Third-Party Data
Also known as: 3PD, third-party audience data, data broker data, purchased data
Third-party data is data about consumers collected by companies that don't have a direct relationship with those consumers: data brokers, ad networks, and data aggregators who compile and sell consumer profiles to advertisers.
Examples include demographic and interest profiles compiled from browsing history across many websites, purchase intent signals from retail data aggregators, and location history data from mobile apps. Third-party data powered the programmatic advertising industry for decades but is facing significant restrictions from GDPR, CCPA, Apple's App Tracking Transparency, and third-party cookie blocking in browsers such as Safari and Firefox.
Third-Party Data, explained at three levels
At home
Third-party data is information about you collected by companies you've never directly interacted with. When you visit websites, ad trackers on those sites record your browsing behaviour and build a profile of your interests. Data brokers buy and aggregate this information from many sources (your browsing, purchase patterns, location history, and public records) and sell these profiles to advertisers. This is how an advertiser can show you an ad for hiking boots even though you've never been to their website.
At work
Advertisers buy third-party data to reach people who match their target customer profile but have never heard of their brand. Third-party data enables targeting like 'in-market for a car purchase' or 'interested in outdoor adventure': interest categories compiled from browsing behaviour across many websites. While this sounds powerful, third-party data quality is inconsistent, consumer consent is increasingly required, and the technical mechanism that enables it (third-party cookies) is blocked by Safari and Firefox.
At home
Third-party data sources include: behavioural data (browsing history across publisher sites, captured by ad networks), purchase data (credit card transaction data sold by banks with consumer consent, or retail purchase data from loyalty programs shared with data brokers), location data (GPS data from mobile apps), and modelled data (inferred attributes like income range or life stage, predicted from behavioural and demographic signals). The accuracy of modelled attributes is often lower than directly observed data.
At work
Third-party data quality varies significantly by provider: some segments are based on recent, direct behavioural signals (recent browsing on relevant category sites); others are modelled from weak indirect signals and may be months out of date. Testing third-party audience segments against first-party conversion data (do people in this 'in-market for software' segment actually convert on our site?) is essential before committing significant spend to third-party audience targeting. Many advertisers find third-party audiences perform only marginally better than demographic targeting alone.
At home
Third-party data travels on identifiers. On the web, a tracker's cookie follows you from site to site; in apps, a device advertising ID does the same job. Safari and Firefox block those cookies, Apple's ATT hides the ID unless you opt in, and Google, after years of planning to remove cookies from Chrome, dropped that plan in 2025 and retired most of its Privacy Sandbox replacements. The fallbacks are login-based IDs such as Unified ID 2.0 and contextual targeting. In my view, third-party profiles are weakening slowly, not vanishing overnight.
At work
Retail media networks represent a new form of 'second-party data' (sometimes categorized within the first-party/third-party spectrum): Amazon Ads, Walmart Connect, Kroger Precision Marketing, and Canadian Tire's Triangle Retail Media are examples of retailers offering advertisers access to their purchase data for targeting and measurement. This data is more reliable than traditional third-party segments because it reflects actual purchase behaviour, but it's only available within each retailer's walled garden and is priced at a premium. Global retail media ad spending passed $100 billion by 2024, by most industry estimates, reflecting how much value brands place on purchase-signal data.
Real-world examples
At home
Jerome ran ads for his B2B software. He tried a 'business decision-makers' third-party audience from a data broker. The audience performed better than no targeting but 40% worse than his first-party retargeting audience (people who had already visited his website). He concluded that for his specific product, first-party retargeting and lookalike audiences built from his customer list significantly outperformed purchased third-party segments.
At work
Lena, a media director, ran a cookieless advertising test to see how campaigns would perform without third-party cookies. She split her programmatic budget equally between cookie-based retargeting, first-party customer match targeting, and contextual targeting (no user data, just page content signals). Cookie-based retargeting was strongest, customer match performed similarly on iOS-heavy demographics, and contextual targeting was less efficient but showed 100% compliance with privacy regulations and worked across all browser environments.
How it compares
| feature | thirdPartyData | firstPartyData |
|---|---|---|
| Source | External brokers and ad networks | Directly from your own customers |
| Consumer consent | Often limited or indirect | Direct opt-in or explicit transaction |
| Cookie dependence | High: relies on cross-site tracking cookies | Low: uses CRM, email, and owned touchpoints |
| Data freshness | Variable: may be outdated | Real-time: reflects current customer status |
| Long-term viability | Declining due to privacy regulation and browser cookie blocking | Growing in importance as third-party alternatives shrink |
Third-party data (from external brokers) is declining in availability and reliability due to privacy laws and browser cookie blocking. First-party data (from direct customer relationships) is increasingly the foundation of effective, compliant digital marketing.
For analysts
When a client's digital advertising is heavily dependent on third-party audience segments, quantify how much of their current campaign structure depends on third-party cookies, which already fail in Safari and Firefox. Run a test allocating 20% of budget to cookieless alternatives (contextual targeting, first-party match) and measure performance gap versus cookie-dependent campaigns. This data makes the business case for first-party data investment concrete, converting an abstract compliance concern into a measurable revenue risk.
History & origin
Third-party data has been a foundational element of the advertising industry for decades. In traditional media, audience data came from Nielsen ratings and Simmons consumer surveys. In digital advertising, third-party data came primarily from tracking cookies placed by ad networks on publisher sites across the web: each cookie recorded browsing behaviour and built consumer interest profiles. Companies like Acxiom and Experian built large third-party data businesses combining online behavioural data with offline purchase records, public records, and survey data. The GDPR (2018) required explicit consent for third-party data collection in Europe. Apple's ATT (2021) asked users to opt in to tracking by individual apps, with most choosing not to. Google's long-planned removal of third-party cookies from Chrome, though dropped in 2025, accelerated the industry's pivot toward first-party data and privacy-preserving alternatives.