How VideoAmp turned fractional data into verified consumer profiles

Identity resolution powers better media measurement

Every media platform runs on the same promise: know your audience, prove your impact. But that promise falls apart when the underlying, foundational identity data is not usable across marketing and measurement systems.

That’s the challenge VideoAmp set out to solve, and it comes down to adopting identity resolution capabilities.

As a media performance platform built to replace legacy measurement systems, VideoAmp needed something legacy systems never delivered: a single, accurate view of the consumer across linear TV, streaming, mobile, and web. Instead, their platform was ingesting a flood of signals that were fractional, anonymous, or simply incompatible across partners: unknown consumers, inflated reach numbers, and IP addresses that shift and get shared across devices.

The result? A consumer view too unstable to build trustworthy measurement on.

Fixing identity at the source

VideoAmp’s answer was identity resolution at scale: integrating two InfutorData products, Total Consumer Insights (TCI) and TCI MAID, directly into VALID™, their multi-source identity graph. TCI brought 300+ consumer attributes into play, enriching anonymous signals with verified profiles. TCI MAID added device-level identifiers tied to confidence scores and crosswalk tables, so identities could reconcile cleanly across partner ecosystems.

As Gina Ive, Senior Director of Partnerships at VideoAmp, put it plainly: bringing in identifiers without resolving them to a real person or household is just an assumption dressed up as data.

Why identity resolution matters beyond VideoAmp

Every company working in cross-platform measurement is fighting some version of this same battle. Signals are fragmenting further, not less. Identity resolution is no longer a nice-to-have layer, but the foundation everything else gets built on.

So what actually happened when VideoAmp put identity resolution at the center of their measurement stack? How much of their previously unknown data became verified? And what did that mean for the accuracy of their audience targeting and reach reporting?