First-party data is data collected directly by a brand or retailer through its own channels, such as e-commerce sites, apps, loyalty programs, point-of-sale (POS) systems, call centers, or newsletters. It includes behavioral data (e.g., products viewed, clicks, purchase history) and transactional data that can be linked to a specific customer based on consent and an identifier.

Zero-party data, first-party data, third-party data—terms

Zero-party data is data that a customer provides knowingly and intentionally, such as shopping preferences, interests, needs, communication frequency, sizes, and goals (e.g., “I want to lose weight,” “I’m looking for a gift”). It typically appears in quizzes, configurators, account settings, wish lists, or brief post-purchase surveys. Its value lies in the fact that it describes the “why” and “for what purpose,” rather than just “what happened.”

First-party data is information that a brand collects directly from customers through its own channels and sources. Thanks to this direct relationship with the user, the data becomes an invaluable, highly reliable, and valuable metric in the context of digital marketing. Where does it come from? It includes, among other things, collected transactional data, behavioral data from web analytics, information from contact forms, and newsletter sign-ups. Understanding and utilizing first-party data is the foundation for personalizing marketing offers and effectively managing customer relationships. A better understanding of the audience’s needs and preferences allows for effective tailoring of the offer.

Third-party data refers to data sourced from external entities and used for targeting outside of a company’s own channels (e.g., within the advertising ecosystem). In practice, this data was often built on tracking user behavior across websites, which is now increasingly restricted by browsers and regulations.

Is the end of third-party data approaching?

The phase-out of third-party cookies is changing not only marketing tools but also the logic behind building a competitive advantage. Broad-reach targeting based on external data is gradually giving way to an approach in which relationships, consent, and the quality of a company’s own data sets become key. In practice, this means fewer shortcuts and more groundwork: from data strategy, through customer experience, to analytical operations.

At the same time, retailers are becoming increasingly important as owners of shopping environments and transactional data. Retail media and data-driven partnerships are becoming a viable alternative to some activities that previously relied on third-party cookies—but only if there is a mature approach to governance, measurement, and data integration. This article explains what first-party and zero-party data are and how brands and retailers can turn them into measurable business value.

What exactly does the end of third-party cookies mean, and why is it strategically important?

For years, third-party cookies have enabled user recognition across domains, audience segmentation, and attribution on the open web. Restrictions on these cookies in browsers and stricter privacy requirements mean that many existing targeting and measurement methods are losing their effectiveness or becoming more difficult to keep compliant with regulations.

This change is not merely a technical issue. It’s a shift in focus from purchased and third-party data to data collected directly—where there is a relationship with the customer, context, consent, and the opportunity to create a valuable exchange: the customer shares information and, in return, receives a better experience, recommendations, an offer, or service.

As a result, the role of data strategy as part of the go-to-market strategy is growing. For many organizations, this is the moment when they need to translate the post-cookie shift into concrete decisions regarding MarTech and analytics architecture: where the customer profile is built (CRM/CDP), how consent management (Consent Management), how events and transactions are collected from first-party channels (tagging, event streaming, POS/e-commerce), and where data is consolidated and used operationally (data warehouse/lakehouse, attribution models, segmentation).

At the same time, there is a need to clarify the operational model: who owns the data and metric definitions, how quality control is handled (ID consistency, deduplication, profile completeness), what the standards are for activating segments across channels (marketing automation, email/SMS/push, onsite personalization), and how to measure the impact of campaigns without relying on cross-domain tracking. In other words, this is not just a matter of compliance and privacy, but a practical redesign of the MarTech stack and analytical processes to align with the realities of first-party and zero-party data.

First-party and zero-party data: How do they differ, and how can we understand them in practice?

First-party data is information collected directly by a brand or retailer through its own channels. It may include behavior on the website and in the app, transaction history, interactions with customer service, responses to campaigns, or data from a loyalty program. Its strength lies in the ability to link it to the business context and the relatively high level of control over quality and consent.

Zero-party data is data intentionally provided by the customer—for example, preferences, needs, and statements regarding interests, purchasing plans, or expectations regarding communication. What sets it apart is transparency and intentionality: the customer knows they are sharing information, and the organization should clearly explain how it will be used.

In practice, both categories work best together. First-party data helps us understand actual behavior, while zero-party data can accurately reveal motivations that aren’t visible in logs or shopping carts. The greatest value is created when an organization knows how to combine both types of data into coherent profiles and action scenarios, rather than treating them as separate initiatives.

How can brands build a competitive advantage using first-party data without risking overspending their budgets?

The fundamental mistake is starting with the tool rather than with the business decisions that the data is meant to support. First-party data should address specific goals: improving conversion rates, increasing purchase frequency, reducing churn, boosting average order value, better tailoring offers, or more reliably measuring marketing effectiveness.

A good starting point is an audit of data sources and consent acquisition processes. It is important to identify which data is reliable, where gaps exist, how customer identifiers are standardized, and whether the organization is able to link online and offline data. Only on this basis can you design use cases that can be implemented iteratively and have their impact measured.

What yields quick results? In practice, it’s worth focusing on:

• standardizing customer identity across our own channels;
• implementing effective incentives for logging in and maintaining a session;
• designing communication based on events and intentions, rather than just demographics;
• improving the quality of product data and content so that recommendations work better;
• developing a measurement framework in a mixed-methods model by combining experiments, modeling, and sales data.

How are retailers becoming data-driven, and what does this mean for brands?

Retailers have a unique resource at their disposal: transaction data, information on availability, prices, promotions, and real-world shopping context. This makes retail media and data collaboration attractive to brands, as they allow brands to reach customers close to the point of purchase and measure the impact on sales more directly than on many open channels.

At the same time, pressure for standardization is growing: comparability of results, definitions of metrics, transparency in attribution, and rules for data usage. Retailers who invest in data quality, governance, and their advertising offerings can monetize attention and data, but they must maintain a balance between advertising revenue and the user experience.

For brands, this means they need to develop the expertise to work with retailer data, integrate it with their own resources, and negotiate terms of cooperation. In practice, decision-making models are also needed: which categories and campaigns to run in Retail media, how to measure incremental impact, and how to avoid sales cannibalization.

How can you collect zero-party data without compromising the customer experience?

Zero-party data isn’t collected through forms just for the sake of collecting it. Customers are more willing to share information when they see an immediate benefit and feel in control. Instead of long surveys, short interactions embedded in the purchase journey, after-sales service, or a loyalty program work better.

It’s worth designing mechanisms that naturally generate preference statements. These can include product-matching quizzes, configurators, wish lists, style selectors, smart filters, availability notifications, as well as communication settings where the customer chooses the frequency and topics of contact.

Examples of design principles that reduce friction include:

• asking one question at a time and using the answer immediately in the experiment;
• using language that highlights the benefits and providing simple explanations of the purpose of data processing;
• allowing users to edit their preferences and easily withdraw their consent;
• avoiding questions that cannot be translated into specific actions;
• testing whether a particular information collection point reduces conversion rates.

Is it possible to maintain effective marketing measurement without third-party cookies?

Effective measurement is possible, but it requires a change in approach. Simple attribution models based on single user identifiers are becoming less reliable. Methods based on first-party data, experiments, and triangulation of sources are becoming increasingly important.

How does this work in practice? In many organizations, the role of incremental testing, cohort analysis, marketing mix modeling, and approaches based on events and customer segments is growing. It’s also becoming crucial to clarify KPI definitions: not just clicks and customer acquisition cost, but also margin, retention, lifetime value, and impact on category penetration.

Research conducted by organizations such as the Hume’s Institute will be helpful in Polish analyses. Such sources can support the calibration of assumptions and the interpretation of trends, particularly when decisions require comparing internal data with the broader market context.

What technological and organizational foundations are needed for data to actually work?

Technology without processes and accountability does not provide a competitive advantage. More often than not, the obstacle is not a lack of tools, but rather scattered data, inconsistent identifiers, limited quality of product attributes, and a lack of common standards across marketing, sales, and IT.

For many companies, the turning point comes when they establish clear data management rules: who owns a given dataset, what the quality control process looks like, what the access policies are, and how to measure the business value of the data. It is also important to create a map of use cases and a development backlog so that investments in the data stack are aligned with business priorities.

What does a practical set of fundamentals include? The top 5 are:

1. A consistent customer identifier and profile merging rules;
2. Compliance with privacy requirements and transparent consent management;
3. Organized tagging of events in digital channels;
4. A data model that combines behavior, transactions, and offer context;
5. Analytical processes focused on decision-making, not just reporting.

How can you leverage first-party and zero-party data to gain a competitive advantage in your strategy?

The competitive advantage does not stem from simply having data, but from the speed at which organizations learn and implement actions. Organizations succeed when they can shorten the time from signal to decision: from identifying a segment, through preparing an offer, to executing it across channels and verifying the results.

In practice, it’s worth thinking of data as an element of category and channel strategy. For brands, this means better product portfolio management, personalization across their own channels, more precise promotional planning, and more effective partnerships with retailers. For retailers, this means optimizing product assortments, pricing, and promotions based on demand data, as well as creating more valuable advertising environments.

In this context, it is important that decisions be grounded in market realities. PMR Market Experts publishes regular reports that help shed light on the dynamics of categories, channels, and buyer behavior. Depending on your needs, industry reports and market forecasts can serve as a starting point, followed by the refinement of actions through a project-based approach.

Key Findings

The shift following the end of the third-party cookie era rewards organizations that invest in customer relationships, data quality, and measurement. An iterative approach yields the best results: first, business priorities and use cases; then, architecture and processes.

• The value of first-party data increases when it is integrated into sales and marketing decisions;
• Zero-party data works best when the customer sees an immediate benefit and has control;
• Retail media strengthen the role of retailers, but require measurement standards and transparency;
• Measurement without third-party cookies is possible, but it is more often based on experiments and modeling;
• Competitive advantage stems from rapid learning and implementation, not from the sheer scale of the data sets.

How can you accelerate the implementation of a data strategy within your organization?

It’s a good idea to start the implementation with a brief assessment of data maturity and measurement, followed by the selection of a few scenarios with the greatest impact on financial results. Next, you should plan a joint roadmap for marketing, e-commerce, sales, and IT that clearly outlines the interdependencies: consent, customer identification, integrations, metrics, and testing.

In many cases, it is also crucial to clarify the terms of the brand-retailer partnership: segment definitions, data usage policies, the billing model, and the method for measuring incremental impact. Such topics typically require analytical and strategic support to avoid discrepancies between claimed effectiveness and the actual impact on sales. In this area, consulting that combines a market perspective with an operational approach to data, measurement, and prioritization can be helpful.

If you’re planning decisions regarding first-party and zero-party data strategies, Retail media development, or a shift in your approach to measuring effectiveness, it’s a good idea to consult with experts on the direction of your efforts and base them on market benchmarks.

PMR Market Experts offers consulting services to help business owners, management teams, and investors assess potential, analyze trends, design portfolios, and develop strategies and product differentiation. Our expertise allows us to translate post-cookie trends into specific priorities, a roadmap, and success criteria.

Please feel free to contact us

Hume’s Institute stands for knowledge. PMR Market Experts stands for experience. When we join forces, you gain a competitive edge. See how we can support your business.

Hume’s Institute stands for knowledge. PMR Market Experts stands for experience. When we join forces, you gain a competitive edge. See how we can support your business.

Dariusz Śmiejkowski

Chief Executive Officer