Comparing Stores Fairly: Weather, Holidays and Campaigns

1. Compare like with like

Compare the same weekday and the same opening hours across weeks. Mixing a holiday with a normal day makes any comparison unreliable, and so does comparing a store that opens late on Sundays with one that opens all day.

Align your calendar. Mark public holidays, school vacations and local events so that they are visible next to the numbers. A simple calendar note is often enough to explain a surprising week.

Pay attention to changes in opening hours, renovations and temporary closures. They affect traffic directly and should be part of every comparison.

2. Account for weather and season

Rain, heat and cold change who walks outdoors and who stays home. Note unusual weather next to your weekly numbers so that a dip has an explanation. A very hot week can lower traffic in street stores while helping air-conditioned shopping centers.

Seasonality matters as well. Compare each week with the same week of the previous year when you have the data, not only with the previous week.

Do not overcorrect. The aim is to understand the main influences, not to build a perfect model of every factor.

3. Separate campaign effects

When a campaign starts, record the date and the stores it covers. Looking at entry rate, not only visitor numbers, helps show whether the campaign changed behavior at the door. Entry rate is the share of passers-by who enter, so it can reveal the effect of a window display or an offer even if traffic around the store stays the same.

Compare campaign stores with similar stores that did not run the campaign. This simple control group is often more convincing than looking at the campaign stores alone.

Check the period after the campaign as well. Some campaigns pull demand forward and leave a quiet period behind.

4. Use an index, not a single number

Comparing each store with its own earlier weeks, and with the average of similar stores, is fairer than ranking stores by raw counts. An index, for example the current week compared with a baseline, highlights real change over noise.

Group stores by type before comparing: flagship, high street, mall, outlet. A small store in a quiet town should not be judged against a flagship in a capital.

Keep the method stable. If you change how the index is calculated, say so clearly and recalculate earlier periods so the trend remains comparable.

5. Check data quality first

Before drawing conclusions, make sure counters were online and counting correctly during the period. Gaps and device issues can look like performance changes, and a sensor that was offline for two days will make a store look worse than it was.

Monitor device status regularly and mark known issues in your reports. A store with incomplete data should be flagged, not silently included in rankings.

Validate accuracy from time to time with manual counts. A short check at the entrance builds confidence in the numbers for everyone who uses them.

Fair comparisons build trust

When regional managers and store teams agree on how comparisons are made, discussions move from defending numbers to improving results. Trust in the data is what makes it useful.

A platform that keeps traffic, calendars and device status in one place, such as Counttrack 360, makes this easier because everyone looks at the same view.

If you would like to discuss how to set up fair store comparisons in your network, our team is happy to help.

Frequently asked questions

Why not rank stores by visitor count? Because visitor count depends on location, size, opening hours, weather and events as much as on store performance.

What is entry rate? It is the share of people passing a store who enter it. It helps measure the effect of windows and offers.

How many weeks of data do I need? At least four to eight weeks for a reliable baseline, and a full year if you want to account for seasonality.