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Foot Traffic Analysis: Counting Guide & Worksheet

By Spotfic Team · Thu Feb 12 2026

Learn how to count foot traffic at a shop entrance, compare locations fairly and separate observed counts from estimates. Includes a printable worksheet.

The short answer

To assess foot traffic, count people passing the actual shop entrance at consistent times on weekdays and weekends. Record weather and unusual events. Nearby popularity, AI estimates and measured visits are different signals—not interchangeable counts.

A simple counting method you can use on your next visit

Choose a fixed point at the entrance and count each crossing consistently. Separate directions if they matter to access.
Repeat equal-length sessions at opening, lunch and evening times across several days. Record date, duration, weather and events.
Compare equivalent sessions between sites. Do not turn one busy quarter-hour into a daily sales forecast.

Illustrative arithmetic: 84 passers-by in 30 minutes is 168 per observed hour. It does not mean 1,680 people will pass in a ten-hour day. Repeat your observations before drawing a conclusion.

Open the printable foot traffic worksheet · Calculate your observed hourly foot traffic

What is foot traffic analysis?

Foot traffic analysis examines how people pass through or visit a place over time. For a prospective shop, distinguish people passing the entrance, people entering a business and people buying. These are different measures. A busy road, a popular neighbourhood or a nearby station does not establish how many customers your premises will receive.

Use the printable worksheet above to record your own observations. The aim is not to find a universal “good” count: it is to understand whether activity at this particular entrance fits your proposed business and trading hours.

Four ways to investigate foot traffic—and what each tells you

Manual counting: observes people at a defined point during the sessions you record. Low equipment cost, but limited by sampling and observer consistency. It does not identify purchase intent.
Door counters: record entrances or crossings at an operating premises. Check sensor placement, staff movements and repeat visits before interpreting the totals. They do not describe activity outside a vacant shop unless installed for that purpose.
Visitation analytics: providers model visits using their own datasets and methods. Ask about geographic coverage, sampling, observation dates and uncertainty. A modelled venue visit is not necessarily a pedestrian crossing your doorway.
Google Maps Popular Times and AI screening: useful contextual clues, not equivalent to a direct entrance count. Popular Times describes activity at listed businesses where available. Spotfic estimates patterns from nearby places and AI interpretation, rather than collecting visitor measurements.

For further background, see Shopify’s foot traffic guide and Placer.ai’s site selection guide. Compare their methods with the specific question you are trying to answer, rather than treating every traffic number as the same measure.

How to count at a prospective shop

Define the counting point. Use the actual frontage or entrance, not the busiest point somewhere along the road. Write down the counting rule so another observer can repeat it.
Choose comparable sessions. Include the periods when your business would trade, on multiple weekdays and at the weekend. More sampling helps identify variation but does not guarantee a representative yearly count.
Record the conditions. Note duration, weather, local events, school or office closures, roadworks and any obstruction to entry. Count in a safe public place and avoid collecting identifying information about passers-by.
Keep raw counts. Record each session before averaging anything. Averages can hide an unhelpful pattern—for example, all activity occurring outside your proposed service hours.

How to compare two locations fairly

Observe both sites during equivalent time windows with the same counting rule. If different people are counting, agree the method first. Compare the distribution of sessions, not just the single busiest observation. If Site A was counted during an event and Site B on an ordinary day, repeat the observations before ranking them.

A useful comparison also records entrance visibility, safe crossing points, steps, parking or transit access and nearby destinations. An office across a divided road may create less accessible demand than a smaller building on the same pedestrian route. Record what you observed and what remains an assumption.

Do not turn passers-by into guaranteed revenue

There is no universal conversion rate for all walk-in businesses. Price, format, opening hours, reputation, signage and the reason people are in the area can all affect whether they enter and buy. Do not apply an unsourced 2–5% conversion rate to an AI-estimated daily crowd and call the result expected sales.

If you build a planning scenario, label every assumption. For example: observed passers-by during a defined period × assumed share entering × assumed share purchasing × assumed average order value. This is scenario arithmetic, not a forecast. Test a range of assumptions and compare with evidence from a similar operating business where available.

Match the observation to your business

Cafe: observe the hours your format depends on, such as breakfast, lunch or evening visits. Look at nearby menus and prices as well as pedestrian activity.
Restaurant: check your intended service periods, how customers reach the door and whether they can see the entrance. Delivery access and kitchen suitability need separate checks.
Destination or appointment business: walk-by volume may be less important than accessibility, visibility to arriving customers and the ability to find the premises.
Retail: compare activity during your proposed opening hours and investigate whether people are shopping, commuting or simply passing through.

Red flags to investigate

The count relies on one event, one employer or one short observation.
Most activity is across a road barrier, on another floor or outside your opening hours.
The quoted number has no source, observation period or clear geographic boundary.
The rent calculation assumes every increase in foot traffic creates the same increase in sales.
The landlord’s visitor claim refers to a whole development rather than the unit you are considering.

Where Spotfic helps—and where it does not

Spotfic uses Google Places business listings and nearby places as inputs to AI analysis. Listing coverage varies by location. Foot traffic, rent, demand and demographic insights are estimates, not measured visitor counts, landlord quotes or a local census survey. Verify them on site before committing money.

Use the sample report to understand the output before signing up. An opportunity score can help organise your research questions; it is not an independently validated probability of success. A confidence label is not a measured accuracy percentage.

New accounts get one credit to try a report. The free preview includes overview and competition insights. Detailed foot traffic, financial, SWOT and launch-plan sections, plus PDF export, require a purchase.

What to do next

Take the counting worksheet to your shortlisted premises. Then combine the observations with a written rent quote and a restaurant site checklist, or review the cafe location guide. If you have a space but no business idea yet, explore ideas for your shop.

Can Spotfic measure the traffic outside a vacant shop?

No. It can estimate patterns from location context, but it does not measure people at the entrance. Visit the premises and record direct observations to validate your assumptions.

How many people per day make a good location?

There is no universal threshold. Consider your business format, relevant trading hours, access, costs and actual customer behaviour. More passers-by alone does not establish viability.

Is detailed foot traffic analysis free on Spotfic?

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Spotfic is an AI-powered location analysis tool for founders, franchise owners and expansion teams. Get competitor mapping, foot traffic, rent estimates, demographics and a Go/No-Go score for any address in under 60 seconds.

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