As of 13 August 2026, AI can only partly estimate footfall for a UK shop location.

What to actually do

1. Hand it to a person

   The route this page recommends

   A person who owns the outcome does this end to end, worth it when the failure is dear.

2. Use a tool built for this

   Second choice

3. Do it yourself

   The distant third

   A chat interface, power-user skill, and roughly 1 hour until you can act on the result.

   How to actually do it

   1. Open a map and the relevant local council, transport operator and town-centre pages, then gather the shop address, nearby stations, bus stops, car parks, pedestrian routes, landmarks and any published pedestrian-count evidence.
   2. Create a spreadsheet with separate columns for source, location, count, date, day type, time period, weather or event conditions and whether the count is measured or estimated.
   3. Add your proposed shop type, target customer, opening days and hours, trading area, expected opening date and the closest comparable shops or sites.
   4. Paste the spreadsheet contents and the links into the prompt, replacing each bracketed slot with your actual information.
   5. Ask the chatbot to produce the low, central and high footfall estimates, showing each calculation and keeping measured observations separate from assumptions.
   6. Open every cited source and compare its date, location and counting method with the proposed site, then remove any source or comparison that is not genuinely comparable.
   7. Ask a colleague with local property or retail knowledge to challenge the assumptions and record which missing counts should be collected in person before committing to the site.

   Prompt

   Estimate pedestrian footfall for this proposed UK shop location: [full address or postcode].
   
   Business type: [business type]
   Target customers: [customer description]
   Planned opening days and hours: [days and hours]
   Expected opening date: [date]
   Nearby landmarks, stations, bus stops, car parks and competing shops: [details]
   Available evidence, with links and dates: [paste data or links]
   Comparable shops or sites: [details]
   
   Use only the evidence I provide or sources you can identify clearly. Do not invent counts, sources or local facts. Separate observed data from assumptions. Produce:
   1. An estimated average daily footfall and a range, if the evidence supports one.
   2. Separate estimates for weekdays, Saturdays and Sundays where the evidence supports that split.
   3. The method and every calculation used.
   4. The strongest factors that could raise or lower footfall.
   5. A confidence rating tied to the quality and recency of the evidence.
   6. A list of missing data that would materially change the estimate.
   7. A sensitivity table showing how the result changes under low, central and high assumptions.
   
   Do not present this as a measured fact or a guarantee of sales. If the evidence is insufficient, say so and give a data collection plan instead. End with specific checks I should make against council, transport, landlord, local footfall and competitor sources before using the estimate in a site decision.

   Open it prefilled in ChatGPT or Claude, or copy it into Gemini, which takes no prefill link.