Foot traffic data, also known as mobility data, can reveal consumer trends that are fundamental to strategic decisions in a variety of industries.
For example, measuring foot traffic to a grocery store can indicate when the store is most crowded and should be staffed accordingly. Similarly, comparing the mobility data of two competitor coffee shops can show which will be a stronger investment for a private equity firm.
There are many uses for and types of foot traffic data. This blog outlines the following to help you better understand what mobility data is and how best to use it:
Before we get into specifics of how you can use foot traffic data for yourself, we’ll cover what foot traffic data is and how it’s collected.
Foot traffic data associates people’s movements with physical places. There are a few different types of foot traffic datasets, each collected differently and with their own pros and cons. Mobility data can be collected via mobile devices, WiFi connections, sensors, and even manually. Some foot traffic data providers aggregate and anonymize the data to purely provide context around the volume and patterns of visits to specific places, while others deliver more personalized information.
One of the biggest struggles with footfall data is navigating privacy laws while still gaining access to enough information to produce meaningful insights. Anonymized data can be joined to demographics data such as age, income, voting patterns, and more, without compromising individual identities. Non-anonymized data connects information directly to individual people, and can contain personal information that makes its usage highly regulated.
Regardless of the type of foot traffic you choose, incorporating it into your analytics will give you deeper insight into who is going where, and when. Analyzing customer mobility patterns is essential for perfecting your retail store design, understanding customer engagement, digging deeper into customer demographics, optimizing your marketing efforts, and determining the best locations to set up new stores.
More generally, foot traffic datasets include metrics which answer questions such as:
How accurately you can answer these questions will depend on what type of foot traffic data you use, which depends on how that data was initially collected.
Foot traffic data can be collected many different ways, ranging from leveraging artificial intelligence (AI) to manually using a clicker counter every time someone enters a place. The proliferation of mobile devices has made foot traffic data collection easy and efficient, enabling data providers to deliver mobile ping locations as well as context around those visits to analysts across industries.
Foot traffic is most accurately collected from mobile devices. Anonymized and aggregated foot traffic data offers insights about where mobile device users travel with incredible accuracy while avoiding personally identifiable information. Mobility tracking data offers insights about users’ complete mobility patterns, offering not only movement into, out of, and within your retail stores, but all user movement that they’ve opted-in to. This gives you the ability to draw even deeper, and more meaningful insights from your analytics, and understand customer behavior with greater accuracy and detail. With anonymized tracking, customer data is safe, while offering you valuable aggregate data to leverage.
Because privacy is a key issue in the use of mobility data, foot traffic datasets made from AI and facial recognition technology are less popular. There are legal restrictions around using facial recognition solutions, and the potential use cases. This will require significant knowledge and navigation to be able to leverage facial recognition solutions.
Some organizations leverage foot traffic data collected from WiFi signals. Businesses can use their internal WiFi to gather foot traffic by allowing guests to connect. Ultimately, any time a user connects, your network will track this. This collection method is less accurate than deriving foot traffic from mobile devices because some people may not connect to WiFi (resulting in under-counting) or may automatically connect to your WiFi when passing by but not entering (resulting in over-counting).
Other common methods of collecting foot traffic data involve hardware such as sensors, pressure mats, video cameras, and clicker counters. These require more manual effort for the collector and also are generally less accurate than mobility data.
To help you learn how to make the most of mobility patterns data, we built a list of the top eight methods of collecting foot traffic data - check it out here.
Understanding if a device visited a place, brand, or type of store can be valuable context to have for your business. Companies use store visit information to build custom audiences for advertising purposes, to better attribute ad campaign spend, and to send contextual push-notifications in real-time. Unfortunately, accurately determining if a device visited a place can be a tough engineering problem to solve.
Dealing with messy GPS data, incomplete business listing information, and limitations in knowing exactly where places are located make visit attribution a complex problem. However, building a visit attribution solution remains a worthwhile endeavor since it enables you to enrich digital data with physical-world context.
Store visit attribution uses GPS location data from mobile phones with POI data to determine if a device visited a place, brand, or type of store. There are two main methods for attributing store visits, but the most accurate way is using precise POI polygons as geofences to truly see which mobile devices passed through a threshold.
The other popular method for store visit attribution is using a centroid radius as the polygon. While this can be easily done with any data point and basic geoprocessing tools, it often contributes to incorrectly attributed visits because a centroid radius is less precise than a building footprint polygon. As a result, GPS pings can be under or over-counted using this method of visit attribution.
For a technical breakdown of visit attribution and help deciding how to measure footfall, read our guide.
Analyzing foot traffic for competitor stores is just as important (if not more) as it is for your own locations. But many methods of attributing store visits require sensors physically at the location, or access to secure information, like the store’s WiFi records. Anonymized and aggregated foot traffic data can be procured for any location, regardless of whether it is your own or not. This is particularly useful for businesses looking to understand competitors and complementary locations.
Context - With latitude and longitude points that represent where people are going, and how long they spend there, you can begin to see trends and patterns. But those trends and patterns don’t mean anything without context around what is happening at a specific location. For example, seeing a cluster of GPS pings at (43.0568076,-77.6523542) isn’t necessarily useful, but understanding that they are at a McDonald’s off the New York State Thruway can indicate this is a popular stop for people on road trips.
Precision and Accuracy - While context is key to truly analyzing GPS pings, that context needs to be accurate. If the POI data used is stale or incorrect, you could be misinterpreting GPS pings. If the coordinates above are incorrectly labeled as a gas station, you may think there is an untapped opportunity to place a fast food restaurant, when in reality McDonald’s is already there.
Detailed Metadata - Spatial analysis, particularly with foot traffic data, is so advanced today that brand name alone is not enough for truly understanding what is happening at a location. Brand relationships, such as parent/child brands, are necessary attributes in any POI dataset so analysts can fully measure brand affinities and footprints. Similarly, spatial hierarchy metadata, like the sets provided in SafeGraph geometry data, provides crucial information about stores located within the same structures. This can make the difference between an incorrectly attributed GPS ping and an accurate representation of visits to that store.
Any organization that deals with consumers in some way can benefit from analyzing foot traffic data. Here are the top 3 use cases for mobility data:
Choosing where to open (or close) a location is an important operation for retailers, healthcare providers, and government agencies alike. With mobility data, any organization can analyze how people move throughout the day and how that can impact the success and accessibility of a new (or existing) location. To learn more check out our retail site selection guide.
To create a solid business strategy, organizations require details about who their target customers are, where they live and go, and how they can best be reached. Foot traffic data provides these insights, resulting in the most accurate method of trade area analysis that is grounded in actual human activity rather than predictions based on proximity alone.
Whether private equity firms are researching their next investment, performing due diligence, or managing their portfolio, they need reliable, up-to-date indicators of current and future business performance. Mobility data is often produced more frequently than other common inputs, such as datasets from the federal government, enabling private equity firms to update their research models more often for timelier results.
