Market scan
Find the best area, not the best address. Returns a scored surface across a whole region that you drill into, again and again, until you have a site.
What it weighs most
Qualified demand
30% of the score. The rest is on this page.
01 / The problem
You do not have a site yet. You have a region and a hunch, and the honest answer to where you should look is somewhere in a hundred square miles that nobody has time to search by hand.
02 / The decisions it serves
Not a report. A decision somebody has to sign.
Each of these is a question your team already asks, and currently answers with a spreadsheet, a phone call and a fortnight.
Where in this region should we even look
The whole area scored as a grid, ranked, rather than three candidates somebody already had in mind.
Which areas rank, and why
Demand, momentum, behaviour and competition, weighted for what you are actually building.
Where to drill next
Metro, then city, then district, then neighbourhood, until you have a street rather than a shape.
03 / The method, in the open
Here is exactly what it weighs. Argue with it.
Most tools will not show you this, because most tools do not have it. These are the weights the playbook really uses.
Qualified demand
Who is actually there. Weighted per use case
Momentum
Whether it is growing or draining
Behaviour
Spending and disposable income
Amplifiers
Daytime population and what draws it
04 / It is willing to say no
It does not hand you numbers. It gives you a verdict.
An assistant that only ever agrees with you is a mirror, not an analyst. This one holds a line, names the thing that kills the deal, and will tell you to walk away from a site you already like.
The thresholds it holds you to
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The weights above are the baseline. Each use case reweights the four demand factors, and they always sum to 100.
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Competition enters as a ratio against the demand, not as a factor to be traded off.
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Every area comes back with an opportunity index from 0 to 100, so the ranking is legible.
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It starts at metro scale and drills: metro, then city, then district, then neighbourhood. Drive-time catchments only fire once you are below district scale, because at metro scale they cost credits and tell you nothing.
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It will not evaluate a single site. That is a different question, and site analysis is the playbook for it.
05 / Why not just ask an AI
A fluent answer and a defensible one are not the same thing.
Every model will answer a location question now, and most of the answers sound right. Ask twice and you get two of them. Ask where the number came from and the room goes quiet.
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It scores an entire metro as a grid and lets you drill into it. That is spatial compute at a scale a chat window structurally cannot do, however good the model behind it.
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It names the places it finds and verifies where they actually are, rather than inventing a plausible neighbourhood.
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It refuses to evaluate a single site. That is a different question, and it will tell you to use a different playbook rather than answering the wrong one.
06 / What it runs on, and what you get
The data underneath
Licensed and authoritative, and named in the output, so the number keeps its source when it travels.
- Esri GeoEnrichment
- Census
- Uber H3 grid
What you walk out with
A ranked, scored opportunity surface you can drill into, ending in a named area and a recommended next move rather than a heat map you have to interpret.
The method it used and the vintage of every number travel with it, so it still holds up when somebody asks you why in six months.
How an answer travels07 / It pairs with
Nobody makes one decision in isolation. Most teams run this alongside two or three of these.
Run Market scan on something you already decided. See if it agrees with you.
The honest test of a method is whether it reaches the conclusion your best analyst already reached, and tells you plainly when it does not.
Twenty minutes, and you can argue with the weights.