Understanding Footfall Patterns to Optimise Operations
Measurable improvements within three months
A multi-site leisure centre operator with 64 locations across the UK wanted to better understand visitor patterns to optimise staffing, class schedules, and facility maintenance. Without reliable footfall data, they were making decisions based on guesswork and anecdotal feedback from front-desk staff.
Data Jam's JamBox sensors were installed across all 64 sites, providing real-time footfall analytics without any cameras or personal data collection. Within the first month, clear patterns emerged that transformed their operational approach.
The data revealed a consistent pattern across all sites: footfall starts building from 5am (early morning swimmers and gym-goers), steadily increases through the morning, then peaks dramatically at lunchtime between 12:00-13:00. This lunchtime surge was 23% higher than any other hour — a pattern that had been underestimated by staff.
After 1pm, footfall gradually declines through the afternoon, with a smaller secondary peak around 5-6pm for post-work exercise, before dropping off sharply after 8pm.
Shifted peak staffing to 11am-2pm instead of the assumed 5-7pm slot. Staff now match actual demand, not assumptions.
Launched 30-minute 'Lunch Express' classes targeting the midday rush — quick workouts for time-pressed members.
Scheduled equipment maintenance during the quieter 8-10am window, minimising member disruption.
Introduced discounted off-peak pricing for 3-5pm to smooth demand and attract price-sensitive members.
Optimised cleaning rotations based on actual quiet periods rather than arbitrary schedules.
Used top-site data (32,000+ daily) to plan pool lane allocation and gym floor capacity during peak hours.
"We thought we knew our busiest times — we were wrong. The data showed us patterns we had completely missed, especially the lunchtime surge. Now we staff smarter, not harder."
JamBox delivers the same insights for your facility. No cameras, no hassle, just data.
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