Learn how foot traffic data, shopper demographics data, & POI data help chains & retailers with trade area analysis, store site selection, & retail analytics.
Foot traffic, also known as footfall, is a term used to describe pedestrian visitors to a business or commercial site. More generally, foot-traffic datasets include metrics which answer questions such as:
- How many people visit this place on a daily or monthly basis?
- How long do visitors stay at this place (dwell time)?
- What times of day do people visit this place?
- How many people walk past the establishment vs. into the establishment?
To collect footfall data accurately, retailers or commercial buildings landlords use a technology called people counting or footfall counting. Some example types of foot-fall counter technologies include:
- Thermal People counters
- Time of flight people counters
- CCTV people counters
- Wi-Fi people counters
- Infrared people counters
One of the most popular hardware people counter devices is the Wi-Fi people counter, where a retailer can use the data behind its Wireless Access Points (WPA) to measure dwell time or the time spent in a particular place.
The above footfall counting methods require a device to be installed physically in a store so it's not possible to get exact foot traffic counts for competitor stores.
One method to get an approximate foot traffic count is by analyzing an anonymous mobile location data panel and using detected visits as a proxy for foot-traffic. Many retail analytics & consulting firms rely on SafeGraph’s foot-traffic data which is based on this method.
While not as accurate as a physical device placed on-site, insights from mobile location data can be gathered for all stores: even competitors stores and nearby stores. SafeGraph's foot-traffic data & retail insights covers 4.4 million places in the U.S.
Retail foot-traffic data is often used for trade area analysis and site selection.
Trade area analysis is the process of mapping where customers live, work, and shop in relation to the location of a commercial site.
Trade area analysis helps retailers and commercial property developers understand where shoppers are coming from and what the average drive-times for those customers are to reach your site. To better understand customers, trade area analysis often incorporates demographics data (census attributes like age, income, and marital status).
Trade area analysis helps businesses with retail site-selection (the process of finding where to open, close, or move a store) and real-estate asset management. When done right, this helps improve financial returns for businesses.
Of course, trade area analysis is only worthwhile if done with accurate data. No amount of post-processing or clever modeling can save your analysis if the underlying data isn’t good.
Commercial building landlords and retailers combine foot-traffic data with trade area analysis to inform site-selection & improve real-estate asset management.
Knowing the actual amount of visitors to your area and neighboring stores, rather than an estimated amount based on drive-times and local population counts, helps trade area analysis be more accurate.
This allows one to identify sites that may seem to be less-frequented based on population counts and conventional methods but in actuality receive a lot of visitors.
Points-of-Interest (POI) data is another ingredient used along with foot-traffic data in live trade area analysis for site-selection. Having accurate POI data helps you avoid opening up stores too close to competitor brands or similar types of places (based on POI category).
Points-of-Interest data also helps deal with regulatory & zoning constraints. One way site-selection companies are already using SafeGraph’s POI data is for making sure cannabis retail stores aren’t opened up too close to schools (NAICS code 611110).
In order to avoid this problem, it’s crucial to have accurate data on where all POI are located and what their NAICS categorizations are. That’s why a big focus of SafeGraph’s data science team is having high fill rates for POI categories & subcategories (NAICS code fill rate is currently at 90%).
Two other important columns in SafeGraph’s foot-traffic patterns dataset for site-selection are the median dwell time (measured in minutes) and the bucketed dwell times, which measure the number of visitors who dwelled at a place for a particular range of minutes.
This gives additional context to the foot-traffic data, by providing insight into whether the visitors to a location are just stopping by, hanging around for a while, or in it for the long haul.
Another feature of the SafeGraph dataset is store visitors by the hour. This gives live-trade-area analysis a time component, which is crucial for businesses which aren’t open all hours, like breakfast food restaurants or nightlife destinations.
Census Data is often a part of the live trade area analysis and site-selection process. Typically, demographics data from neighborhoods surrounding a candidate site are considered to make sure the population growth, employment rates, and income levels can support the business being opened.
One way in which companies are better using Census data is by combining it with SafeGraph’s foot-traffic patterns dataset, which has a column named ‘visitor home cbgs’ and ‘visitor work cbgs’. These columns list the home and workplace Census Block Groups (CBGs) that sent the most visitors to a Point-of-Interest.
By joining the top CBG data with Census demographics data, you can get a more accurate understanding of the income level, education level, and marital status of the visitors who frequent particular Points-of-Interest in a trade area.
Having this extra context is crucial in areas where the visitor population is significantly different than the population surrounding the site. Areas where this phenomenon tends to occur are tourist spots and neighborhoods which attract many commuters (ex. Midtown Manhattan).