NumberEight Publishes New Research on the Accuracy of Demographic Data in Advertising
by on 23rd Sep 2026 in News
NumberEight, a leader in AI-native audience intelligence, today (23rd September, 2026) announced the publication of its new research report, Demographic Data in Advertising: Traditional vs. ID-less Methods, examining how reliably the demographic data used in advertising reflects the audiences consuming different media.
As media consumption becomes increasingly fragmented across mobile, gaming, podcasting, and connected TV (CTV), advertisers continue to rely on demographic data for media planning, targeting, and measurement. Yet many established approaches use persistent identifiers and household-level signals to infer individual characteristics such as age and gender.
The new report compares traditional approaches, including self-reported data, IP-based matching and third-party identifiers, with ID-less methods that combine contextual and behavioural signals with anonymised first-party demographic data.
Drawing on published academic and industry research alongside real-world mobile and CTV analyses, NumberEight examines the strengths and limitations of each approach and the implications for advertisers seeking to understand audiences without relying on personal identifiers.
Key findings from the research
In mobile gaming, NumberEight compared gender predictions from its ID-less model with an IP-based model from a leading US data provider across five games with distinctly different audiences.
The IP-based model repeatedly returned gender distributions close to 50/50, regardless of the game. For example, Fashion Battle was identified as 51% female by the IP-based model, compared with 82% female using NumberEight's ID-less model.
A separate CTV analysis examined third-party demographic data across 7,013 shows and more than 3 million unique users. Household-level demographic profiles showed very little variation across different genres, despite established differences in audience consumption patterns.
These findings highlight a fundamental limitation of using household- or network-level match keys to infer individual demographic characteristics. While an IP address can help associate activity with a household or network, it cannot reliably identify which individual is consuming the content.
"Demographic data is one of the most widely used inputs in advertising, but we rarely stop to question how that data is actually generated or how well it reflects the person consuming the content," said Emma Raz, commercial director at NumberEight. "What stood out in this analysis was how consistently some traditional approaches produced almost identical demographic profiles across content we know has very diverse audience profiles. The issue isn’t simply whether you can match an identifier. It’s whether the data attached to that identifier actually tells you something meaningful about the audience."
Exploring alternatives to traditional demographic data
Beyond its mobile and CTV findings, the report examines the wider limitations of self-reported data and third-party identifiers, including inaccurate information, fragmented identity graphs, and declining identifier availability.
It also explores how ID-less audience intelligence can use anonymised first-party demographic data alongside contextual, behavioural and device-level signals to infer demographic characteristics without relying on PII, IP addresses or device IDs in the processing.
"The shift away from persistent identifiers creates an opportunity to rethink how we understand audiences," said Abhishek Sen, CEO and co-founder of NumberEight. "Sophisticated AI-led approaches that combine a variety of data sources from various channels enable us to create holistic predictive models that cover demographic predictions that are omnichannel at their core. Instead of treating identity as a proxy for understanding, we can focus on whether the signals actually tell us something meaningful about the audience."
Download the full report: Demographic Data in Advertising: Traditional vs. ID-less Methods.
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