Our Data

Validate your next business decision with our data-informed human mobility insights

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A 3d visualization of migration patterns map

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Building blocks for our datasets

We create datasets of foot traffic, trade areas, and the cross visitation to help you solve your biggest business problem

A 3D visualization of foot traffic map

Understand visits in and around a location with time

Our foot-Traffic dataset helps you understand the visitation at a specific site or area – how many people visit, type of visitor, and traits of the visit (when people visit, how long they stay and how often they return)

  • Number of people visiting a location
  • Time people spend at a location
  • Capture rate (how many people were in close proximity of your store vs people who actually visited a store)
  • Customer loyalty (do customers return every month)
A 3D visualization of the Trade Area

Understand your key trade areas

Make better decisions by understanding home and work locations of your visitors.

  • Changes to your trade areas over time
  • Distance your customers travel to your location
  • Where your customers live and work
Products Shopper Journey: A 3D map showing a shopper's journey through lines between businesses

Understand what other venues customers visit

Identify what shops your customers also visit to learn about their shopping preferences

  • Identify other locations your customers visit
  • Understand patterns in the locations customers visit
  • Find cross-merchandising opportunities by knowing which other shops your customers visit
Our Process

How we build our datasets

Our datasets are built with a privacy-first mindset to give you peace of mind as you solve your biggest business problems.
Step 1

Gather raw data

Unacast works with partners to gather raw GPS data, from +130M smartphones and mobile apps.

We use GPS location data because it is the most reliable. It works by sending signals, or “pings,” from your mobile device to a constellation of satellites. GPS uses triangulation to determine where on the planet your device is, and describes that position using latitude and longitude. Each ping from a device also has a timestamp.

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Step 2


The raw pings are clustered by our algorithms into events that indicate activity, such as dwelling at a location, or traveling and assigning retail venues and brands.

An array of latitudes and longitudes are of no use. That ́s why Unacast's data engine translates the raw data feed into something we can understand by adding context. In addition we assign events to census block group (CBG). While this is not the finest-grain map out there, we want to make sure that private information, such as people’s homes, are obfuscated and not discernible to an address. We can add CBGs together to measure neighborhoods, cities, counties, states, and even the entire country.

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Step 3

Create curated datasets

We have datasets depending on your data maturity and needs: Foot Traffic, Dynamic Trade Areas, and Cross Visitation.

It’s tempting to think that once you add some context to the GPS pings on maps you’re done. There are literally an uncountable number of ways to make these datasets say different things, some of which are esoteric, and others that are downright conflicting. This is where the team of data scientists and business strategists at Unacast helps you “ask” the data just the right questions.

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Schedule a Meeting

Meet with us and put Unacast’s data to the test.