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Step 1: Gather raw data

Unacast uses GPS location data to measure foot traffic because it is the most reliable. It works by sending signals, or “pings,” from mobile devices 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.

Step 2: Contextualize the data

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 understandable by adding context.

We make sure that private information, such as people’s exact home location, are obfuscated and not discernible to an address. We can add areas together to measure neighborhoods, cities, counties, states, and even an entire country.

Step 3: Create curated foot traffic datasets

We have datasets depending on your data maturity and needs: Foot Traffic Data, Dynamic Trade Areas Data, and Cross-Visitation Data. It’s tempting to think that once you add some context to the GPS pings on maps you’re done but there is more to it than data visualization. But there are literally an uncountable number of ways to make these location datasets say different things via catchment analysis, some of which are unhelpful.This is where the team of data scientists and business strategists at Unacast helps you “ask” the data just the right questions.