Research from audience intelligence firm NumberEight suggests that traditional IP-based and household-level identifiers are increasingly inaccurate for demographic targeting in fragmented media environments like CTV and mobile gaming. The study advocates for a shift toward ID-less, contextual, and behavioral modeling to improve audience precision without relying on persistent identifiers.
The reliance on IP addresses as a proxy for identity creates a significant measurement gap for advertisers in fragmented environments like CTV and podcast advertising. When household-level data fails to distinguish between individual members, ad spend is likely being misallocated to incorrect demographics. This shift toward ID-less, behavioral modeling suggests a move away from persistent identifiers in favor of AI-driven predictive signals that do not rely on PII. As privacy regulations tighten, the industry must determine if these contextual models can provide the granular reach that traditional third-party data promised but failed to deliver. Watch for whether major DSPs begin integrating behavioral modeling to supplement or replace aging IP-based targeting sets.
Recent programmatic data enrichment challenges highlight the growing skepticism surrounding legacy targeting methods. Advertisers are also increasingly looking to CTV marketing mix models to improve campaign calibration.
A new study by NumberEight reveals that IP-based identifiers in CTV often fail to distinguish individual viewers within a household. Traditional proxies produced nearly identical demographic profiles across diverse content, whereas ID-less behavioral models captured more precise segments. This highlights a significant measurement gap for advertisers relying on legacy targeting methods.
IP-based models often fail to distinguish between individual viewers within a single home, resulting in consistent demographic distributions across diverse content genres rather than reflecting actual audience differences.
The study found that traditional identifiers produced a 53% to 55% female distribution regardless of content, while behavioral modeling showed an 82% female split for 'Fashion Battle' compared to 51% using IP-based models.
NumberEight advocates for ID-less methods that utilize contextual signals, such as the host, topic, and device type, to identify audience segments without relying on persistent identifiers or PII.
Inaccurate household-level data leads to a significant measurement gap, which likely results in ad spend being misallocated to incorrect demographics in fragmented environments like CTV.
Add StreamingMeme as a preferred source on Google to see more of our streaming news at the top of your Search results.
Add as preferred source
