One chain, many stores, run them all on the same evidence

The job to be done

You operate many similar locations, and you’re judged on the performance of all of them. The hard questions repeat in every store: How many people pass, and how many come in? How long do they stay, and where? Which stores convert browsers into buyers, and why? Where are the dead zones and the bottlenecks? Is my staffing matched to when customers actually arrive? Did that campaign or window display do anything? Sales reports tell you what happened. They don’t tell you why, or what to do next.

Wi-Fi people counting across every store

Bumbee Labs turns the in-store Wi-Fi you already run into a measurement layer, and runs the same method in every location. In most stores that means no cameras and no new sensor hardware: the access points already in place do the job, with at most one or two added for a doorway or a passing-by frontage. Because the counting, the statistical engine and the delivery (a single dashboard, or straight into your own BI through a well-documented API) are identical store to store, the numbers are comparable like-for-like across the whole estate, and a strong store’s playbook travels through the network instead of staying local. All of it is anonymous, and it is the only footfall method in Europe approved by a data protection authority, so a chain-wide rollout never takes on privacy risk.

How Wi-Fi people counting works →

What data you get

The thing retail chains care about most is the conversion funnel, measured consistently in every store so you can compare like with like:

• Passers-by outside the store (the addressable audience).
• Visitors, how many actually enter (the outside-to-store capture rate).
• Dwell time, how long they stay, split into categories (bounces vs. real visits).
• Hot and cold zones, where in the store people linger, and where they don’t.
• Store-to-store benchmarking, the same metrics across every location, so best practice is visible and copyable.
• Trends and peaks, by hour, day, week and month, so staffing and opening hours follow demand.
• Integrations, connect the cashier/POS feed and you can layer average spend per visit and sales-per-minute on top.

How those zones, paths and dwell times come together on a single floor plan is shown on retail heatmaps.

→ Every metric and delivery format, in one place

Proof: a Scandinavian retail chain

A well-known Scandinavian retail chain, a large store network across the Nordics, selling food and drink, health and beauty, homewares and furniture, wanted to do more with less by understanding the customer journey in store. They chose Bumbee Labs for the data quality and the ethical, GDPR-clean approach. The only investment most stores needed was one or two extra access points. With the funnel and dwell-time data in hand, they:

• Adjusted opening hours after discovering more passers-by in the evening than early in the day.
• Re-planned staff schedules around real peak hours.
• Used hot zones for campaign messaging, and redesigned the main path in some stores.
• Benchmarked stores to spread best practice on product placement, staffing and layout, cutting marketing costs and lifting profitability.

Read the retail chain results →

Where this fits

If your network includes forecourts or roadside stores, see petrol & convenience retail analytics. Multi-site hospitality operators face the same “many similar locations” problem and use the same approach.