Zero-Party Data: What It Is, Examples and How to Collect It Without Annoying Users

Zero-party data is information a customer intentionally and proactively shares with a brand: their preferences, goals, interests, needs and how they want to be contacted. The term comes from Forrester Research, and the key word in its definition is "intentionally." Nobody infers zero-party data from behavior; the customer tells you.

That makes it the most accurate data a company can have about what someone wants. It also makes it the hardest to get, because people only share it when the exchange feels worth it. This guide covers how zero-party data differs from other data types, real examples, and the UX principles that decide whether people answer your questions or close the tab.

Zero-party vs first-, second- and third-party data

The practical difference between zero-party and first-party data is stated versus observed. First-party data tells you a visitor looked at hiking boots three times. Zero-party data tells you they're planning a winter trek in Scotland and care most about waterproofing. Both are useful, and the best results usually come from combining them: what people say they want, checked against what they actually do.

Why zero-party data matters now

You'll often read that zero-party data matters because third-party cookies are disappearing. The real picture is more nuanced. Safari and Firefox block third-party cookies by default, but Google decided in 2025 to keep third-party cookies in Chrome rather than phase them out, and later wound down most of its Privacy Sandbox alternatives. Third-party tracking hasn't vanished; it has become less reliable, more regulated and less trusted.

Three forces make zero-party data more valuable anyway. Privacy laws such as GDPR in the EU and the CCPA in California require a clear basis for using personal data, and data people chose to give you is the easiest to justify. Users are more aware of tracking and more likely to reward brands that ask openly. And personalization, including AI-driven recommendations, works much better when it's based on what users actually want instead of guesses from browsing patterns.

Zero-party data examples

Product quizzes. Skincare, coffee, mattress and fitness brands ask a few questions about needs and preferences, then recommend products. The quiz is useful to the customer and produces structured preference data for the brand.

Onboarding questions in SaaS. Many software products ask new users about their role, team size and main goal during sign-up, then tailor the setup, templates and first steps. A project management tool that asks "What do you want to manage first?" and opens a matching template is collecting zero-party data and using it immediately.

Preference centers. Instead of an all-or-nothing unsubscribe link, users choose topics, frequency and channels. Every choice is zero-party data, and the alternative is usually losing the subscriber entirely.

Homepage and in-product micro-surveys. One question, such as "What brings you here today?", can route B2B visitors to the right content. Inside a product, a short question after a key action can reveal what users are trying to achieve.

Post-purchase and feedback forms. Asking what a product was bought for, or whether it was a gift, improves future recommendations and reduces irrelevant follow-up emails.

Configurators and calculators. Price calculators, plan selectors and product configurators collect requirements in a way that feels like a tool rather than a form.

How to collect zero-party data: UX principles

Every question you ask has a cost. Each one adds friction, and if users don't see why you're asking, they'll skip it, guess, or leave. These principles make the difference between data people give gladly and data you never get.

Offer clear value in return. The best collection points give something back right away: a better recommendation, a tailored setup, fewer irrelevant emails. If you can't explain what the user gets for answering, don't ask yet.

Ask at the right moment. Ask about preferences when the user is already making a related decision. A dietary preference question fits naturally in a meal-planning setup; it feels intrusive on a newsletter pop-up.

Ask less, then ask again later. Progressive profiling means collecting a little at a time: one or two questions at sign-up, more after users have seen value. Long upfront questionnaires lose people before they reach the product.

Make answers fast. Use tappable options, visual choices and sensible defaults rather than open text. Include "Not sure" or "Skip" so users don't invent answers to move on, which pollutes your data.

Show the effect immediately. When an answer changes what the user sees, say so: "Because you chose design teams, we've added these templates." This proves the question was worth answering and makes people more willing to answer the next one.

Let users change their answers. Preferences change. Put them somewhere easy to find and edit, such as a profile or settings page. Data users can update stays accurate; data locked in a one-time quiz goes stale.

Design for everyone. Quizzes and forms need to work with keyboards and screen readers, on small screens and in plain language. Inaccessible collection flows exclude users and skew your data toward whoever could complete them.

Designing the main collection patterns

Quizzes and product finders

Keep quizzes short, usually a handful of questions, and show progress. Each question should clearly lead toward the result, and the result page should explain why these recommendations fit the answers. Ask for an email only after showing value, or offer to send results rather than gating them entirely.

Onboarding questions in SaaS

Ask only what changes the first experience: role, use case or team size if it actually alters templates, defaults or guidance. Every onboarding question should map to a visible change. We cover the broader patterns in our guide to user onboarding.

Preference centers

Group options by topic and frequency, use plain labels, and confirm changes clearly. Offer "send less" as an alternative to unsubscribing; many people want fewer emails, not none.

In-product micro-surveys

Trigger one question at a relevant moment, such as after completing a task or using a feature for the first time. Don't interrupt critical flows, limit how often the same user sees a survey, and close the loop when feedback leads to a change.

How to use zero-party data

Personalize communication. Segment emails and messages by stated interests and goals rather than broad demographics.

Tailor the product and website. Show relevant templates, content, features or products based on what users said they need, and use their language in headlines and recommendations.

Inform the roadmap. Aggregated answers about goals and needs reveal which use cases matter most and which segments are underserved. Combined with user research, they help decide what to build next.

Improve AI personalization. Recommendation systems and AI assistants give better results when they start from stated preferences instead of inferring everything from clicks. We cover personalization with privacy as a design trend in our article on UX design trends.

Privacy and compliance

Zero-party data is still personal data. Under GDPR and similar laws you need a lawful basis for processing it, should collect only what you'll use, and must explain clearly how it will be used. Health, financial and other sensitive information carries stricter rules. Tell users why you're asking, keep answers secure, honor their right to see and delete what they've shared, and never sell or pass stated preferences to third parties without explicit consent. The trust that makes zero-party data valuable is easy to lose.

Common mistakes with zero-party data

Asking without using. Collecting preferences and then sending the same generic emails teaches users that answering is pointless.

Asking too much, too early. Ten questions before users see any value is the quickest way to lose them.

Treating answers as permanent. Preferences change; data that can't be updated slowly turns wrong.

Hiding the purpose. Vague questions with no explanation feel like data harvesting, even when the intent is good.

Ignoring behavior. What people say and what they do can differ. Use first-party data to check and refine stated preferences.

Zero-party data is a design problem as much as a marketing one. The brands that collect it well ask good questions at the right moment, give something back immediately and let customers stay in control of what they've shared.

Frequently asked questions

What is the difference between zero-party and first-party data?

Zero-party data is information customers intentionally share, such as preferences or goals. First-party data is information you observe from their behavior on your own channels, such as purchases, clicks or product usage. Zero-party data is stated; first-party data is observed.

What is first-, second- and third-party data?

First-party data is collected directly by a company from its own customers and channels. Second-party data is another company's first-party data shared through a partnership. Third-party data is collected by organizations with no direct relationship to the people in it, often aggregated and sold.

What are examples of zero-party data?

Quiz answers, preferences chosen in a preference center, goals and roles entered during product onboarding, survey responses, stated purchase intentions and requirements entered in a configurator are all zero-party data.

Is zero-party data GDPR compliant?

Zero-party data is still personal data, so GDPR applies. Because users share it knowingly, it's easier to collect transparently, but you still need a lawful basis, clear explanations, data minimization and respect for users' rights to access and delete their information.