in how the tool collected the scan.

Google's local ranking framework is useful for forming hypotheses: relevance, distance and prominence are reasonable areas to investigate. It is not a published weighting formula.

For the underlying geographic effect, see Google Maps Proximity.

Geo-grids and geographic expansion

Geo-grids can help a service-area business decide where to investigate further.

For example, if a real business serves two nearby towns but the grid shows almost no visibility in one of

them, that can trigger questions:

• Is the business genuinely competitive and commercially active there?
• Does the website contain a useful page for that geographic intent?
• Is there first-hand local evidence from real jobs or customers?
• Are competitors materially stronger in that area?
• Is the town simply far enough away that proximity is a major constraint?

A grid can help prioritise investigation. It does not guarantee that creating a town page, adding a service area in the Business Profile or changing categories will "turn the grid green".

For the broader decision framework, use Geographic Expansion for Service-Area Businesses.

What a geo-grid cannot tell you

A geo-grid does not directly measure:

• organic website visibility;
• Search Console clicks or impressions;
• phone calls;
• form enquiries;
• quote quality;
• booked jobs;
• revenue;
• every personalisation or context variable involved in a real user's search.

That is why geo-grid data should sit inside a larger measurement stack.

Use Google Search Console for Local SEO for organic website data and Local SEO Leads and Conversions for business outcomes.

How often should you run a geo-grid?

There is no universal scan frequency. The cadence should match the decision you are trying to make and the amount of change taking place. Scanning several times a day can create a large amount of noisy data without improving the decision. Scanning only once a year may miss meaningful change.

For a stable small local business, a sensible approach is to capture a baseline before a meaningful change, allow enough time for the relevant systems to reprocess the change, and then repeat the same grid. For ongoing reporting, use a consistent cadence that the business can sustain and interpret.

If you change the cadence, record that too. The purpose is repeatable measurement, not maximising the number of scans.

An illustrative grid example

[ILLUSTRATIVE EXAMPLE - NOT REAL PROJECT DATA] A local service business runs a 5x5 grid for one non-brand service query.

• The centre points show positions 1-3.
• The western edge shows positions 4-8.
• The eastern edge is mostly outside the top 10.
• Two northern points are unexpectedly weak despite being physically close.

The wrong conclusion is: "We need more Location Pages east and north."

The better conclusion is: "The business has a strong core but uneven visibility. We should inspect the local competitors and relevance at the weak points, confirm distance patterns, and then decide whether any website, profile or authority change is justified."

The grid does not prescribe the fix. It describes where the problem appears.

Real Project: McKnight's Flat Pack Assembly

McKnight's Flat Pack Assembly is a real service-area business based in Ballyclare and is the live LetsBuzzMedia case study.

and spacing was supplied to this drafting environment. This page therefore does not create a colourful "real" grid or quote a project ranking score.

The appropriate Flat Pack measurement plan is:

1. choose one commercially meaningful non-brand furniture-assembly query;
2. define one geographic question, such as visibility across Ballyclare and selected nearby customer areas;
3. fix the grid centre and dimensions;
4. record the tool and spacing;
5. capture the baseline with the date;
6. log website/profile changes separately;
7. repeat the same scan later;
8. compare patterns without claiming causation from sequence alone.

When real scans exist, add them to the McKnight's Flat Pack Assembly case study with the exact configuration.

Key takeaway

Geo-grids are useful because local visibility is geographic. They turn multiple local rank observations into a map that can reveal a strong core, weak edges, asymmetry and competitive pockets.

But the grid is a third-party measurement system. Its colours and summary metrics are not Google scores.

The safest use is to keep the setup stable, compare repeated scans, form hypotheses carefully and connect Maps visibility to the rest of the measurement stack.

Next step

Define one commercial keyword and one geographic question. Capture a documented baseline grid and save the centre, size, spacing and tool settings. Do not change the setup before the next comparison.