Staff planning with footfall data

Staff planning: the roster usually lags the visitors

Most schedules are built from habit, gut feel and last year’s pattern, then copied forward. When the roster and the visitors drift apart, you pay twice: queues and thin service at the peaks, idle wage cost in the quiet hours. Sales data cannot close the gap on its own, because the till records transactions while the pressure on staff starts earlier, with people browsing, asking and queueing. The fix is to measure presence, when people are actually in the building, and plan from that.

Measure when people actually come

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 adds the where to the when, so a large venue can see which areas load up at which times. Standard metrics are ready the day after the visit, which keeps the picture current enough to plan next week on, and near-real-time crowd alerts are available where live occupancy matters. The full set of measures, and how they reach your scheduling workflow through dashboards and the API, is on data deliverables.

Cover the peaks, recover the quiet hours

A large Scandinavian retail chain working with Bumbee Labs re-planned its staff schedules around real peak hours, and adjusted opening hours after discovering more passers-by in the evening than early in the day. That is the typical shape of the finding: the data rarely says “more staff”, it says “staff at different times”. Peak coverage improves service exactly when it matters; quiet-hour precision recovers cost without customers noticing any difference.

From trend to roster

The practical loop is short. Pull the hourly pattern per weekday and look for the recurring peaks; set base staffing on the quiet floor and flex on the peaks; check the same pattern across seasons before fixing holiday rosters; then revisit monthly, because campaigns, weather and the calendar move the curve. Where opening hours are the question, passers-by outside current hours show the demand you are not yet open for. And across many sites, the same curves are comparable 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.

It works wherever people walk in

• Retail. Retail chains measure the same peaks in every store, so staffing follows demand locally while best practice spreads across the network.
• Hospitality. Guests touch many points in one visit. In hotels and hospitality, the lobby, bar, restaurant, spa and event spaces are each zoned, so staffing and service match real guest flow at every touchpoint.
• Public venues. Libraries use visits by hour, day, week and season to set opening hours and rosters that fit how the building is really used, evidence that also serves their funding conversations.

Anonymous by design

Measurement is passive: no app, no login, nothing asked of the visitor, and no individual is ever identified. Signals are anonymised and aggregated into statistics, and the result rests on the only footfall method in Europe approved by a data protection authority. That makes the same approach workable in a flagship store, a hotel and a public library alike, with privacy settled before the first roster changes.