Run every store in the chain on the same evidence

Why is store 14 the weakest in the chain?

Put every store in the chain on the same funnel of passers-by, entries and dwell, and the weakest one shows where it loses people. A low capture rate points to the street and the frontage, while healthy capture with weak conversion points to the floor or the till. A sales report alone cannot separate the two.

A large Scandinavian retail chain measured every store the same way and used the results to cut its marketing costs, schedule staff on real peaks and remove dead zones from its floors. The full story is in Retail Chain Runs One Playbook.

Can we compare a big city store with a small suburban one?

Yes. Compare each store with its own baseline. Express every store as an index, weighted for floor area and location, and a large city store and a small suburban one sit on the same scale for the regional manager. Benchmarking footfall explains why an index beats raw visitor counts.

The same campaign ran in every store: where did it work?

Set each store’s visits and dwell in the campaign weeks against its own ordinary weeks, and the stores where the campaign lifted footfall stand apart from the rest. Footfall KPIs give the lift store by store. Conversion per campaign also needs the till data, and you can measure event impact the same way for any campaign.

Are the opening hours right for the whole chain?

Count the passers-by outside each store by hour with Wi-Fi people counting and set them against its opening hours. The chain then sees where the street is busy before the doors open or after they close. Retailers we measure have adjusted their hours after finding more passers-by in the evening than early in the day.

We are testing in three stores: how do we roll out to the rest?

Run the pilot on the Wi-Fi already in the stores, using the definitions you will want chain-wide, and the rollout then repeats the same method. Add sensors only at the doors that need exact counts, as in hybrid people counting, and bring in sales data through an integration. The privacy review happens once for the whole chain.

Can we get the footfall data into our own BI?

Yes. The chain’s counts can go to one dashboard or straight into your own BI through a well-documented API. Anything that can call an API can read the data, Power BI and Tableau included, without a native connector, so each region can build its reports in the tool it already uses.

Can we put till data on top of the store visits?

Yes. Each store’s transactions come in from its own till system through an integration and sit beside its visit counts. Transactions divided by visitors give conversion, store by store and hour by hour, and in-store conversion rate shows the formula. Without till data, the count stops at visits.

Different hardware in different stores: are the figures comparable?

Only when every store follows the same definitions, because figures from different counting methods are not comparable by default. The surest route is one method in every store, which is how Bumbee Labs runs a chain. How to count visitors compares what each method measures, so you can see which stores differ and why.