Understand foot traffic activity at a specific site or area including the who, when, frequency, and length of a visit.
Foot traffic data can inform a variety of use cases, including pinpointing new store locations, understanding store performance, and staying ahead of your competitors. Our machine-learning powered foot traffic data is the industry's most accurate solution for understanding visitation to an area or point of interest. When validated against ground truth data, Unacast’s models recorded an R-Squared of 91.6% or higher, widely considered to be best-in-class.
Customers leverage our foot traffic data for key business criteria including:
Understanding your target audience and their lifestyles in an area can help you adjust store layout, inventory, and marketing.
Understanding patterns in your customers' visits on a hourly, daily, weekly, and monthly basis.
When expanding your number of stores, knowing if your new location is attracting enough people and the right type of audience will help you make the right choices.
Understand if your target audience frequents your competitors more than your store and their pattern of visits.
Benchmark your stores to the industry to understand how individual stores within a brand are performing relative to the industry category and other competitors.
Many people turn to foot traffic data to help make better and more sustainable decisions, from creating a data-driven marketing campaign to applying location data in understanding mobility patterns.
For investors, retailers and urban planners alike, mobility data from mobile devices is the key to making better real time decisions.
Want to know more about foot traffic data and how to use it? Schedule a meeting with us.
Try out one of our datasets! Download a sample below, or get in touch with us if your team needs something specific.
We help you understand people’s movements in the physical world using accurate foot traffic data.
How many people visit your store?
Percentage of people in the area who end up visiting your store.
Patterns for when people visit your store (hourly, daily, weekly, monthly).
How much time do people spend at your venue?
Do your customers return to you every month or go elsewhere?
Identify visitors by age, gender, income group, education, and race.