Zero-party data: How to collect customer insights they actually want to share

Remember when you could just ask someone their favorite color and trust they’d tell you the truth? Marketing wishes it were that simple. The shift from eavesdropping on digital breadcrumbs to having customers voluntarily share their preferences sounds perfect in theory. Direct information, explicit consent, clear intentions. But what customers tell you isn’t always what actually happened.

Marketing has spent years trying to piece together customer preferences from behavioral signals and tracking data. The promise of zero-party data—information customers intentionally share—feels like finally getting invited into the conversation instead of listening through the wall. And in many ways, it is. But treating self-reported data as the ultimate source of truth ignores a fundamental reality: people are unreliable narrators of their own decision-making processes.

Key takeaways

• Zero-party data is information customers intentionally and proactively share with brands, including preferences, purchase intentions, and personal context that goes beyond observable behavior
• Collection methods like preference centers, interactive quizzes, and post-purchase surveys build trust and provide valuable signals for personalization without relying on invasive tracking
• Self-reported data has inherent limitations because customers often can’t accurately recall which marketing touchpoints influenced their purchase decisions
• Memory bias, social desirability, and the complexity of multi-touch journeys mean that what customers think happened often differs significantly from what actually drove revenue
• Zero-party data works best as one input for personalization and customer experience, while marketing mix modeling provides the statistical foundation for understanding what actually drives business results
• The most effective measurement strategies combine zero-party data for customer perspective, first-party behavioral data for observed actions, and MMM for unbiased attribution across all channels
• Privacy regulations exempt zero-party data from most restrictions because it’s explicitly consented, making it increasingly valuable as third-party tracking continues to decline

What is zero-party data?

Zero-party data is information that customers deliberately choose to share with your brand. Unlike behavioral data that you observe or infer, this customer data comes directly from them proactively telling you something about themselves, their preferences, or their intentions.

The term was coined by Forrester Research to describe a category of customer data that didn’t fit neatly into existing definitions. It fills the gap between what you can observe about customer behavior and what you can only know if customers tell you directly.

This includes information like:

• Stated product preferences or interests
• Quiz or assessment responses
• Preference center selections
• Purchase intentions or future plans
• Personal context like dietary restrictions or skin type
• Communication preferences
• Birthday and anniversary dates
• Product ratings and reviews

The defining characteristic is that the customer intentionally provides this information, usually in exchange for a better experience. They’re not passively generating data through their browsing behavior (that’s first-party data, and we’re getting to that). They’re actively choosing to tell you something.

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

The landscape of customer data includes multiple categories that differ fundamentally in how they’re collected and what level of customer awareness exists around that collection. It’s not always obvious what the difference is between third party data and first party data, for example. Understanding these distinctions helps you build a data strategy that balances effectiveness with customer trust.

Zero-party data is information customers intentionally and proactively share with your brand through explicit actions like completing quizzes, filling out preference centers, or responding to surveys. The customer knows exactly what they’re sharing and chooses to provide it in exchange for a better experience.

First party data is behavioral information you collect from customer interactions with your owned properties (website visits, purchase history, email engagement, and browsing patterns). While customers may understand data collection is happening with first party data, they’re not actively providing this information the way they do with zero-party data.

Second party data is another organization’s first party data that they share with you through a partnership or data-sharing agreement. A retail partner might share purchase data with a brand, or trusted partners might exchange customer insights to improve their joint offerings.

Third party data is aggregated information collected by entities with no direct relationship to the customer, typically gathered through third party cookies tracking behavior across multiple websites and sold through data brokers. Customers generally have little awareness of or control over third party data collection.

The key difference across these categories is the level of customer consent and awareness, with zero-party data representing the most transparent and permission-based approach while third party data operates with the least direct customer knowledge or approval.

How zero-party data differs from other data types

Understanding where zero-party data fits in your overall data strategy requires seeing how it compares to other consumer data categories. The cart below included privacy concerns and examples so you can have a wider context on the differences between zero, first, second, and third party data.

Data type	Source	Customer awareness	Examples	Privacy concerns
Zero-party data	Directly from customer, intentionally shared	High – customer chooses to share	Quiz responses, stated preferences, wishlist items, purchase intentions	Minimal – explicitly consented
First party data	Customer behavior on your properties	Medium – may not realize extent of tracking	Website clicks, purchase history, email opens, browsing patterns	Moderate – depends on transparency
Second party data	Partner’s first party data	Low – often unaware of sharing	Data from retail partners, co-marketing arrangements, trusted partners	Higher – indirect relationship
Third party data	Aggregated from multiple sources	Very low – no direct relationship	Cookie-based behavioral profiles, demographic segments, third party cookies	Highest – no direct consent

The progression from zero-party to third party data represents a shift from explicit permission to passive collection. Zero-party data sits at one end of this customer data spectrum; the customer knows exactly what they’re sharing and why.

The value exchange

Customers don’t share personal information out of generosity. They do it because they expect something valuable in return. The value exchange for zero-party data collected typically includes:

• Better product recommendations When you tell a skincare brand that you have sensitive, dry skin, you avoid getting pitched products with harsh active ingredients. The personalization happens immediately and saves you from wasting money on products that won’t work.
• Fewer irrelevant marketing messages Selecting your communication preferences means you only hear about the product categories you actually care about. Instead of getting every promotion the brand sends, you get curated content matched to your interests.
• Faster, more helpful customer service When your preferences and purchase history are readily available, customer service can solve problems without making you repeat your account details or explain your situation from scratch.
• Early access and exclusive offers Many brands reward customers who complete preference profiles or engage with quizzes by giving them first access to new products or special promotions tailored to their stated interests.

The key to successful zero-party data collection is making this value exchange explicit and immediate. Customers should see the benefit of sharing information right away, not weeks later.

Why zero-party data matters for marketing

Beyond the immediate benefits of personalization and customer experience, zero-party data plays a strategic role in how brands build sustainable marketing strategies. The shift toward explicit consent and transparent data collection addresses several challenges that have plagued digital marketing for years.

Building trust through transparency

The erosion of consumer trust in data collection has pushed brands to rethink their approach entirely. Zero-party data represents a fundamental shift from “take what we can track” to “ask for what we need.”

When customers explicitly consent to share information, they understand the relationship. There’s no hidden tracking, no surveillance-style data collection happening in the background. This transparency builds trust in ways that passive data collection never could.

Brands that prioritize zero-party data signal to customers that they respect boundaries and value explicit permission over opportunistic tracking. In an era where data privacy concerns dominate headlines, this positioning matters.

Personalization without guesswork

Zero-party data allows you to personalize experiences based on what customers actually tell you rather than what you infer from their behavior. This removes uncertainty from personalization decisions.

When a customer explicitly tells you they’re interested in sustainable products, you don’t have to guess based on their browsing patterns. You can confidently feature eco-friendly options in their email campaigns, product recommendations, and website experience.

This directness helps with:

• Immediate tailoring of product recommendations The moment someone completes a style quiz or preference assessment, you can show them products matched to their stated preferences rather than waiting to gather enough behavioral signals.
• More relevant email content and offers Communication preferences let you segment your email list by stated interests, ensuring each customer only receives content about product categories they’ve indicated they care about.
• Better customer service interactions When a customer contacts support, their stated preferences and purchase intentions provide context that helps representatives anticipate needs and offer relevant solutions.
• Reduced waste on irrelevant impressions Knowing what customers don’t want is just as valuable as knowing what they do want. Preference data helps you avoid showing ads for products they’ve explicitly said don’t interest them.

Future-proofing against privacy changes

The trajectory of digital privacy is clear: tracking is getting harder, regulations are getting stricter, and customer expectations are shifting toward explicit consent models.

Third party cookies are disappearing, even with Google’s repeated delays. Apple’s iOS changes have already significantly impaired cross-device tracking. Regulations like GDPR and CCPA establish precedents that other jurisdictions are following.

Zero-party data sits outside most of these restrictions because it’s based on explicit, informed consent. When a customer voluntarily fills out a preference form or completes a quiz, they’re consciously choosing to share that information. This makes zero-party data collection far more resilient to future privacy changes than passive tracking methods.

As behavioral tracking becomes less reliable, brands that have built strong zero-party data collection strategies won’t be starting from scratch. They’ll already have permission-based relationships with their customers and structured ways to gather the information they need.

The limitations of self-reported customer data

Before we discuss a zero-party data strategy, we need to address a critical caveat: people aren’t always accurate reporters of their own behavior or decision-making processes. This happens for several understandable reasons that have nothing to do with dishonesty and everything to do with how human memory and self-awareness work.

• Memory is unreliable. Customers genuinely can’t remember every touchpoint that influenced them. The ad they saw three weeks ago fades from memory, even if it planted a seed. They may remember the last thing they saw before buying, not the first thing that made them aware.
• Social desirability bias. People report discovering brands through “word of mouth” or “research” because it sounds more sophisticated than “I saw an ad.” Admitting that advertising worked feels less autonomous.
• Attribution is complex even for the person making the decision. Your YouTube ad built awareness, but they converted after a Google search weeks later. They think Google “found” them, when really YouTube made them search. The human brain doesn’t naturally track multi-touch attribution.
• Aspirational versus actual behavior. Customers indicate preferences for products they think they should want. Stated purchase intentions don’t always convert to actual purchases. The gap between “I plan to buy organic” and actual shopping cart contents can be significant.

This limitation doesn’t make zero-party data useless, but it does mean you shouldn’t rely on it as your primary measurement tool. When a post-purchase survey says “How did you hear about us?” and 60% of customers say “Google search,” that doesn’t mean your awareness campaigns aren’t working. It means customers remember the search that led to purchase, not the YouTube video that made them aware of your brand three weeks earlier. Zero-party data tells you what customers think happened. Marketing mix modeling shows you what actually drove revenue.

How to collect zero-party data

The method you use to collect zero-party data significantly impacts both the quality of information you receive and the customer experience. The best strategies to collect data feel helpful and engaging rather than intrusive or burdensome.

Interactive quizzes and assessments

Quizzes turn data collection into an engaging experience that provides immediate value. Instead of asking customers to fill out a dry preference form, you guide them through an interactive assessment that delivers personalized results.

• Style finder quizzes help fashion and home goods brands match customers with products that fit their aesthetic preferences. A customer answers questions about their favorite colors, patterns, and styles, then receives curated product recommendations based on their responses.
• Product recommendation tools ask about specific use cases, needs, or constraints to narrow down options. A mattress company might ask about sleep position, firmness preference, and whether customers sleep hot or cold to recommend the right product.
• Needs assessment surveys help complex product categories by asking diagnostic questions. A skincare brand asks about skin type, specific concerns, and current routine to recommend a complete regimen rather than individual products.

The key to effective quizzes is making them feel like a helpful tool rather than a data collection exercise. Customers engage because they want the personalized results, and the zero-party data you collect is a byproduct of that value exchange.

Preference centers

A preference center gives customers explicit control over what they hear from you and how often they hear it. This transparency builds trust while collecting valuable zero-party data about communication preferences.

• Communication frequency preferences let customers choose how often they want to hear from you: daily, weekly, monthly, or only for special occasions. This prevents unsubscribes from customers who like your brand but feel overwhelmed by email volume.
• Channel preferences allow customers to indicate whether they prefer email, SMS, push notifications, or some combination. Respecting these preferences reduces friction and shows you value their time and attention.
• Product category interests help you segment your list so customers only receive promotions for categories they care about. A multi-category retailer might let customers choose between home goods, fashion, electronics, and outdoor gear.
• Content topic selections go beyond products to let customers choose what types of content interest them, like styling tips, care instructions, trend reports, or sustainability initiatives. This is some of the most strategic preference center data you can collect.

Effective preference centers are easy to update and show immediate results. When a customer changes their preferences, the next communication they receive should reflect those changes.

Post-purchase surveys

Post-purchase surveys capture information while the buying experience is fresh in customers’ minds. The most common question—”how did you hear about us?”—provides interesting context about customer perception, though it’s important to remember the limitations of self-reported attribution we discussed earlier.

• Attribution questions ask customers to identify which marketing touchpoint they remember most clearly. While this doesn’t tell you what actually drove the purchase from a statistical perspective, it reveals what felt most influential from the customer’s point of view.
• Product use case questions help you understand how customers plan to use their purchase. This information can inform product development, marketing messaging, and recommendations for complementary products.
• Satisfaction and feedback collected immediately after purchase provides early warning signals about product or service issues before they escalate to public reviews.
• Future purchase intentions indicate what customers might buy next, allowing you to personalize follow-up marketing and understand customer lifetime value potential.

Keep post-purchase surveys short. Three to five questions maximum. Customers are more likely to complete brief surveys, and you can always collect additional information later through other touchpoints.

Progressive profiling

Progressive profiling spreads data collection across multiple interactions rather than asking for everything at once. Each touchpoint requests a small amount of additional information, gradually building a comprehensive customer profile without overwhelming anyone.

• At checkout, ask one question beyond the required transaction fields. This might be “how did you hear about us?” or “what’s the occasion for this purchase?”
• After first use, follow up with a question about the product experience or how it compared to customer expectations.
• At milestone moments, like a customer’s third purchase or six-month anniversary, request information that helps you personalize their long-term experience.

The key is making each request feel appropriate to the moment and limiting yourself to one or two additional questions at a time. Customers tolerate progressive profiling when each ask is small and clearly connected to improving their experience.

Chatbots and conversational interfaces

Chatbots can collect zero-party data through natural conversation rather than formal surveys or forms. This approach feels more engaging and allows for context-aware follow-up questions.

• Natural language collection means customers can describe their needs or preferences in their own words rather than selecting from predetermined options. The chatbot extracts structured data from these conversational responses.
• Context-aware question flows adapt based on previous answers. If a customer indicates they’re shopping for a gift, the chatbot asks different follow-up questions than if they’re shopping for themselves.
• Real-time personalization shows customers immediate benefits from sharing information. As they answer questions, the chatbot narrows down product recommendations or provides increasingly relevant suggestions.

Effective chatbots balance helpfulness with data collection. The primary goal is solving the customer’s problem or answering their question. Collecting zero-party data is a secondary benefit that happens naturally through the conversation.

Loyalty and rewards programs

Loyalty programs create natural opportunities for zero-party data collection by offering clear benefits in exchange for information. Customers understand they’re sharing data to receive rewards, special offers, and personalized experiences.

• Birthday and anniversary data allows you to send timely promotions and gifts that feel personal and thoughtful.
• Lifestyle information helps you segment members by life stage, interests, or values, ensuring the rewards and offers you provide align with their priorities.
• Purchase preferences for rewards let customers indicate which types of rewards they value most: discounts, early access, exclusive products, or experiences.
• Event and milestone tracking captures information about significant moments where your products might be relevant, like weddings, home purchases, or new babies.

The explicit value exchange in loyalty programs makes customers more willing to share information. They understand that providing data helps you reward them more effectively.

Best practices for zero-party data collection

How you collect zero-party data matters as much as what you collect. Following these best practices helps ensure high-quality data while maintaining customer trust.

Make the value exchange clear

Customers need to understand why you’re asking for information and what benefit they’ll receive in return. Vague promises aren’t enough. Be specific about how you’ll use their data.

• Tell customers exactly how you’ll use their information
• Show immediate benefits when possible
• Be transparent about data usage and storage

Making the value exchange explicit respects customer intelligence and builds trust. Customers are more willing to share information when they understand the tradeoff they’re making.

Keep it simple and low-friction

Every additional field or question creates friction that reduces completion rates. Ruthlessly prioritize which information you actually need versus which would be nice to have.

• Don’t ask for everything at once
• Use skip options for non-essential questions
• Mobile-optimize all collection points
• Save progress on longer forms

Reducing friction increases completion rates and improves the quality of data you collect, since customers aren’t rushing through to get it over with.

Use the data you collect

Nothing erodes trust faster than asking for preferences and then ignoring them. If a customer tells you they’re not interested in a particular product category, continuing to email them about it signals that their input doesn’t actually matter.

• Close the loop by showing personalized results
• Reference stated preferences in communications
• Regularly audit whether you’re honoring preferences

When customers see that sharing information leads to tangible improvements in their experience, they become more willing to provide additional zero-party data in the future.

Refresh and update regularly

Customer preferences change over time. Someone who was interested in your budget products when they first discovered your brand might be ready to explore your premium offerings a year later.

• Build in periodic preference review
• Make updating easy and friction-free
• Prompt updates when circumstances change
• Watch for signals that preferences have shifted

Keeping zero-party data current ensures it remains useful for personalization rather than becoming outdated noise in your customer database.

Common pitfalls to avoid

Even well-intentioned zero-party data strategies can fail if they fall into these common traps.

• Asking for too much information upfront. Long forms intimidate customers and reduce completion rates. Collect the minimum necessary information first, then build your dataset over time through progressive profiling.
• Failing to secure explicit consent. Even though zero-party data is voluntarily provided, you still need clear consent language explaining how you’ll use the information. Pre-checked boxes and buried consent language undermine trust.
• Making it hard to update or withdraw consent. If customers can easily provide information but struggle to change or remove it later, you’ve created a dark pattern that erodes trust.
• Ignoring stated preferences in your marketing. This is the fastest way to make customers regret sharing information with you. If someone opts out of a product category and continues receiving promotions for it, they’ll stop trusting your data collection entirely.
• Treating self-reported attribution as ground truth for budget decisions. When customers say they discovered you through Google search, that doesn’t mean your awareness campaigns aren’t working. It means customers remember the search, not the YouTube video that made them aware of your brand weeks earlier and prompted that search.

Where Prescient comes in

Zero-party data is valuable for personalization and understanding customer perspective, but when it comes to making budget allocation decisions, you need measurement that captures what actually drives revenue, not just what customers remember. Your post-purchase survey might show that 60% of customers say they “found you on Google,” but Prescient’s marketing mix modeling reveals that your YouTube awareness campaigns drove the brand awareness that prompted those searches in the first place. The most effective measurement strategies use zero-party data for personalization and customer context while relying on MMM for the unbiased attribution that guides strategic budget decisions.

Prescient’s platform provides this statistical foundation, capturing the full impact of your marketing investments across all channels and showing how awareness campaigns feed into bottom-of-funnel conversions over time. Unlike self-reported attribution that systematically undervalues brand building, Prescient reveals the complete picture of which marketing activities drive your growth, allowing you to make confident budget decisions based on mathematical reality rather than customer memory. Book a demo to see how Prescient combines with your zero-party data strategy to create a complete measurement framework.

Zero-party data FAQs

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

First-party data is behavioral information you observe about customers through their interactions with your brand (website visits, purchase history, email opens, browsing patterns). Zero-party data is information customers proactively and intentionally share with you, like stated preferences, quiz responses, or survey answers. The key difference is that zero-party data requires active customer participation and explicit permission, while first party behavioral data is collected passively as customers engage with your brand through your digital properties.

What are some examples of zero-party data?

Common examples include preference center selections specifying email frequency and product categories of interest, quiz or assessment responses about style preferences or skin type, post-purchase survey answers about product use cases, stated purchase intentions for future buying, product ratings and reviews with detailed feedback, wishlist items showing products customers want, communication channel preferences between email and SMS, and personal context like birthdays or dietary restrictions. Any information a customer deliberately provides rather than information you infer from behavioral data counts as zero-party data.

What is 1st party, 2nd party, and 3rd party data?

First party data is information you collect directly from your customers through your own channels, like your website, app, email, or in-store interactions. Second party data is essentially another company’s first party data that they share with you through a partnership arrangement, like a retailer sharing purchase data with a brand or trusted partners exchanging customer insights. Third party data is aggregated information collected by entities that don’t have a direct relationship with the customer, typically gathered through third party cookies and tracking across multiple websites and sold to advertisers through data brokers.

What is the 3 3 3 rule in marketing?

The 3 3 3 rule suggests that customers need to see your marketing message three times, across three different channels, within three days to move from awareness to action. While this provides a rough framework for thinking about repetition and multi-channel exposure in marketing efforts, modern customer journey patterns are typically more complex and extended over longer time periods than this rule suggests. Marketing mix modeling helps you understand the actual touchpoint patterns and time horizons that drive conversions for your specific brand, rather than relying on general rules that may not reflect your customer behavior.