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New Version

This is the documentation for the latest version of our Machine Learning Visitation Product. If you have an existing integration all details might not align with what you are receiving.

Unacast's Machine Learning Visitation Product is your solution for unlocking the power of physical location intelligence. Our advanced machine learning models combine multiple data sources such as historical foot-traffic, area profile, demographic context, or privacy-friendly first party GPS data to estimate visitation at different locations. With this data, you can maximize marketing and advertising strategies, benchmark your brick and mortar stores against competitors, make informed site selection decisions, investigate back-to-office behavior, and improve location performance.

About Unacast’s Machine Learning Visitation

Our product includes these datasets:

  • Foot Traffic: estimated visitation to your location
  • Trade Area: estimated home and work locations of your visitors
  • Demographics: estimated demographic breakdown of your visitors

Our datasets can be applied to the following locations:

Datasets/LocationsPoint Of Interests (POIs)Census Block Groups (CBGs)ZIP Code Tabulation Areas (ZCTAs)Custom Locations
Foot Traffic✔️✔️✔️
Trade Areas✔️✔️✔️✔️

These datasets come with the following cadences:

Datasets/CadenceWeeklyMonthlyQuarterlyRolling Monthly (3 Month observation period)
Foot Traffic✔️✔️✔️
Trade Areas✔️✔️

Rolling Monthly

Rolling monthly data is a combination of monthly and quarterly data. It is delivered on a monthly cadence, but covers a rolling window of the prior three month period. For example, data for January would cover the period of 01-Nov through 31-Jan. This cadence is ideal for customers who want to see monthly updating data for metrics that require longer aggregation windows such as trade areas.