Foot traffic data, sometimes called footfall data, is the number of consumers passing through and visiting specified areas such as retailers (retail stores) or shopping malls. The data indicates the number of people in the area during specified times, frequency of store visits, and the duration of stay. It provides traffic counts and traffic patterns such as the most popular times of the day (and days of the week) for consumer visits.
Using laser beam interruptions is a common tool to deliver the count of consumers entering and leaving stores. Thermal imaging sensors provide another method for obtaining basic data of the number of people in a specific area. Both the tools are simple, and the data they provide may not be accurate or complete.
More reliable tools for collecting data for foot traffic analysis are WiFi, Bluetooth, and GPS. At the micro-level of individual stores or shopping malls, WiFi and Bluetooth work the best. At the macro levels of large shopping areas, GPS location detection is more reliable if it gets updated in real time. GPS data provides information over a large area and cannot determine reliable information at a micro-level.
This type of data should have a minimum of two attributes: timestamped hours of operation and total hours of operation. More detailed data includes the following information about the Point of Interest (POI):
Testing the quality of the data involves testing the accuracy of the POI location data and the credibility of the sources. Foot traffic databases and datasets typically get updated regularly. Nonetheless, it is a good idea to get the timeliness of data verified.
To test the quality of the data:
Retailers use the data to optimize the store design and organize the working hours and staffing to manage the traffic. They also use the metrics to plan maintenance activities or staff breaks when the foot traffic is lower.
Comparing foot traffic data with actual sales can help to calculate the sales conversion rate. The insight into the sales conversion rate helps define the store performance and compare the top-performing stores with the underperforming ones. You can use this comparison to strategize boosting the sales in underperforming stores with additional advertising or promotions. This insight also drives the decision on store locations.
This type of data data drives marketing strategy and plays a significant role in deciding next steps of marketing efforts and promotional activities. The accuracy of the data is critical. A major challenge for foot traffic data is the completeness and recency of the data points. The challenges are outlined in detail below:
Foot traffic data is similar to consumer spending data and other related demographic data categories used in retail.
You can find a variety of examples of consumer and demographic data in the Explorium Data Gallery.
Sign-up for Explorium’s 14-day free trial to access the data available on the platform.
The most common use case for foot traffic data is in marketing campaign strategy. This data provides actionable insights on the number of visitors, which helps determine if the sales conversion rate meets the predicted rate. It also helps optimize resources in each store, hire temporary staff, schedule maintenance, or utilize low foot traffic periods for staff breaks.
Several diverse industries use foot traffic data to power foot traffic analytics, meeting challenges unique to their target consumers.
You can use foot traffic data to enrich other types of consumer demographic data for other marketing use cases in retail and financial services.
These insights help understand consumer preferences, optimize stores, strategize promotional campaigns, and strengthen marketing efforts.
Marketers leverage foot traffic data to help them build the marketing strategy and improve store performance. Industries that supply goods or services through retail stores include automotive, apparel, hospitality, QSR, CPG, pharmaceutical products, real estate, travel, entertainment, and telecom service providers among others. Banks, insurance providers, and financial services companies can also use this data to optimize their branch offices.
The quality of vendors providing foot traffic data is based on their ability to deliver a large volume of trusted data at scale. Some of the methods used for judging the vendor quality include customer testimonials and demos.