Zero-party data: definition and difference with first-party
Updated on February 22, 2026
Quick definition
Zero-party data refers to the data that users proactively and intentionally share with a company — personal preferences, purchase intentions, interests — in exchange for a perceived value such as a personalised experience or exclusive content. It is the most accurate and ethical type of data because the user is a conscious, voluntary actor.
How it works
The term 'zero-party data' was popularised by Forrester Research in 2020 to distinguish data voluntarily declared by the user (zero-party) from behavioural data observed by the company (first-party data).
In practice, zero-party data takes many forms:
- Personalisation quizzes: 'What type of analytics are you looking for?'
- Preference centres: 'I want to receive newsletters about SEO'
- Voluntarily filled user profiles (industry, team size, goals)
- Satisfaction surveys and NPS
- Wishlists and product ratings
The key to zero-party data is reciprocity: the user agrees to share their data because they receive in return a more relevant and personalised experience.
Concrete example: an analytics SaaS that asks new sign-ups 'What is your main analytics challenge?' via an onboarding quiz collects zero-party data that lets it personalise onboarding emails and feature recommendations.
Zero-party data is particularly valuable because it is not tainted by observation bias (the user says what they actually do) and is consented to by definition — requiring no cookie banner.
Why it matters
In a context of growing restrictions on third-party cookies and user mistrust toward digital surveillance, zero-party data represents the future of marketing personalisation.
- More accurate than behaviourally inferred data
- More GDPR-compliant because explicitly consented to
- More durable because not subject to browser restrictions
- It strengthens trust and the relationship between brand and user
Unlike first-party data, which can become inaccessible in case of consent refusal, zero-party data remains available because sharing is voluntary.
How to improve or use it
- 1Integrate onboarding quizzes that reveal personalised recommendations in exchange for declared preferences.
- 2Create communication preference centres to let users choose the topics that interest them.
- 3Add in-session surveys (contextual micro-surveys after a key action).
- 4Enrich user profiles by proposing optional fields whose completion unlocks features or content.
- 5Accompany each request with a clear value proposition: 'Share your preferences to receive personalised recommendations'.
With Sublim
Sublim helps you identify the optimal moments to collect zero-party data by tracking your users' behaviours and engagement. By understanding when a user is most engaged (full read of an article, use of a key feature), you can trigger contextual zero-party data requests — maximising response rate and data quality — without resorting to third-party cookies or compromising GDPR compliance.
Frequently asked questions
What is the difference between zero-party data and first-party data?
Zero-party data is intentionally shared by the user (declared preferences, quiz answers). First-party data is observed by the company through user behaviour (pages visited, clicks, purchases). Both are owned and GDPR-compliant, but zero-party is more accurate because it represents what the user explicitly says about themselves.
Does zero-party data replace cookies?
Zero-party data does not directly replace cookies in their technical functions (authentication, cart). It does, however, replace third-party cookies in their profiling and personalisation functions, by offering a more accurate, more consented and more durable alternative to behavioural data inferred via cross-site tracking.
How do you encourage users to share zero-party data?
The recipe is reciprocity: offer tangible value in exchange for the data. A quiz that reveals personalised recommendations, a profile that unlocks exclusive content, or preferences that improve the product experience are effective mechanisms. Transparency about how the data is used also increases willingness to share.
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