Zero-party data is information that customers intentionally and proactively share with a brand—such as preferences, interests, and purchase intentions—in exchange for more relevant, personalized experiences.
Consumers have big expectations of the brands they engage with today, and they won’t settle for generic experiences. Instead, they want brands to understand who they are and deliver personalized experiences across channels that meet their needs. To do that, brands need data that helps them understand their customers intimately.
Enter zero-party data.
Coined by Forrester Research, zero-party data is data that your customers intentionally and proactively share with you in exchange for something of value. The most common zero-party data is preference data, including information such as contact methods, interests, preferences, favorites, etc. But it can also include other data from quizzes or surveys.
How Zero-Party Data Differs From First, Second, and Third-Party Data
While there is an increased focus on zero-party data, it’s not the only type of data available. It’s essential to understand the differences between customer data types, because they all play unique roles when delivering a great customer experience.
First-Party Data (1P Data)
First-party data is collected through direct interactions with your customers through marketing, sales, and customer support activities. It can include information on products and services customers have interacted with, web pages viewed on your website or mobile application, emails opened or clicked on, and so on. Types of first-party data include personal data like name, address, phone number, demographics, website activity, sales interactions, email engagement, and customer feedback surveys.
Second-Party Data (2P Data)
Second-party data is data you get from a trusted source, like a partner. It’s similar to first-party data in that it’s collected from customers but not directly by you. It can include personal data, demographics, website activity for the partner’s website, feedback surveys, and more. Because you have a direct, trusted relationship with the partner, you know the data is reliable and high quality.
Third-Party Data (3P Data)
Third-party data is much different from all other data types. It’s usually data obtained from ad platforms and aggregators, and can include data such as demographics, firmographics, and buyer signals. This type of data isn’t collected directly from customers, but from multiple sources. It’s then combined into a single dataset and sold to companies who then match it up with their first and second-party data. As a result of how this data is captured, you have no way of knowing if it’s accurate and reliable.
Adding Zero-Party Data (0P Data) to the Mix
So how does zero-party data fit in here? First, it’s one of the best data sources you can have because it comes directly from the customer, and tells you exactly how that customer wants to be engaged. When connected with first-party data, you can create highly personalized experiences with accuracy.
It’s also important to note that zero-party data is given freely, so you have permission to use it to personalize offers, content, and other experiences. At a time when data privacy regulations are expanding globally, and consumers are becoming more particular about how their data is captured and used, having access to zero-party data is crucial.
In 2026, zero-party data has gained a powerful new use case: AI agents can now use explicitly stated customer preferences to autonomously personalize interactions in real time. When a customer tells you they prefer email over SMS, or that they are interested in running shoes over casual footwear, an AI agent can act on that data immediately, provided the preference is actually wired into the agent’s decision layer rather than merely stored — selecting the right channel, product recommendations, and messaging without human intervention when it is. This makes zero-party data one of the highest-value inputs for AI personalization.
Why Brands Are Shifting To Zero-Party Data Engagement Strategies
The deprecation of third-party cookies has reduced brands’ ability to create personalized experiences through inferred data. Third-party data was never as reliable, and zero-party data offers a far better alternative.
But it’s not just the loss of third-party cookies, or the privacy controls built into Apple devices and major browsers. Customers expect you to provide omnichannel experiences that shows you know and understand them.
Customer expectations are forcing brands to rethink their idea of customer identity. First-party data tells you a lot about your customers, but doesn’t tell you everything. And you don’t have that first-party data until you sell something or engage with a consumer who engages back.
That’s why many brands are starting to implement zero-party data engagement strategies. Zero-party data can be collected for customers and prospective customers alike. Access to this data type means that you know what customers want; they’ve told you. You don’t have to infer it from less reliable data, and you don’t have to make best guesses regarding intentions.
Implementing a zero-party engagement strategy also means you have a greater chance of winning a customer. For example, let’s say someone signs up to your website and sets their preferences for products they want to see and how they want you to communicate with them. They’ve yet to purchase from you, but you now have critical information to engage with them the way they want, increasing the chance they will purchase from you.
How Zero-Party Engagement Helps Foster Value Exchange
With the economy in a downturn, acquiring new customers is becoming more challenging. But retaining existing customers and building true loyalty to a brand is an equal challenge. It only takes one bad experience for a customer to switch to a competitor. And that experience could be as simple as continually sending a newsletter with content featuring a product they’ve already purchased. Or, hyping a sale on Nike sneakers when the customer has told you they prefer Reebok.
A zero-party data strategy lets you ask your customers what they want to see and do with you. If they trust you and want the value you are offering in exchange for that data, they will provide it. It’s critical that you act on that data — that value exchange is what you need to grow loyalty and retention.
As long as you continue to provide the value you promised, customers will continue to come back and buy from you. Keep in mind that a customer’s preferences change over time. Babies turn into toddlers, who turn into teenagers; people keep up to date with the latest fashion trends, or switch from regular cola to diet. Providing a way for them to modify their preferences as needed is also critical to ensuring loyalty and retention.
For example, Sephora lets its customers set beauty traits to get personalized product recommendations. Then, they can come back any time and update them.
Sephora Beauty Traits and Color IQ
Types of Zero-Party Data Engagement Strategies Brands Are Using Today
There are a number of ways brands can implement a zero-party data engagement strategy. But first, you must know what information you want to capture, and how you will use it.
Spend some time thinking about what type of information you’ll need, including data like:
- Favorites
- Interests
- Needs
- Communications
- Preferences
How to Capture Zero-Party Data
Some examples of how you can capture zero-party data include:
- Interactive funnels like surveys and quizzes
- Social media polls
- Contests
- An email welcome series that includes a “getting to know you better” survey
- Opt-in forms
- A preference center included in the registration or sign-up process.
One example is from Old Navy, which uses the AI tool True Fit to help people find the right clothing size by taking a visitor through a short quiz.
Providing Value Using Zero-Party Data
Once you capture that information, what value will you provide? Consider these options:
- Recommendations for products or related content
- Discounts or exclusive offers
- A special gift on their birthday
- Assessments or scores
- Exclusive or personalized content
In this example, Sketchers asks for communication preferences, so it can personalize the types of email campaigns they send to you. The brand also asks for your birthday to provide a special discount when the big day is close.
In all these zero-party data examples, the goal is always to understand the customer well enough to create the personalized experiences they have come to expect. It’s critical to be transparent and open about what data you are collecting and how you will use it. It’s also important to give consumers the ability to update that information where possible, or opt-out if they change their mind.
Ultimately, brands need to understand their customers on a much more intimate level if they are going to create differentiating personalized experiences and build brand loyalty. As AI decisioning and autonomous agents become the primary engines of customer engagement, zero-party data provides the explicit, consent-based signal that makes AI recommendations trustworthy and effective. Zero-party data can help lead the way.
How to Tell Whether a Zero-Party Data Program Is Working
Most zero-party programs report on collection volume — quiz completions, preference-center signups, survey responses. That number rises with promotional spend and says nothing about whether a declared preference ever reached the customer as a better experience. Four measures do, drawn from how loyalty and lifecycle teams actually audit these programs, and each maps to a loyalty outcome rather than a campaign one — including for a program like the Skechers preference center above, where submitting the form is the easy half and honoring the birthday and channel choices it captured is the half that actually keeps the customer.
Declared coverage. The share of active customers whose profile carries at least one current declared attribute — not the count of submissions, which the same already-engaged segment inflates by answering everything you publish. Break it out by loyalty tier and by tenure. Red flag: coverage concentrated in your top tier. Those customers were already understood; the mid-tier accounts where retention is actually decided remain inferred.
Preference-honored rate. Two things, tracked separately: the share of sends that match the customer’s selected channel and topic (a per-message check), and the share of customers who stayed within their selected frequency over a rolling period such as the past 30 days (a per-customer check, since frequency compliance isn’t something any single message can carry alone). Measure both against sends and customers respectively, not against the preference file. A preference stored but not enforced is worse than one never collected — the customer supplied it, remembers supplying it, and reads the next mistimed email as proof the ask was decorative. Red flag: unsubscribes rising fastest among customers who have set preferences. That group told you how to keep them.
Declared-segment lift. Randomly hold out a share of the declared-attribute segment from personalization, and compare its performance against a size-matched behavioral-equivalent segment carrying the same holdout structure — a same-mechanism comparison, not a before-and-after, so seasonality and list growth do not get credited to the program. This is the number that keeps the budget. Red flag: lift that shrinks once the comparison segment is behavioral rather than the full remaining list — that pattern means you are measuring engagement in general, not the value of what customers specifically told you.
Re-confirmation rate. The share of customers who update an expiring attribute when prompted inside something they were already doing — a delivery confirmation, a tier review, a returns flow — tracked per attribute rather than as one program-wide figure, since different attributes expire on different clocks. Report it on the same cadence as the loyalty dashboard it feeds, with one owner per attribute type rather than one owner for “zero-party data” broadly; a profile nobody maintains is a cheaper early warning than a lapsed purchase cycle, if someone is actually watching for it. Red flag: re-confirmation prompts performing below your list average. Customers stop maintaining a profile when they stop seeing a return on the last thing they shared.
Read together, these are retention metrics rather than data-quality ones, which is why they belong next to customer retention and repeat-purchase reporting rather than in a separate data dashboard. They are also far easier to compute when the declaration, the send, and the outcome sit on the same profile than when each lives separately in the quiz tool, the survey vendor, and the loyalty app that collected it — which is the practical case for routing zero-party data through a customer data platform. Teams without one yet can approximate the same four numbers by joining exports on a shared customer ID: start with declared coverage and preference-honored rate, since those need the least data engineering, and treat lift and re-confirmation as reasons to prioritize the unified profile rather than numbers to fake without it. Brands that can answer all four questions are running a value exchange. Brands that can only report completions are running a data collection campaign and calling it loyalty.