A site-level prediction of customer arrivals for stores, restaurants, venues, and attractions, used to plan staffing, stock, and service levels.
The AI analyses your historical footfall data and combines it with external factors that influence visits, such as weather, school holidays, local events, and disruptions. It then tests multiple forecasting models for each site, selects the most accurate approach, and continuously refines forecasts as new data becomes available.
It provides a dependable view of traffic before it happens, so staffing, stock, and resources can be aligned to demand, covering peaks without chaos and cutting waste in lulls for steadier service and tighter cost control.
Accuracy can typically reach around 97% in mature deployments, when strong historical data and external signals are available. Accuracy is tracked per site and improves continuously with feedback from actuals.
We maintain the largest library of algorithms and evaluate hundreds of approaches per site; the system selects and re-tests the best-performing model as patterns change.
Our solution is technology agnostic and customer bespoke, per-site model selection (not one model for all), external signal enrichment by default, continuous learning from actuals, and delivery in formats your teams already use.
Footfall = predicted visitors; transactions = predicted purchases and revenue. Use both for the full picture, operations versus financials.
Footfall & Visitor Forecasting helps venues anticipate when visitor volumes are likely to approach or exceed safe capacity limits. By forecasting demand by time, area, and entry point, operators can plan interventions in advance, such as adjusting staffing and security, introducing premium pricing at peak times, encouraging visits during quieter periods through offers like happy hours, or redirecting demand to alternative zones within a complex venue. This enables venues to protect safety and compliance while maintaining control over customer experience and commercial outcomes.
