Foot traffic data

Foot traffic data estimates how many people visit a place, when they visit and how long they stay. Retailers and real estate investors use it for site selection and performance benchmarking, investors as an early read on store sales, and landlords and city planners to understand how areas are used.

Typical fields:

• Place ID linked to a POI record or polygon
• Date or week, and hour of day
• Estimated visits and unique visitors
• Median dwell time
• Visitor home areas, aggregated to postal or census level
• Panel size and normalization factor
• Day-of-week and seasonality indicators

Sources and collection

Most foot traffic estimates come from location signals of a panel of mobile devices whose users consented through apps, combined with place polygons to detect visits. Other sources include on-site sensors, Wi-Fi counters and aggregated telecom data. Panel counts are scaled to the population, which introduces bias if the panel is not representative.

Delivery and pricing

Delivered as aggregated files by place and period through cloud storage or warehouse shares, or via dashboards and APIs. Pricing is usually an annual licence by number of places, brands or regions, plus history depth.

Privacy

Foot traffic data should be aggregated with minimum thresholds, in line with k-anonymity principles, so that no individual device or visit can be singled out. Precise location histories of identifiable people or devices are prohibited on DataStoa.

Before you buy

• Ask about panel size and how it changed over time.
• Check minimum counts and suppression rules for small places.
• Compare estimates with locations where you know actual counts.
• Review polygon accuracy, especially in malls and dense streets.
• Get the consent and aggregation process in writing.