What is first-party data?

First-party data is information a brand collects directly from its own audiences across its owned touchpoints. That includes website and app activity, purchase history, email engagement, customer support interactions, loyalty program activity, and transaction data collected with consent.

For marketers, first-party data matters because customers expect personalized, consistent, real-time experiences, and those moments depend on high-quality customer data.

As expectations rise and third-party signals fall away, first-party data can give teams a foundation for understanding customer behavior, building customer profiles, and generating audience insights they can act on.

The better your first-party data, the more data agile you can be too—getting the right data into the right systems, fast enough to power relevant customer experiences across channels.

TL;DR

First-party data is information collected by brands directly from their own customers.

Understanding first-party data

First-party data is information you collect directly from your audience through your own channels and touchpoints. It reflects actual customer behavior based on how people interact with your brand.

You can collect first-party data from sources like:

• Your website and mobile app
• Email, SMS, push, and in-app engagement
• Purchases, subscriptions, renewals, and returns
• Loyalty and membership activity
• Customer support interactions and feedback
• Account activity and opt-in status

Because it's collected directly with customer consent and knowledge, first-party data is more reliable and builds trust while supporting personalization.

Why is first-party data important?

First-party data is important because it gives marketers a direct line to customer behavior and preferences. Without it, teams end up filling gaps with assumptions, which can drag down engagement and make it harder to build durable customer relationships.

Here are two reasons first-party data has become non-negotiable:

• The decline of third-party cookies and identifiers: As browsers and platforms restrict tracking, third-party signals become less dependable for audience building, attribution, and activation.
• Reliable inputs for personalization and AI: Personalization and AI rely on accurate customer profiles and audience insights. First-party data reflects real customer interactions, which supports more relevant customer experiences across channels.

What’s the difference between first-party data, second-party data, third-party data, and zero-party data?

These terms describe where data comes from, and how directly it’s tied to a customer relationship.

• First-party data is collected by your brand through your own channels and direct interactions. It reflects how people engage with your experiences, and it can include behavioral, transactional, and engagement data tied to known or anonymous users.
• Second-party data is someone else’s first-party data that you access through a direct partnership. For example, a publisher may share audience segments with an advertiser, or a brand may share data with a retail partner as part of a co-marketing agreement.
• Third-party data is aggregated by an external provider from multiple sources, then sold or licensed. Because it’s collected indirectly, it’s often less transparent, harder to validate, and less durable as rules and platform changes limit tracking.
• Zero-party data is information a customer intentionally shares, like preferences, product interests, or goals. It's typically captured through preference centers, quizzes, surveys, and interactive experiences where the value exchange is explicit.

First-party data is the most durable category because it comes from direct engagement and can be collected with clear consent and stronger governance.

Benefits of first-party data

First-party data offers several key benefits that can significantly improve your marketing efforts:

1. More relevant customer engagement

First-party data comes from direct interactions with your brand, making it more reliable than aggregated or inferred data. You can segment audiences with precision, identify high-value customers, and understand preferences based on actions rather than assumptions. A streaming service suggests unwatched content based on viewing history. An eCommerce store surfaces products that complement past purchases. This relevance increases click-through rates, conversions, and customer satisfaction.

2. Better marketing efficiency

Identify which channels and campaigns generate actual results by tracking performance against customer behavior. Shift budget toward tactics that drive conversions, suppress audiences who've already purchased, and stop investing in strategies that don't deliver measurable returns. First-party data helps you build and refresh audiences using real-time engagement signals, reducing wasted spend on paid media and improving overall ROI.

3. Stronger customer trust and retention

When collection is transparent and customers see the value—faster checkouts, better recommendations, fewer irrelevant messages—they're more willing to engage. This foundation of trust strengthens relationships, reduces churn, and increases lifetime value. Brands that prioritize transparency in their data practices report stronger customer retention and higher lifetime value.

4. Clearer attribution and optimization

Track customer journeys using data you control. Understand which touchpoints influence decisions, measure campaign impact accurately, and optimize based on verified outcomes rather than estimated attribution. Activate data in real time to send timely messages—abandoned cart reminders within minutes, welcome series triggered immediately after sign-up, or re-engagement campaigns based on recent inactivity. This responsiveness creates momentum and keeps customers moving forward.

How to collect first-party data

Collecting first-party data requires transparency and mutual value. When customers understand what you're collecting and how it benefits them—personalized recommendations, smoother experiences, relevant content—they share willingly. This consent-first method creates a unified approach and builds trust as well as delivering accurate data.

Before collecting data:

• Regularly update and clean your database to maintain accuracy
• Obtain explicit consent and follow GDPR and CCPA requirements
• Integrate data sources through direct connections to your data warehouse and digital properties

Four steps of first-party data collection

1. Your owned digital platforms

Your website, mobile app, and product track user behaviors like page views, click-through rates, session duration, and feature usage. This data reveals preferences and patterns that inform relevant marketing.

2. Direct customer interactions

Capture behavioral data from customer touchpoints like account creation, login frequency, feature usage, and support interactions. Track how customers engage with your emails, push notifications, and in-app messages to understand communication preferences and response patterns.

3. Transaction and commerce data

Track purchase histories, order frequencies, average order values, and product returns. This behavioral data powers personalized product recommendations and targeted promotions.

4. Social media and community engagement

Connect your social platforms with your website to collect data on likes, shares, comments, and engagement patterns. This helps refine your social strategy based on actual audience response.

How a well-executed first-party data strategy can enhance your marketing

First-party data's true power emerges when you activate it across every customer touchpoint. The brands seeing the strongest results use their data to orchestrate connected experiences—coordinating messages across email, push notifications, SMS, in-app, and more based on real-time customer behaviors.

Here's how leading marketers turn first-party data into cross-channel engagement that drives results.

Personalization of web and mobile experiences

First-party data transforms generic browsing into relevant experiences tailored to individual preferences and behaviors.

Use case 1: Product recommendations based on purchase history

Show customers complementary products based on what they've already bought. Someone who purchased running shoes sees matching athletic wear during their next visit. This approach gives the customer the right products at the right moment, increasing average order value without feeling pushy.

Use case 2: Dynamic content across channels

Personalize messages across email, push, and in-app based on individual behavior. A travel app sends destination recommendations via email, follows up with flight deal notifications, and displays personalized trip planning in-app—all coordinated around each customer's search and booking history. This creates a cohesive experience that feels responsive, not repetitive.

Advertising

First-party data makes paid media more precise and cost-effective by targeting the audiences most likely to convert.

Use case 1: Retargeting campaigns

Retarget customers who browsed specific products but didn't purchase by syncing behavioral segments to platforms like Facebook and Google. Coordinate these ads with email reminders to reinforce the message across channels, turning interest into action without wasting spend on customers who already converted.

Use case 2: Lookalike audience targeting

Build lookalike audiences based on your highest-value customers—those with strong purchase frequency and lifetime value. A meal kit service, for example, can reach new prospects who mirror their most engaged subscribers, reducing acquisition costs while improving conversion rates.

Customer retention

First-party data helps you identify at-risk customers and re-engage them before they churn.

Use case 1: Personalized loyalty programs

Create targeted loyalty journeys based on purchase history and engagement patterns. A coffee chain automatically rewards a customer's fifth purchase with a free drink offer via push notification. If unredeemed after a week, a follow-up email with a bonus offer drives them back. This strengthens habit formation and increases visit frequency.

Use case 2: Churn prevention campaigns

Identify customers who haven't purchased in 60 days and re-engage them with automated journeys that start with personalized email, escalate to push notifications with special discounts, and finish with SMS offers for free shipping. Each touchpoint reflects their preferences and favorite categories, making the outreach feel relevant rather than desperate.

Improved user experience

First-party data allows you to personalize every interaction, creating smoother, more intuitive experiences that keep customers engaged.

Use case 1: Personalized onboarding flows

Tailor onboarding based on preferences collected during sign-up. A language-learning app adjusts the first session for someone learning "Spanish for travel" to feature relevant vocabulary instead of generic lessons. Follow-up emails with personalized study tips and push reminders to complete their first lesson keep momentum going, boosting first-week engagement and long-term retention.

Use case 2: Adaptive interface experiences

Adjust what customers see based on browsing behavior. A fashion retailer's app notices frequent handbag views and highlights new arrivals in that category on the homepage. When the customer abandons their cart, coordinated push and email messages showcase a limited-time offer on their selected item. This reduces friction and increases conversion without manual intervention.

User acquisition

First-party data helps you identify and reach high-potential prospects across multiple channels.

Use case 1: Personalized referral programs

Segment customers by engagement and purchase frequency to create tailored referral incentives. Weekly orderers receive a "Give $20, Get $20" offer via email and in-app, while occasional customers get a double-reward to encourage both referrals and their own re-engagement. Automated follow-ups for those who haven't shared yet increase participation and boost customer lifetime value.

Use case 2: Cross-channel acquisition journeys

Guide new users toward conversion with coordinated free trial campaigns across email, push, and SMS. Start with an email highlighting premium features. If they don't sign up, a push notification reminds them of the offer. Still hesitant? An SMS with a first-month discount closes the deal. This multi-touch approach increases sign-up rates while the engagement data refines future acquisition efforts.

A/B/n testing

First-party data enables you to experiment with different approaches and optimize based on what actually drives results.

Use case 1: Message content optimization

Test messaging elements—tone, CTA placement, imagery—to discover what resonates. A meditation app compares "Feeling stressed? Take a deep breath" versus "Your daily moment of calm is ready" in push notifications. The softer tone drives higher open rates and session completions, insights that then improve performance across email and in-app messaging.

Use case 2: Channel performance testing

Compare communication channels to identify the most effective approach. A grocery delivery app tests re-engaging inactive users with push notifications offering "10% off" versus in-app messages promoting "free delivery." Push drives more reactivations while in-app increases average order value, allowing the brand to optimize future marketing campaigns based on specific goals.

AI decisioning

AI decisioning uses first-party data to make 1:1 choices about the experience each customer receives next, optimizing decisions like message, offer, channel, timing, and frequency based on real customer responses.

Use case 1: Offer selection that adapts to each customer

Instead of picking one offer for everyone in a segment, use AI decisioning to test multiple offer options and learn which one each person is most likely to engage with. A retail or subscription brand can rotate offers across customers, then automatically favor the option that drives the outcome you care about, like conversions, renewals, or repeat purchases.

Use case 2: Coordinated channel, timing, and frequency decisions

Use first-party engagement signals to decide how a customer should be contacted next, and when. For example, if someone regularly clicks email but ignores push, the experience can shift toward email-first outreach, with fewer interruptions elsewhere. Over time, the model learns which combinations drive results while staying aligned with consent and channel preferences.

First-party data examples

When brands activate first-party data strategically, the results speak for themselves. Here are three companies that turned customer insights into measurable business impact.

First-class strategy: ClassPass boosts conversions and saves time with smarter ad targeting

ClassPass connects fitness enthusiasts with classes and wellness experiences through a single subscription app. As they scaled, they needed a way to optimize marketing spend and create more cohesive customer acquisition campaigns.

The challenge

ClassPass's process for targeting paid social ads was manual and time-consuming. The team spent hours each week creating static audience lists for Facebook, Google, and TikTok, leaving room for errors and missed opportunities.

The strategy

Using Braze Audience Sync, ClassPass automated how they synced real-time customer behavior and first-party data with their paid social platforms. This eliminated manual processes and dynamically refined audience targeting based on actual customer actions.

The results

• 2% lift in TikTok conversion rates
• 6 hours saved per month across all social channels
• Improved campaign performance and team productivity through efficient first-party data activation

Produce and personalize: Mon-marché.fr drives 43% order increase through seamless data activation

Mon-marché.fr delivers market-fresh groceries to busy Parisians 15 hours a day, seven days a week. To keep customers engaged and drive repeat orders, they needed better access to customer preferences and behavioral data.

The challenge

Getting valuable customer data from external tools like surveys and customer service platforms into their marketing system required technical resources and created delays. The manual process limited how quickly they could personalize campaigns.

The strategy

With Braze Data Transformation, Mon-marché.fr automated data imports from sources like Typeform and Zendesk. This gave their marketing team direct access to customer preferences, NPS scores, and purchase patterns without waiting for technical support—enabling personalized order reminder campaigns tailored to each customer.

The results

• 43% increase in orders
• 21% push notification open rate
• Faster campaign execution without technical bottlenecks

Waste not, want not: Too Good To Go's personalized approach doubles conversion rates

Too Good To Go fights food waste by connecting users with "Surprise Bags" of surplus food from restaurants and stores at discounted prices. With limited inventory at each location, they needed to match the right customers with the right offers at the right time.

The challenge

Despite high app engagement, many users weren't making purchases because they couldn't find relevant Surprise Bags nearby. Generic notifications led to messaging fatigue and customer frustration.

The strategy

Too Good To Go segmented users by preferences, app behavior, and purchase history using first-party data. They created automated campaigns with Braze Catalogs that dynamically personalized recommendations based on real-time supply, location, and individual customer profiles.

The results

• 135% increase in purchases attributed to customer engagement campaigns
• 2X increase in message conversion rates
• Reduced food waste while dramatically improving campaign ROI