Foot traffic data, or footfall, refers to the number of consumers passing through and visiting certain areas, such as malls or businesses. The data shows the number of people in an area during specific times, how often they came, and how long they stayed. The data also shows the most popular times and days people walk past or stay in an area.
There are many tools to collect footfall data for a certain store, mall, or area. Laser beams are a simple tool that is limited to a basic count and can often be misleading. Thermal imaging sensors are also a relatively basic tool for footfall analytics, though their bulbs burn out quickly and are expensive to replace.
The most common and reliable tool for the micro level (store or mall) is WiFi, which doesn’t even require a phone to connect to. Alternately, you could use Bluetooth.
On the macro level (shopping areas), GPS mobile location is useful, but it is not always updated in real time. It is also not accurate enough for the micro level.
Attributes of footfall data can include the number of visitors in a certain POI over a certain amount of time, mobile devices located at a POI, exact location, certainty and accuracy of location and demographic data about the location of your POI.
Footfall data directly helps improve store performance. By measuring local foot traffic data against your sales numbers, you can confirm the effect of foot traffic on sales volume, the percentage of shoppers who actually make purchases, which stores in a franchise are under performing, and whether recent advertisements have been effective. By establishing your lowest-performing hours, you can also set the best staff schedules.
To assess the quality of a dataset, consider location accuracy and detail, the scale and volume of the dataset, and the recency of the database updates.
You should use the same factors used to vet data providers in order to assess the quality of your data. For example, a dataset with accurate locations that updates infrequently or inaccurately is not completely accurate, and vice versa.
In 2018, the coffee brand recorded an average monthly foot traffic gain of 6.6% that expanded to 13.8% in 2019.
Foot traffic at Dunkin’ stores continued at an “exceptionally strong” growth rate of 9.2% in the first two months of 2020, he said.
Gastronomy Databases offers restaurant data for establishments all over the world. With more than fifteen years of experience in gastronomy, their data sets can be filtered by over 100 criteria.
Foursquare’s Visit Data provides visit data on over 100 million customers in the US, allowing you to run targeted marketing campaigns, develop products with your customers’ behavior in mind, and more. Once ordered, Foursquare can deliver the data in under 72 hours, in either a flat file or an API integration.
Attribution is the result of the partnership between the geospatial data vendor Factual and the location-based recommendation app Foursquare.
Atttribution measures the efficacy of advertisements—online, on TV, on the radio, etc.—by parsing out who visited your store as a result of exposure to your ad. Customer data includes demographic, geographic, census, operating system, and prior visit data.