Staff planning with footfall data

Why does the staff roster lag the visitors?

Staff planning often runs on habit. Many rosters are built from last year’s pattern and copied forward, even when visitors no longer follow it. The cost shows up in two places: queues and thin service at the peaks, and idle wage cost in the quiet hours.

Sales data cannot close the gap on its own. The till records transactions, but the pressure on staff starts earlier, with people browsing, asking and queueing. Demand-led scheduling measures when people are in the building and plans rosters around that demand.

What does staff planning need from footfall measurement?

Staff planning needs a dependable view of when people are in the building. Bumbee Labs counts visitors anonymously and turns the counts into peaks and lows by hour, day, week and season, the basis for staffing and planning. Zone-level data shows where as well as when, so a large venue can see which areas fill up at which times.

Standard metrics are ready the day after the visit, which is current enough to plan next week’s roster. Near real time is available via the Crowd Alert add-on. Where 3D sensors are in place, Xovis staff exclusion keeps employees out of the counts, so the demand curve you roster against contains only visitors.

Data deliverables lists the full set of measures and how they reach your planning tools through dashboards and the API.

Do your roster and opening hours match when visitors come?

Measured arrivals make a mismatch visible. A large Scandinavian retail chain working with Bumbee Labs re-planned its staff schedules around real peak hours. Retailers we measure have changed their opening hours after counting more passers-by in the evening than earlier in the day.

Where opening hours are the question, passers-by outside current hours show the demand you are not yet open for. The weekly rhythm of footfall looks at opening hours as a data question in more depth.

The change the data points to is often a shift of staff between hours, and less often extra headcount. Better coverage at the peaks improves service, and tighter staffing in the quiet hours can recover cost.

How does the trend become a roster?

The roster starts from the hourly pattern per weekday, which shows where the recurring peaks and the quiet floor sit. Campaigns, weather and the calendar move the curve, so the pattern needs revisiting.

1. Pull the hourly pattern per weekday and look for the recurring peaks.
2. Set base staffing on the quiet floor and flex on the peaks.
3. Check the same pattern across seasons before fixing holiday rosters.
4. Revisit it monthly.

Across many sites the curves compare like for like, so a roster pattern that works in one location can be tested against the demand curve of the next before it is copied.

Which venues can use footfall data for staff planning?

Any venue that people walk into can plan staff on footfall data. Retail chains, hotels and libraries build their rosters from the same kind of hourly curves, measured in their own buildings, so each plans on its own demand.

• Retail chains measure the same peaks in every store, so staffing follows demand locally.
• In hotels and hospitality, guests pass through many areas in one visit. The lobby, bar, restaurant, spa and event spaces are each zoned, so staffing and service match guest flow in every area.
• Libraries use visits by hour, day, week and season to set opening hours and rosters that fit how the building is really used. The same evidence supports their funding conversations.

Is staff planning with footfall data anonymous?

Yes, the measurement is passive and no individual is ever identified. Visitors don’t need an app or a login and are not asked to do anything. Signals are anonymised and aggregated into statistics, and the method works the same way in a flagship store, a hotel or a public library.

Bumbee Labs is the company behind the only footfall method in Europe approved by a data protection authority.