Site scoring — against your own best stores

You've got twelve stores open. Three are strong, four are average, five underperform — and nobody's ever put a number on why in a way that carries over to the next site decision. So when five candidate addresses come in, the process is: a broker sends a PDF of foot-traffic counts, and someone compares it by feel against a store they remember doing well. No shared baseline, and no record of how the call was made on the last deal.

One way it plays out

Your best stores become the yardstick — foot traffic for all twelve locations gets measured once, across the handful of things that actually matter (daytime population, drive-by volume, dwell time — whatever turns out to move the needle for your brand), and boiled down into one picture of what "performs well for us" looks like in numbers.
A candidate site gets scored against that yardstick — same measurement, same thresholds every time, with a plain Bad / Neutral / Good signal — not a different broker's gut call on each deal.
The reason comes in plain language — which factors drove the score, how the site compares to your actual top performers, and what to flag before you sign — so the site lands on your map already scored and already explained.

That's one way it plays out — the exact factors and thresholds get shaped around your brand and what your winners have in common.

So five candidates come back scored and ranked side by side, measured against your own best stores, instead of five separate PDFs you're comparing from memory.

Works for: site selection and location scoring, tenant/landlord fit, and any decision that compares a new option against a learned baseline rather than a fixed rule.

Everything stays on servers here in Canada, and once it's built the whole thing is yours to keep — not a subscription, and not tied to anyone's platform. I'm here in Winnipeg: I set it up, and I stay with you and your team until it's running the way you want.

Curious how it's built? Technical breakdown available on request.