Despite initially planning to phase out third-party cookies entirely from Google Chrome, the tech giant has pivoted, opting instead to provide privacy-focused alternatives while retaining certain cookie functionalities for now. However, privacy regulations, evolving consumer expectations, and restrictions from other browsers mean businesses must still prepare for a cookieless future.
This listicle explores the eight best alternatives to third-party cookies, offering practical insights to help you maintain targeting precision and measurement capabilities while staying privacy-compliant.
1. First-party data
First-party data refers to information businesses collect directly from their users through interactions on owned channels such as websites, mobile apps, and offline stores. The first-party data originates directly from user engagement, making it accurate, consent-driven, and tailored to the brand’s audience.
Benefits:
- Gained through user consent, enhancing credibility.
- Aligns with privacy laws like GDPR and CCPA.
- Sustainable compared to costly third-party data.
- Drives tailored campaigns, boosting engagement.
Challenges:
- Requires robust optimisation strategies for collection and management.
- Limited to users already interacting with your brand.
Businesses can track user behaviour through website analytics platforms, gather valuable insights via surveys and feedback forms, or analyse transactional data such as purchase history and order frequency.
Example: An e-commerce store using first-party data such as search history (within its platform) to personalise product recommendations.
2. Zero-party data
Zero-party data refers to information that users explicitly share with businesses. This data includes preferences, purchase intentions, and personal context, as well as how users want to be recognised and engaged. Unlike inferred data, zero-party data is direct, eliminating guesswork.
Benefits:
- Highly specific, user-consented data enhances trust and enables personalised ad campaigns.
- Ideal for aligning with identity solutions in privacy-focused ecosystems.
Challenges:
- Relies on active participation, limiting scale.
- Requires creativity to collect data through interactive tools.
Use surveys, quizzes, preference centres, and opt-ins with incentives to gather user insights transparently and build trust.
Example: A music app uses zero-party data collected through a survey asking users about their favourite genres and moods, then curates personalised playlists based on their responses.
As per a study on data-based personalisation, First-party data emerged as the most valuable data type for advertisers, contributing 43.3% to personalization levels in advertising, compared to 33.9% for third-party data and 22.0% for zero-party data.
First-party data delivers the most balanced performance, with a significant boost in ad clicks at moderate personalization levels before tapering off. Zero-party data appears less effective for higher personalization levels, potentially due to limitations in scope and applicability.
This suggests that while personalization is valuable, its success depends on the appropriate use of data types, with first-party data emerging as the most reliable and privacy-compliant option for sustainable ad performance.
3. Contextual targeting
Contextual targeting or contextual advertising is a digital advertising method where ads are placed on web pages relevant to the page’s content. This approach ensures your ads appear in front of the target audiences already engaged with topics related to your products or services, increasing the likelihood of capturing their interest.
Contextual targeting, supported by AI-driven algorithms, is seen as privacy-friendly and capable of enhancing brand metrics. Advertisers report significant increases in KPIs like ad recall, brand affinity, and purchase consideration—on average, twice as much as other methods [Source].
Benefits:
- Ads align with user interests, boosting engagement.
- Doesn’t rely on cookies, ensuring compliance with laws like GDPR.
- Drives traffic and sales by appearing in relevant contexts.
Challenges:
- Accuracy relies heavily on the quality of algorithms analysing webpage content.
- Requires advanced tools for optimisation and content analysis.
Advertisers define keywords or topics, algorithms match ads to relevant content, and ads are placed on suitable pages, increasing visibility and impact.
Example: An ad for travel essentials placed within a travel blog.
4. Google’s Privacy Sandbox
Google’s Privacy Sandbox is a suite of privacy-focused tools and APIs designed to protect user privacy while maintaining a functional advertising ecosystem. Key features include alternatives like Topics API for interest-based targeting and Privacy Sandbox for Android, which replaces device-level identifiers with privacy-compliant solutions.
Benefits:
- Enhances user privacy by reducing reliance on third-party cookies and identifiers.
- Provides APIs for targeted advertising without tracking individuals.
- Aligns with regulatory standards, fostering compliance.
Challenges:
- Requires large-scale adoption across the adtech ecosystem for effective implementation.
- Raises concerns about technical complexity and its impact on smaller players in the ad industry.
- Ongoing regulatory scrutiny from bodies like the UK’s CMA adds uncertainty.
You can leverage Privacy Sandbox APIs like Topics for interest-based advertising, and Protected Audiences for retargeting campaigns, while exploring Privacy Sandbox for Android to manage advertising on mobile platforms.
Example: A retailer uses the Topics API to target ads for eco-friendly products to users assigned an interest in sustainability, based on their browsing habits.
5. Universal IDs
Universal ID solutions, or identity solutions, are emerging as a key replacement for third-party cookies. These systems assign users a persistent ID based on attributes like email addresses or device details, enabling cross-platform tracking and better ad targeting in a privacy-compliant manner.
Benefits:
- Aligns audience data for better identity resolution.
- Supports precise ad targeting and segmentation.
Challenges:
- Adoption requires collaboration across the adtech ecosystem.
- Effectiveness depends on integration with other systems.
- Relies on industry-wide adoption to achieve full potential.
You can partner with The Trade Desk or similar providers to use Universal IDs. Integrate Unified ID 2.0 or hybrid solutions like ID5 to align data, target users, and measure performance across platforms.
Example: An online retailer using universal identifiers to deliver personalised ads based on email-verified preferences.
6. Device fingerprinting
Device fingerprinting, or browser or machine fingerprinting, is a tracking technique that identifies a device based on its unique configuration. Unlike cookies, which are stored client-side, device fingerprints are stored server-side, offering a persistent way to track users across sessions and platforms.
Benefits:
- Unlike cookies, fingerprints remain effective even when cookies are deleted or blocked.
- Links user activities across devices, improving tracking accuracy.
- Identifies anomalies in online transactions, aiding in combating e-commerce and financial fraud.
Challenges:
- Raises ethical questions due to covert tracking.
- Increasingly countered by browser updates to protect user anonymity.
Implement JavaScript trackers to collect data points such as IP address, browser type, screen resolution, and installed plugins. Use the combined data to generate unique identifiers for tracking and analysis.
Example: A bank uses device fingerprinting to detect fraud by identifying an unusual device during account logins, triggering additional verification to protect users.
7. Data clean rooms
Data clean rooms are privacy-focused platforms enabling brands and advertisers to analyse data for ad targeting, campaign performance, and attribution without exposing user-level information. They securely match first-party data from multiple sources, ensuring compliance with privacy regulations like GDPR.
Benefits:
- Protects user data through encryption, pseudonymisation, and aggregation.
- Provides insights across platforms without compromising personal data.
- Maintains ownership of data while enabling collaboration with other parties.
Challenges:
- Requires data to be standardised before uploading.
- Limited interoperability across platforms (e.g., Google Ads, Amazon).
- Aggregated data may lack granularity.
Data clean rooms are usually implemented by uploading first-party data, which is then encrypted, pseudonymised, and aggregated to ensure privacy. The platform matches this data with others to identify shared audiences and provides aggregated insights.
Example: An advertiser refining its ad campaigns by combining clean room data with CRM insights.
8. Identity resolution
Identity resolution is compiling customer data from various sources to create a unified profile. It relies on an identity graph that connects and reconciles customer data from multiple sources. Identity resolution offers an alternative for tracking, personalising, and improving customer interactions across devices and channels.
Benefits:
- Tracks customers even if devices or identifiers change.
- Fills gaps with second- and third-party data.
- Uses anonymised identifiers to meet data protection regulations.
- Builds accurate models for targeting similar audiences.
Challenges:
- High costs for data integration and management.
Identity resolution combines data from first, second, and third-party sources into a unified customer profile. It uses deterministic matching, which links exact identifiers like emails or phone numbers for high accuracy, and probabilistic matching, which estimates matches based on attributes like IP addresses or device types, providing broader reach with less precision.
Example: A brand links a customer’s email from a purchase with their website and app activity using identity resolution, enabling personalized recommendations.
The evolving role of third-party cookies in a privacy-first era
As digital advertising evolves, third-party cookies remain essential, even as their role diminishes. While Safari and Firefox block them by default, Google Chrome’s delay in deprecation highlights their continued value for tracking, personalisation, and measurement.
This decision offers the industry time to refine alternatives like Google’s Privacy Sandbox while still benefiting from cookies’ reliability in optimising campaigns. It also underscores the importance of first-party data, contextual targeting, and cookie consent platforms to balance personalisation with privacy.
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The cookieless future is on the horizon, but third-party cookies still act as a bridge to get there.
There is no one-size-fits-all replacement for third-party cookies. Instead, a combination of strategies is being implemented to balance user privacy and effective marketing:
- First-party data: Businesses collect data from users via their platforms, ensuring accuracy and compliance.
- Zero-party data: Voluntarily shared information provides highly specific insights into user preferences and intent.
- Contextual targeting: Aligns relevant ads with webpage content, avoiding the need to track individual users.
- Google’s Privacy Sandbox: Introduces APIs like Topics to enable privacy-first targeting.
- Universal IDs: Aggregate user data into cross-platform identifiers for cohesive targeting.
Several technologies mimic the functionality of cookies, particularly for tracking and user identification:
- First-party cookies: Allow websites to remember user information within the same domain.
- Device fingerprinting: Tracks users by analysing unique device attributes like screen resolution and browser type.
- Universal IDs: Act as cohesive identifiers across devices and platforms.
- Zero-party data: While not a direct replacement, it provides voluntary, high-quality insights about user preferences.
- Local storage: Stores data in the user’s browser, allowing session and state management.
Google initially planned to phase out third-party cookies completely but has opted for a balanced approach. Its Privacy Sandbox introduces several APIs to provide privacy-safe solutions for advertisers:
- Topics API: Groups users into interest-based cohorts for targeting instead of tracking individuals.
- Attribution Reporting API: Measures ad performance without exposing individual user data.
- Other tools: Designed to enable privacy-compliant tracking and measurement across websites.
Several technologies can replace cookies for user authentication, ensuring a secure and good user experience:
- First-party cookies: Still relevant for session management and remembering login credentials.
- OAuth tokens: Provide secure authentication and authorisation across platforms.
- Zero-party data: Preferences and user-provided information can facilitate secure and consent-based login processes.
- Biometric systems: Integrate with apps and devices to offer seamless, cookie-free verification.
- Local storage: Securely stores session information on the user’s web browser.
A range of technologies is emerging to replace cookies collectively, each addressing different marketing needs:
- First-party data: Forms the foundation for compliant data strategies.
- Zero-party data: Provides high-quality, consented information for personalisation.
- Contextual targeting: Matches ads to content rather than user behaviour.
- Universal IDs: Enable cross-platform identification and targeting.
- Data clean rooms: Facilitate privacy-safe collaboration for audience insights and campaign measurement.
- Google’s Privacy Sandbox: Balances privacy and functionality with tools like Topics.
Shreya is the Senior Content Writer at CookieYes, focused on creating engaging, audience-driven blog posts and related content. Off the clock, you’ll find her happily lost in the world of fiction.
