Why footfall is not sales, and what to put next to it

Footfall tells you how many people-hours are spent around a place; sales tell you how many of those people bought something from you. The two are linked but not the same, and they part ways for ordinary reasons: the crowd is passing through, it is the wrong crowd, it is there at the wrong hour, a rival catches it first, or your door is hard to find. Use footfall as the size of the opportunity, then put your own sales, the competition and the people who live nearby next to it before you decide anything.

This is for store managers, franchise and expansion teams, landlords and analysts who are tempted to rank sites or judge a branch by footfall alone. It lays out the gap, the figures that help explain it, a made-up example, the mistakes, and an AI employee that brings footfall and your sales into one weekly view.

Where footfall and sales part ways

• Presence, not visitors. Mobility Footfall counts people-hours: a commuter waiting 20 minutes for a bus adds a third of an hour; an office worker at her desk all day adds eight. Neither is a shopper by default.
• Passing through. A street between a station and offices can be packed at 08:30 with people who will not stop.
• The hour does not match. If the busiest hour is 08:00 and you open at 10:00, the crowd is gone.
• The wrong crowd. Students around a premium jeweller, or office workers around a toy shop.
• Someone else gets there first. Ten cafes on a busy corner share the same people.
• The site itself. A unit up a flight of stairs or hidden by scaffolding converts poorly whatever the street does.

The figures that close the gap

No single number replaces sales, but a few together explain much of the difference:

What to put next to footfall when you judge a site or a store.

Population is not available everywhere: it covers countries such as France, Canada, India, Thailand, Egypt and Australia, but not the United Kingdom or the United States. Footfall covers most of the world from 17 October 2026, though not yet Australia. Each day of footfall arrives about 10 days later.

How to compare footfall with your own sales

The useful number is a ratio you work out yourself: sales for a period divided by the footfall around the store over the same days. It is not a conversion rate (people-hours are not people), but it lets you compare your own stores fairly and follow each one over time.

1. Pick a period footfall has already arrived for, ending about 10 days ago.
2. Read footfall_daily for each store over exactly those days, with the same distance around each (200 m is a good start).
3. Take your sales for the same days from your own records.
4. Divide sales per day by footfall per day for each store, and rank the stores by it.
5. Look hardest at the extremes: busy streets with a low ratio and quiet streets with a high one.

A made-up example in Sao Paulo

Invented figures for illustration. A pharmacy chain compares four of its branches in Sao Paulo over the same 14 weekdays:

Invented figures for illustration; not real figures for Sao Paulo or any chain.

Station D has the most footfall and the weakest ratio: its busiest hour is 07:00, before the branch opens, and six rivals sit inside the station's reach. Residential C has the least footfall and the best ratio: one rival, regular local customers. Ranking by footfall alone would have put D first for investment and C last. The chain instead tests longer morning hours at D and looks for more residential corners like C.

Mistakes to avoid

• Calling the ratio a conversion rate. It divides sales by people-hours, not by people who walked in.
• Mixing periods. Footfall for October against sales for November tells you nothing. Use the same days.
• Different distances. A 500 m circle around one store and 200 m around another makes the ratios incomparable.
• Judging a new site by footfall alone. Add rivals, residents where they are measured, and the hours the street is busy.
• Ignoring the hour. Compare footfall_peak_hour with your opening hours before blaming the street.

Put it in front of you with an AI employee

1. Schedule: every Monday at 08:00.
2. My data with your stores and their last fortnight's sales (or Database query from your own database).
3. Mobility Footfall: 200 m, 14 days, with Footfall and Busy hours.
4. Nearby places around each store, set to count your kind of rival within 500 m.
5. Calculated field: the sales divided by footfall_daily, per 1,000 people-hours.
6. Sort and limit by that ratio, lowest first.
7. Email the list to the operations team.

What lands each Monday is every store with its footfall, busiest hour, rival count and ratio, the weakest first, with the dates covered. Make sure the sales you load cover the same days as the footfall window. For site decisions rather than store reviews, the templates where to open next and where to open my next cafe put competition and people next to each candidate.