The morning and evening peaks around offices, and how to sell into them

Around offices, footfall has a recognisable shape: it climbs as people arrive in the morning, often reaches its highest point around lunch, holds through the afternoon and falls away in the evening, with weekends far quieter than weekdays. In footfall data you see this as a large morning and midday share, a small late-night share, a busiest day between Monday and Friday and a quietest day at the weekend. If a district shows that shape, the people around it are mostly there to work, and anything you sell has to fit their working day.

This piece is for businesses that live on office workers: coffee and lunch places, caterers, gyms selling early and lunchtime classes, dry cleaners, phone repair kiosks, and the sales teams who sell corporate memberships or services to the companies in those buildings. It covers how to recognise an office pattern, what the data can and cannot show about rush hours, an example from Nairobi and Frankfurt, and a weekly AI employee that keeps the picture current.

Recognising an office district in the figures

Mobility Footfall returns, for each place, the busiest hour of the day in local time, the average footfall in that hour, and how the whole day splits between four parts: morning, midday, evening and late night. Three readings together point to offices:

• The morning and midday shares together are well over half of the day's footfall.
• The late-night share is small. Few people live there, so few are present overnight.
• The busiest day is a weekday and the quietest is a weekend day. Running the block once with weekdays only and once with weekends only makes the drop plain; the method is in weekday and weekend footfall.

A mixed district, with flats above offices, looks different: a large evening share and a solid late-night share alongside the weekday daytime. That matters, because a mixed district keeps trading after six and at weekends while a pure office district does not.

An example: two districts, two working days

A made-up comparison. A healthy-lunch brand runs kitchens in Nairobi and Frankfurt and wants to know where a breakfast menu would earn its keep. It measures 30 days of weekday footfall within 200 m of a site in each business district. Suppose it sees this (invented for illustration):

Invented figures to show how the fields read; not real figures for either city.

Both are clearly office districts: tiny late-night shares, quiet Sundays. But the Nairobi site is busiest at eight in the morning with the largest morning share, which suggests an early start and people arriving well before their desks fill: a breakfast menu has a real audience. In Frankfurt the weight sits at lunch; a breakfast push there would chase a thinner crowd, and the money is in the lunch hour and the hour after.

Neither conclusion is proven by the figures. They tell the brand where to test first and what to test.

Timing an offer to the working day

• Opening time. If the morning share is large, open before it, not halfway through it. A shop that opens at nine in a district busiest at eight has missed the first wave.
• Staffing. Put the most people on the till in the part of the day with the largest share; see matching staff rotas to footfall.
• Promotions. A late-afternoon offer in a pure office district reaches people about to leave; an evening offer reaches almost nobody.
• Weekends. In districts that empty on Saturday and Sunday, shorter weekend hours or closing outright can be the right call.
• Selling to the companies. Gyms and caterers selling corporate packages can use the district's pattern as an argument: classes at the hours their staff are already nearby.

Mistakes to avoid

• Reading a midday peak as a lunch crowd for you. It tells you people are present, not that they eat out. Check the places already serving them with Nearby places.
• Using all days. Weekends drag the average down and blur the weekday shape. For office questions, measure weekdays on their own.
• Too wide a circle. At 500 m a business district can swallow a residential street next to it and change the shares. Keep to 200 m around each building or corner.
• Holiday weeks. A window that includes a public holiday looks quieter on weekdays than usual. Footfall covers days that ended about ten days ago, so check the dates in footfall_from and footfall_to.
• Expecting it in Australia. Footfall does not cover Australia yet; offices in Sydney or Melbourne will say no footfall data here.

A weekly office-district check with an AI employee

1. Start with Schedule, every Monday morning.
2. Bring in your sites or prospects: My data with your kitchens or clubs, or Nearby places searching office buildings in your district.
3. Add Mobility Footfall: weekdays only, Footfall and Busy hours, 200 m, the last 30 days.
4. Add Filter to keep places where footfall_daily is above a level you choose, so quiet side streets drop out.
5. Add xMap LLM asked to label each place as early-start, lunch-led or mixed, and to suggest an opening time and the hour for a promotion.
6. Send by Email. In the inbox each Monday: every site with its busiest hour, four shares, label and suggested hours.

For sales teams selling into the buildings themselves, follow the same flow with Apollo to find who runs facilities or people at each company, and send the list to your sheet instead.