How to find the busiest hour and day around your shop

To find the busiest hour and day around a shop, look at how many people are present in the streets around it, hour by hour, over a few weeks, and average it. Mobility Footfall does this for each place you give it: it returns the busiest hour of the day in the shop’s own local time, how the day splits into morning, midday, evening and late night, and the busiest and quietest day of the week.

This article is for shop owners and area managers who want to set opening hours, time a promotion or book deliveries away from the rush. It explains what each figure means, works through a made-up example, and lists the traps that lead people to the wrong hour.

What the figures describe

The block measures presence, not purchases. It counts people-hours: one person who stays in the area for two hours adds two, and a passer-by who crosses it in ten minutes adds a sixth of one. The figures are scaled from a panel of devices to the whole population, and only totals ever leave xMap, never a person or a device.

Three settings decide what you get back. Around each place is the circle, from 50 to 500 metres; up to 200 metres it is the shop’s own small cell of the map (an H3 hexagon of about 0.1 km²). Time window is the last 7, 14, 30 or 90 days. Days is all days, weekdays only or weekends only. Under Return, tick Busy hours and Busiest days.

Fields Mobility Footfall adds when Busy hours and Busiest days are ticked.

A worked example: two shops, two shapes of day

Here is a made-up example. A bubble tea shop on a side street in Bangkok and a hardware store on a commercial road in Chicago each run the block with a 200 metre circle and the last 30 days.

Made-up figures, to show how to read the fields.

The two streets have different lives. Around the tea shop, the evening and late night hold almost two thirds of the day, so a shop that closes at 19:00 misses most of its street’s crowd. Around the hardware store, the morning is the heavy part and the evening tails off, so staying open until 21:00 serves a street that has mostly gone home.

Both share a busiest day, but not the quietest. The Chicago street empties on Sunday, which makes Sunday a good day for a stock count. The Bangkok street is quietest on Monday, which is where a staff day off hurts least.

Turning the figures into decisions

• Opening hours. Compare your hours with footfall_by_part. If a fifth of the day falls after you close, test a later closing one or two nights a week before changing it for good.
• Promotions. A happy hour works best just before the peak, when people are arriving, not at the peak when the shop is already full.
• Deliveries and restocking. Book them in the quietest part of the quietest day, when the pavement and loading bay are clear.
• Window displays and signs. Change them before the busiest day, so the most people see the new one first.

Mistakes to avoid

• Reading people-hours as customers. A street full of commuters can be busy and still sell little. Footfall tells you when people are there, your sales tell you whether they buy.
• Using a circle that is too wide. At 500 metres the circle can take in a station or a school whose rush is not yours. Keep it to the shop’s own cell unless the shop draws from a wider area.
• Judging from a week. One holiday or one rainy weekend can move the busiest day. Thirty days smooths that out; check the dates in footfall_from and footfall_to.
• Forgetting the delay. Each day’s figures arrive about ten days later, so the last 30 days end about ten days ago (see why footfall data arrives days later).

Let an AI employee keep it up to date

The shape of a street changes with the seasons, so read it again each month rather than once. A small flow does it for every shop you have:

1. Start with a Schedule block set to the first Monday of the month.
2. Add My data with a table of your shops and their addresses or positions.
3. Add Mobility Footfall with 200 metres, the last 30 days, and Busy hours and Busiest days ticked.
4. Add xMap LLM with the instruction: for each shop, suggest opening hours and the best hour for a promotion from the peak hour and the split of the day.
5. End with an Email to the area managers, or a row per shop in Google Sheets.

What lands in the inbox is a short list: each shop with its busiest hour, how the day splits, its busiest and quietest day, and one or two suggestions written from those figures. A shop with no data yet says so instead of showing zero.