How Spatial gained household-level insights with InfutorData

When averages hide the audience that matters most

The quality of your consumer intelligence lives and dies on precision and accuracy, and that promise breaks down the moment your foundational data is no longer layered with identity resolution.

As a consumer intelligence and audience segmentation platform, Spatial’s models are built to help retailers, restaurants, and consumer brands find their highest-value customers. But before InfutorData came into the picture, that precision stopped at the block group. Spatial could describe what a neighborhood looked like, but not who actually lived in it.

Turning neighborhood data into household-level segmentation

Spatial’s answer was to integrate InfutorData’s household-level demographic data directly into its segmentation model, layering it against social, spending, and location signals to classify every U.S. household into one of 80 behavioral segments.

As Lyden Foust, CEO and Founder of Spatial, put it: “InfutorData allows us to differentiate one household from another. That’s the nuance no one else has — and that’s where the value lives.”

Why household-level precision matters beyond Spatial

Every brand is challenged by a similar issue: customers who matter most are often the ones neighborhood-level data can’t see. Sharper segmentation is what separates the brands that find their overlooked audiences from the ones still guessing at averages.

So what did household-level segmentation actually uncover for Spatial’s customers?