Put this into practice
Run A/B tests and personalize your store without code. 14-day free trial, no credit card.
Start free trial →Run A/B tests and personalize your store without code. 14-day free trial, no credit card.
Start free trial →Third-party data is audience information collected by an entity that has no direct relationship with the individuals in the dataset — data brokers, ad networks, data marketplaces, and publishers who aggregate information from many sources and sell it to advertisers and marketers. Examples include demographic profiles, interest categories, behavioural segments, and purchase propensity scores that advertisers use to target ads on platforms like Google Display Network and programmatic exchanges.
Third-party data is distinct from:
Third-party data has historically been the engine of digital advertising — enabling advertisers to reach audiences based on inferred interests and behaviour across the web, not just within their own sites. For ecommerce brands, it underpins much of the display advertising, programmatic retargeting, and audience enrichment that drives top-of-funnel traffic.
However, third-party data is declining in reliability and availability due to three converging forces:
For Indian D2C brands, reliance on third-party data for audience targeting is a strategic risk. Building first-party and zero-party data assets is the durable alternative.
A D2C furniture brand uses third-party audience segments ("home renovation intenders," "new homeowners") for their display advertising campaign targeting. They notice conversion rates from these segments are 0.8% — well below the 2.4% from retargeting campaigns using their own first-party data. Investigation reveals: the "home renovation intenders" audience was last updated 45 days ago; match rate against their actual customer lookalikes is 28%; and the segment includes many apartment renters who can't purchase large furniture. Their first-party lookalike audience (Meta's "Similar Audiences" based on their own purchaser list) converts at 1.9% and has a 40% lower cost-per-acquisition. They reduce third-party audience spend by 70% and reinvest in first-party data collection and lookalike targeting.
Third-party data has limited direct application in A/B testing, but it affects the traffic composition that reaches your tests. If your acquisition shifts from highly targeted third-party audiences to first-party lookalikes, the visitor profile entering your experiments changes. Be aware of traffic quality shifts when evaluating test results across time periods where your acquisition mix has changed.
Run smarter A/B tests with CustomFit.ai — 14-day free trial, no credit card required.