Zero-party data is information that a customer deliberately and voluntarily shares with a brand — as opposed to data that is observed, inferred, or purchased. It includes explicitly stated preferences, purchase intentions, personal context, and interests that users provide directly, typically in exchange for more relevant products, recommendations, or experiences.
The term was coined by Forrester Research to distinguish a particularly high-quality category of first-party data. While first-party data encompasses all data collected through owned interactions (including passively observed behavioral signals), zero-party data is the subset where the customer has actively participated in the data collection, making it the most accurate and consent-clear data an advertiser can possess.
What counts as zero-party data
Preference centers where users indicate their interests, content preferences, communication frequency preferences, and product categories they care about are the most common source of zero-party data. A user selecting "I'm interested in running gear" directly provides actionable segmentation data.
Quizzes and configurators collect rich intent signals. A skincare brand's "find your routine" quiz, a software product's onboarding questionnaire about company size and use case, or a travel site's trip planning tool all generate zero-party data at scale — users engage willingly because the outcome is useful to them.
Wishlist and save features on e-commerce properties capture explicit purchase intent. Unlike browsing behavior (first-party) which is inferred, a product added to a wishlist is an explicit declaration of interest.
Stated intentions during purchase flows — budget ranges, timing ("I plan to buy within 3 months"), and specific requirements — provide zero-party purchase signals that are far more valuable than modeled lookalike intent.
Feedback and surveys collected post-purchase, during product trials, or in loyalty programs round out the picture with explicit satisfaction signals, competitive context, and stated needs.
Why zero-party data is valuable for advertising
Accuracy advantage over behavioral inference is the defining characteristic of zero-party data. When a user states their preference directly, there is no modeling error. Inferred interests from behavioral data carry significant noise — a user browsing baby products may be shopping for a gift, not expecting a child. Zero-party data eliminates this ambiguity.
Consent clarity is inherent. Zero-party data is always collected with the user's active participation, making the consent foundation unambiguous. Under GDPR and similar regulations, using data the user intentionally provided to personalize their experience is on the clearest legal ground available.
[Ad personalization](/glossary/ad-personalization) quality improves dramatically when targeting is based on explicitly stated preferences. Creative relevance, product recommendations, and offer matching all benefit from zero-party signals that reflect actual customer desires rather than inferred proxies.
Durability is another advantage. Behavioral data can become stale quickly — a user who browsed running shoes three months ago may no longer be in-market. Zero-party data, refreshed at the time of collection, captures current intent and preferences.
Collecting zero-party data at scale
Value exchange design is the fundamental principle. Users share zero-party data when the return is worth the effort — better recommendations, personalized experiences, relevant offers. Marketers must design collection mechanisms that deliver immediate, tangible value to the user.
Progressive profiling gathers data incrementally rather than frontloading data collection. A user's first interaction might capture just a product category preference; subsequent interactions build toward a richer preference profile without creating friction at any single touchpoint.
Integration with ad platforms closes the loop between collection and activation. Zero-party data must flow from preference centers and quiz tools into audience segmentation systems and ad platform custom audiences to drive targeting decisions.
How AI activates zero-party data
AI-driven advertising platforms can transform zero-party data from static segment inputs into dynamic targeting signals. Soku AI, for example, can combine zero-party preference signals with behavioral context and real-time performance data to continuously refine which creative messages and offers are served to each declared preference segment — ensuring that explicit intent is matched with highly relevant dynamic creative.
Challenges and considerations
Collection friction limits the volume achievable. Not all users will complete quizzes or engage with preference centers. Conversion rates for zero-party data collection are typically far lower than passive behavioral data collection, requiring deliberate investment in compelling value exchange design.
Data freshness management requires systems to periodically prompt users to update preferences. Stated preferences from 18 months ago may no longer reflect current needs, particularly in categories with fast-moving purchase cycles.
Integration complexity between preference collection tools and advertising activation systems requires technical investment. Data collected in a CRM or CDP must be mapped to ad platform audience fields, which often require custom integration work.
Audience size constraints can limit the scalability of zero-party targeting. Because zero-party segments are smaller than broad behavioral audiences, reaching meaningful scale may require extending with lookalike audiences seeded from the zero-party segment.
Over-reliance risk means zero-party data should complement, not replace, behavioral signals. Stated preferences sometimes diverge from actual purchase behavior — users say they prefer sustainable products but buy based on price. The richest targeting strategies triangulate across all available data types.
