Imagine you’re getting to know someone new. They might tell you directly: “I love hiking and I’m vegetarian.” That’s them volunteering information. Or you might notice they always order salads and wear hiking boots. That’s you observing their behavior. Both give you insight, but the first is explicit and (likely) accurate, while the second requires interpretation and could be wrong.
This is exactly the difference between zero-party data and first-party data in marketing. As third-party cookies crumble and privacy regulations tighten, understanding how to collect and use both types of data has become essential for building direct customer relationships and driving marketing effectiveness.
Key takeaways
- Zero-party data is information customers intentionally and explicitly share with your brand, while first-party data is collected through observing customer behavior and interactions on your owned channels.
- Zero-party data requires active customer participation and explicit consent, which (most of the time) provides high accuracy about stated preferences and purchase intentions. First-party data is collected automatically through website visits, app usage, and other direct interactions.
- Both data types are owned directly by your company and comply with privacy regulations when collected with proper consent, unlike third-party data from external sources.
- Zero-party data typically has lower volume but higher accuracy, while first-party data offers higher volume but requires interpretation to understand customer preferences.
- Combining zero-party data and first-party data creates the most complete picture of your customers: what they do (behavioral data) validated by what they want (stated preferences).
- First-party data collection happens through browsing behavior, purchase history, and customer interactions. Zero-party data is collected through preference centers, customer profiles, customer surveys, and interactive content.
- The key difference lies in how data is collected: zero-party data comes from explicit customer input, while first-party data is gathered through passive observation of user behavior.
What is zero-party data?
Zero-party data is information that a customer intentionally and proactively shares with a brand. Think of it as customers raising their hand and telling you exactly what they want, what they like, and how they prefer to interact with your business.
The term “zero party” emerged to distinguish this highly intentional data from other party data types. Unlike first-party behavioral data that you observe, zero-party data collected comes directly from customers interact with forms, quizzes, and preference centers where they explicitly state their interests.
Common examples of zero-party data include:
- Preference center selections where customers choose communication preferences and content interests
- Survey responses about product satisfaction, brand perception, or feature requests
- Quiz answers that reveal style preferences, needs, or product fits
- Wishlist additions showing future purchase intentions
- Size, fit, or dietary preference information customers provide
- Account profile data voluntarily updated beyond what’s required
- Feedback and product reviews
- Event registration information and session preferences
- Birthday and anniversary dates shared for special offers
- Personal context like “shopping for myself” versus “shopping for gifts”
What makes zero-party data valuable is that it eliminates the need for inference. When someone tells you their pant size, you can take that information at face value and use it to filter products appropriately. However, zero-party data isn’t perfectly accurate just because it comes directly from customers, and recognizing this limitation is crucial for using it effectively.
Understanding the accuracy of zero-party data
People don’t always tell the complete truth when sharing preferences, and this isn’t necessarily about deliberate deception. Sometimes customers answer based on aspirational identity rather than actual behavior. Other times, stated purchase intentions reflect genuine interest in the moment but change as circumstances shift. Zero-party data tends to be most reliable with concrete, factual information like sizes, locations, and specific feature requirements, while subjective preferences and future intentions can be less predictive of actual behavior.
This doesn’t make zero-party data useless. This type of data is just incredibly human. The key is using it appropriately and, whenever possible, validating stated preferences against actual behavior through first-party data. We explore the nuances of zero-party data accuracy, collection strategies, and use cases in more depth in our explainer on what is zero-party data.
What is first-party data?
First-party data is information your company collects directly through customer interactions and user behavior across your owned channels. This is the behavioral data that accumulates as customers interact with your website, app, emails, and other touchpoints where you have direct customer relationships.
Unlike zero-party data where customers actively volunteer information, first-party data collection happens automatically as customers navigate your digital properties. You’re observing what they do rather than asking what they want.
Common examples of first-party data include:
- Website visits and browsing behavior (which pages they view, how long they spend, navigation patterns)
- Purchase history showing what they’ve bought, when, and at what price point
- Shopping cart activity including items added, removed, or abandoned
- Email engagement metrics like open rates, click-throughs, and time spent reading
- App usage patterns showing feature adoption and session frequency
- Customer service interactions and support ticket history
- Transaction data including payment methods, shipping addresses, and order values
- Search queries within your site revealing what customers are looking for
- Video watch time and content engagement
- Form submissions and account creation data
- Device types, browsers, and general location information (when permitted)
- Session duration and visit frequency patterns
- Product page views that don’t result in purchases
First-party data strategies have become increasingly critical as third-party cookies phase out. This data is yours, collected through direct interactions with your customers on your own channels. That means it’s not affected by browser restrictions on third-party cookies or limitations on data aggregators.
The challenge with first-party behavioral data is that it requires interpretation. Someone viewing your pricing page five times might be seriously considering a purchase, or they might be a competitor doing research, or they could be confused about your packaging. You’re inferring intent from user behavior rather than knowing it for certain. We go into all of this more in depth in our guide to what is first-party data.
Key differences: zero-party data vs first-party data
While both zero-party data and first-party data come from direct customer relationships, understanding the key difference between them shapes how you collect, interpret, and use each type.
How the data is collected
Zero-party data requires explicit customer action. Someone must fill out a form, answer a quiz question, update a preference, or provide feedback. The data collection is obvious and intentional.
First-party data collection happens passively as customers use your digital properties. Tracking pixels, analytics tools, and engagement metrics capture user interactions automatically, often without the customer actively thinking about sharing data.
Customer awareness and intent
With zero-party data, customers know exactly what they’re sharing and why. They’ve made a conscious decision to tell you their preferences, which creates transparency and customer trust.
First-party data collection can happen with varying levels of customer awareness. While users consent through cookie banners and privacy policies, they may not actively think about all the behavioral data being captured as they browse, especially regarding browsing behavior and user interactions that feed into data collected automatically.
Data accuracy and interpretation
Zero-party data provides the highest data accuracy because it comes straight from the source with no interpretation needed. If someone tells you they prefer email over text, you know their communication preferences definitively.
First-party data requires analysis to understand what behaviors mean. High cart abandonment might indicate pricing concerns, confusing checkout, or customers comparison shopping. You’re making educated guesses about customer behavior rather than knowing customer preferences for certain.
Volume and scale
First-party data collection generates massive volume. Every website visit, every click, every email open creates a data point. You can collect first-party data at scale without additional customer effort.
Collecting zero-party data requires customer participation, which naturally limits volume. People will only answer so many questions or fill out so many forms. You need to be strategic about when and how you ask.
Use cases and applications
First-party data excels at understanding the customer journey, identifying behavior patterns, building customer segments based on actions, and powering attribution models that show which touchpoints drive conversions. It’s essential for marketing campaigns that target based on demonstrated interest.
Zero-party data shines in personalization, product recommendations based on stated needs, customer satisfaction tracking, and creating relevant content matched to expressed interests. It allows you to deliver personalized customer experiences that feel genuinely helpful rather than creepily observed.
Privacy and consent
Both require customer consent under privacy regulations, but zero-party data inherently includes explicit consent since customers are actively choosing to share. The act of providing the data is the permission.
First-party data collection needs clear privacy policies explaining what data you collect, how you use it, and giving customers control. Consent management becomes more complex because the data collection isn’t always immediately obvious to users.
Why both zero- and first-party data matter for modern marketing
The most sophisticated data strategy doesn’t require you to choose between zero-party and first-party data. You need to understanding how they work together to create a complete picture of your customers, including where stated preferences diverge from actual behavior.
The complementary nature of data types
First-party data shows you what customers do. Zero-party data tells you what customers say they want. The gap between these two is valuable information.
Someone might state in a preference center that they care deeply about sustainability (zero-party data) but consistently purchase the cheapest option regardless of environmental impact (first-party data). This tells you that sustainability is an aspirational value rather than a primary purchase driver for this customer. You might mention sustainable practices in your marketing to align with their self-image, but you wouldn’t price premium products solely on environmental benefits for this segment.
Conversely, someone might browse your outdoor gear section repeatedly (first-party behavioral data) but never purchase. If you also know from zero-party data that they’re planning a hiking trip in three months, you understand the timing and can adjust your marketing approach accordingly. Here, the stated preference (future trip) explains and validates the behavioral pattern.
This combination helps you build more accurate customer segments. Instead of grouping people solely by past actions or solely by stated preferences, you can create segments that reflect both demonstrated behavior and expressed intent, weighted appropriately based on how well stated preferences predict actual behavior in your specific context.
Privacy laws and data privacy advantages
Both party data types offer significant advantages in our privacy-conscious environment. Since they come from direct customer relationships, you own them completely. There’s no dependence on third-party data from data aggregators or worries about third-party cookies disappearing.
When you collect both with proper explicit consent and clear data privacy policies, you’re building a compliant data strategy that respects privacy regulations like GDPR and CCPA. You’re also building customer trust by being transparent about what data you collect and how you use it.
This matters even more as privacy laws continue to evolve. Data privacy regulations increasingly favor direct relationships and transparent party data collection over the opaque data practices that characterized third-party data ecosystems.
Competitive advantages and data strategy
Companies that master both collecting zero-party data and maximizing first-party data collection create defensible competitive advantages. You’re building a direct relationship with customers that competitors can’t replicate or purchase through second-party data or third-party data. (Similarly, you will never have access to someone else’s first-party data or zero-party data.)
This becomes particularly powerful when you think about data quality. First-party data strategies combined with zero-party data collection create a proprietary understanding of your customers. You know things about your audience that paid advertising platforms and marketing campaigns from competitors can’t easily access.
The brands winning with data strategy are strategically combining data types to unlock insights neither could provide alone. They’re using first-party insights about behavior to inform what voluntary customer data they should collect, and using that customer data to validate their interpretations of first-party data.
Combining zero- and first-party data for better insights
The real power emerges when you use zero and first party data together, validating one against the other and filling gaps that either type alone would leave.
Validation and gap identification
Direct consumer data provides a reality check on interpretations of first-party behavioral data. When someone’s browsing behavior suggests interest in premium products but their zero-party data indicates high price sensitivity, you have valuable information about the gap between aspiration and intent.
These disconnects between stated preferences, as frustrating as they can be for marketers, should be seen as insights. Someone might browse luxury items for inspiration while planning to purchase mid-range alternatives. Understanding this through combined data types prevents you from misinterpreting their browsing behavior as purchase intent at the higher price point.
You can also use direct customer data to validate customer segments built from first-party data. If your behavioral segmentation identifies a group as “high engagement” but their zero-party data shows low satisfaction scores or declining interest, you know the engagement metrics don’t tell the complete story.
Enhanced personalization
Layering both data types creates more nuanced, effective personalization. First-party data shows someone repeatedly viewing a specific product category. Direct customer data from a quiz reveals they’re shopping for a gift rather than themselves, and they have a specific budget in mind. Now you can personalize not just the product category but the price range and gift-oriented messaging.
Recommendation engines improve dramatically when they incorporate both behavioral signals and stated preferences. Streaming services that blend viewing history (first-party behavioral data) with explicit ratings and preference settings (volunteered customer data) deliver significantly better recommendations than either data type alone could produce.
Marketing campaigns become more sophisticated when you can target based on demonstrated behavior validated by stated intent. Someone browsing your business software (first-party data) who also indicated in a survey that they’re evaluating new tools in the next quarter (zero-party data) represents a much higher-quality lead than browsing behavior alone would suggest.
Practical examples of data types working together
First-party data shows an ecommerce apparel brand that a customer regularly browsing women’s athletic wear but rarely purchasing. Their preference center data indicates they prefer shopping during sales and have a stated preference for sustainability.
Now the brand can send targeted marketing communications when sustainable athletic wear goes on sale, addressing both the behavioral pattern and the volunteered preferences. The response rate will be dramatically higher than targeting based on browsing behavior alone.
A B2B SaaS company might notice through data collected that certain users spend significant time in specific product features but never advance to paid plans. Zero-party data from an in-app survey reveals these users don’t understand how to apply the features to their use case. The company can create targeted onboarding content, address a knowledge gap they wouldn’t have identified from user behavior alone, and improve conversion rates.
A subscription service tracks first-party data showing certain customers rarely use the service after their initial month. Zero-party data from an exit survey reveals the primary reason is “too busy” rather than dissatisfaction with the product. This allows the company to create a “pause” option and re-engagement campaigns focused on convenience rather than trying to improve a product that customers actually like.
Wrapping it up…
The distinction between zero-party data and first-party data represents two fundamentally different ways of understanding customers that become exponentially more powerful when combined. Zero-party data tells you what customers want through their explicit choices and stated preferences. First-party data shows you what customers do through their observed behavior across your owned channels.
The brands that will win are the ones collecting the right data types and using them strategically. They understand that first-party behavioral data needs validation from zero-party stated preferences. They recognize that browsing behavior gains meaning when viewed alongside purchase intentions and customer preferences that customers explicitly share.
From a measurement perspective, understanding the marketing return on investment from your ad budget remains crucial. While zero-party and first-party data help you understand customers, you still need sophisticated measurement to understand which marketing campaigns and channels are actually driving growth.
This is where Prescient’s marketing mix modeling becomes essential. We help brands understand not just customer behavior but which marketing investments drive the best outcomes. Our Validation Layer feature takes this further by allowing you to test whether incorporating first-party data from surveys and stated preferences improves your model’s accuracy.
The future of marketing measurement lies in combining rich customer data from both zero-party and first-party sources with sophisticated modeling that can validate what truly drives results. When you know both what customers want and what marketing efforts actually influence their behavior, you can make confident decisions about where to invest your budget for maximum impact. Book a demo to see what insights the Prescient platform could reveal for your brand.
FAQs
What are the differences between zero-, first-, second-, and third-party data?
Zero-party data is information customers intentionally share with you, like preference selections and survey responses. First-party data is information you collect directly through observing customer behavior on your owned channels, like website visits and purchase history. Second-party data is essentially someone else’s first-party data that they share with you through a partnership (for example, a retailer sharing customer insights with a brand they carry). Third-party data comes from external data aggregators who compile information from multiple sources and sell it to companies that have no direct relationship with those customers.
The key distinction is about the directness of the relationship: zero-party and first-party data come from your direct customer relationships, second-party data comes from a partner’s direct relationships, and third-party data comes from sources with no direct relationship to the customers at all.
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