Cookieless Tracking Solutions: Top Methods and Tools to Reduce Cookie Dependence

Third-party cookies have been the cornerstone of connecting the dots between ads, websites, users, and conversions for years. That era of unrestricted third-party tracking is ending, and that’s a good thing. The goal isn’t to eliminate tracking cookies entirely, but to regain control by relying on first-party cookies and privacy-safe data flows you actually own. 

This shift away from third-party cookies forces brands to rethink how to collect, process, and attribute data in a privacy-first world. 

But cookieless doesn’t mean clueless. From server-side tracking to first-party IDs and contextual targeting, new solutions are replacing third-party cookies and delivering more accurate, compliant, and resilient insights.

With this article, we want to help you understand differences between tracking methods that make you rely less on third-party cookies; how they work and where each fits in a modern data strategy. You’ll also see why server-side tracking has become the foundation of cookieless measurement, and how server-side tracking tools like Tracklution make adopting it radically simple.

Overview: Key approaches to reduce cookie dependence

There’s no single replacement for cookies and certainly no one-size-fits-all cookieless tracking solution.

Instead, different technologies play different roles in helping you rely less on cookies. Some manage how data is collected and measured, while others shape how ads are targeted and attributed.

Not all cookies are bad. First-party cookies (those set under your own domain) remain essential for user experience and accurate session tracking. The real goal is to remove third-party cookie dependency, not all cookies.

From server-side tracking and other first-party data strategies to probabilistic modeling and contextual targeting, each approach has its own strengths, trade-offs, and best-use cases. 

The table below summarizes where these methods fit in, and what you should know about things like accuracy, compliance, scalability, and setup complexity.

Does your business need a server-side tracking tool?

The short answer? Most likely yes.

Server-side tracking doesn’t mean going completely cookieless. Instead, it helps you minimize dependence on cookies by keeping tracking logic and data collection under your control, and moving data storing and processing to a server rather than keeping it in the browser. You can still use first-party cookies responsibly to connect sessions and measure conversions, without handing data to browsers or ad networks unnecessarily.

Among all cookieless tracking methods, server-side tracking stands out as the foundation. This layer supports accurate measurement, privacy compliance, and long-term data control. Other methods like first-party IDs or probabilistic modeling can extend its capabilities, but without a reliable server-side setup, everything else risks being built on shaky ground.

You’ll benefit from a server-side setup if:

• Your conversion data looks inconsistent between platforms (Meta, Google Ads, GA4)
• You’re losing signals because of browser restrictions or ad blockers
• You handle customer data in multiple regions and need tighter privacy compliance
• You want full control over what data gets shared with each marketing or analytics platform

Server-side tracking isn’t just a technical upgrade: it’s how modern marketing teams protect data accuracy and stay ahead of privacy shifts. If clean, reliable performance data matters to your business, a server-side tracking tool is no longer optional.

Deep-dive: Tracking methods that help you go cookieless

Let’s take a closer look at each major cookieless tracking method: how they work, where they shine, and how they fit together in a future-proof data strategy.

1. Server-side tracking: The backbone of (third-party) cookieless marketing

There’s a good reason to cover server-side tracking first. It is currently a key piece of marketing data infrastructure that helps extract more value from other solutions.

• For marketers, server-side tracking restores the reliability of data that’s been lost through cookie restrictions.
• For technical teams, it provides a privacy-first architecture: no hidden scripts, full control over what’s sent, and flexibility to adapt as platforms evolve.

How it works

At a high level, server-side tracking moves data collection from the user’s browser to a server. Instead of sending tracking data directly from a user’s device to third-party platforms (like Google Ads or Meta), the data flows first through a secure server, where you can validate, enrich, and share it selectively.

In most setups, server-side tracking should still be accompanied with some client-side tracking methods, like using a first-party cookie under your own domain, when possible (when you have consent to do so), to maintain session continuity and recognize returning users efficiently. 

A server-side setup can technically operate fully cookieless, but in general, we recommend using first-party cookies where possible.

In more practical terms, this is how server-side tracking works:

• Your website or app sends events (like purchases or form submissions) to a server endpoint (instead of gathering, handling and forwarding everything in the users’ browser)
• That server processes and forwards the data to analytics and ad platforms using their server-to-server APIs (e.g. Meta Conversions API, Google Enhanced Conversions)
• This setup bypasses many browser limitations and enables data enrichments from other sources than just the website, ensuring more conversions are captured accurately and compliantly

Pros

• Accurate measurement: Less data lost to blockers, browser caps, or network errors.
• Privacy and compliance: You decide what data leaves your environment.
• Faster website performance: Fewer browser scripts and requests.
• Data enrichment: You can enrich data collected from your website with data from other systems (like CRM and CDP).

Cons 

• Requires configuration: Initial setup and maintenance need technical expertise.
• Hosting costs: You’ll need your own infrastructure or a managed service.
• Limited visibility if misconfigured: Data accuracy depends on event validation.

Note: you can avoid many of these challenges with a marketer-friendly platform, which handles the technical setup and infrastructure for you.

Best tools for server-side tracking

These are the most common options for implementing server-side tracking:

1. Tracklution: The easiest way to implement true server-side tracking without deep technical expertise. Designed for accuracy, privacy, and simplicity - without compromising quality.
2. Google Tag Manager Server (sGTM): A flexible, developer-oriented framework for teams who want full control and customization.
3. Stape: A managed sGTM hosting service that simplifies setup and scaling, and makes server hosting cheaper.
4. Segment: A customer data platform that supports server-side event forwarding and audience management.

Ready to take control of your tracking? Start for free with Tracklution

2. First-party data & IDs

As cookie-based tracking becomes more and more restricted, first-party data becomes every marketer’s most valuable asset. It’s the data you collect directly from your customers through website interactions, app usage, email signups, or purchases.

First-party data often works hand in hand with first-party cookies. A first-party cookie can help maintain continuity between visits, while your CRM or analytics system uses that signal to build a privacy-compliant, unified customer view.

For many brands, first-party IDs are the bridge between known users and accurate personalization. Instead of relying on cookies set by external platforms, brands use their own data to recognize and connect with customers across sessions and channels.

How it works

First-party data is collected directly by your website or app and stored in your own systems (e.g. CRM, CDP, analytics database).

When a user logs in, signs up, or completes a transaction, a first-party identifier (like an email, user ID, or hashed token) is created or matched. You can use these identifiers to:

• Enrich customer profiles
• Sync audiences across marketing tools
• Measure conversions even when cookies are blocked

Pros

• Ensuring compliance: Fully controlled by your business.
• Stable identifiers: Less affected by browser restrictions.
• Enables personalization: Supports advanced segmentation and remarketing.
• Integrates well with SST: Server-side setups can securely process and share first-party IDs.

Cons 

• Requires logged-in or identified users: Harder to cover anonymous visitors.
• Data hygiene challenges: Duplicate or outdated identifiers can reduce accuracy.
• Infrastructure dependency: Needs CRM/CDP or unified data systems to scale effectively.

Common tools

These tools and platforms help brands collect, manage, and activate first-party data:

• Customer.io: Combines behavioral data with automation for personalized messaging.
• HubSpot CRM: A marketer-friendly platform to centralize first-party data and engagement.
• Salesforce Marketing Cloud: Enterprise-level data unification and audience management.
• Segment: Acts as a data hub to consolidate and distribute first-party IDs across tools.

Combined with a powerful server-side platform, first-party data becomes even more valuable.

3. Fingerprinting

Fingerprinting is one of the more technically creative ways to maintain user session continuity when cookies aren’t available. Instead of relying on stored cookies or explicit IDs, it identifies sessions by combining multiple device and browser characteristics into a unique “fingerprint.”

For example, a mix of screen resolution, browser type, installed fonts, and system settings can create a pattern distinctive enough to recognize returning users, or simply to keep a session together as someone moves from one page to another.

While fingerprinting is often discussed in the context of fraud prevention or analytics, it also plays a practical role in cookie-restricted scenarios. For instance, if first-party cookies are not available, fingerprinting may be used as a temporary fallback to keep measurement functional.

How it works

When a user visits your site, a small script collects various device and browser attributes. These are combined or hashed to create a temporary ID that can be compared across pageviews. If no cookies are available, this is often the only way to maintain session continuity, but it still requires the same legal basis and user consent as cookies.

Modern browsers like Safari, Firefox, and Chrome limit or randomize key data points to reduce misuse, so fingerprinting is most effective when applied in a controlled, consent-based setup, not as a silent tracking method.

Pros

• Works without cookies: Keeps sessions together even when cookies are blocked.
• Useful for security and analytics: Common in fraud detection, bot filtering, and session tracking.
• Immediate implementation: Only requires a front-end script and minimal setup.

Cons

• Be aware of regulation: Often treated similarly to cookies in privacy regulations.
• Declining reliability: Browser privacy updates continue to limit available signals.
• Shouldn’t fully replace cookies: Recommended to only be used as a fallback, not as a substitute for first-party cookie based user recognition.

Common tools

Fingerprinting or device recognition is typically implemented through specialized vendors or libraries such as:

• Fingerprint.com — Advanced device recognition platform for identification and fraud prevention.
• ClientJS — An open-source JavaScript library for collecting browser attributes.
• DeviceAtlas — Provides detailed device and network recognition for analytics and optimization.

When used appropriately, fingerprinting helps maintain minimal tracking functionality in cookie-restricted environments, but it shouldn't replace first-party cookies or consent-based data collection without a good reason.

4. Probabilistic & modeled attribution

As direct user identifiers become harder to access, marketers are turning to probabilistic attribution and modeled conversions to fill the data gaps. Instead of tracking each individual, these methods use statistical modeling and aggregated signals to estimate performance. They allow you to still understand which channels drive results, even when user-level data is incomplete.

Probabilistic attribution doesn’t try to identify a person; it predicts the likelihood that a conversion was driven by a specific campaign or channel. It’s the backbone of many major ad platforms’ reporting systems today.

How it works

These systems analyze patterns across large datasets, like campaign IDs, timestamps, device types, and regional signals, to infer which ads led to conversions. When user-level identifiers are missing (due to cookie loss or consent limitations), the model fills the gaps based on historical performance and correlation data.

Example: If a campaign historically drives conversions from mobile Safari users, and a new conversion appears without an identifier but matches that behavior pattern, the model attributes it to that campaign with a calculated probability.

This is how Meta’s Aggregated Event Measurement and Google’s Modeled Conversions continue providing insights even when direct attribution isn’t possible.

Pros

• Maintains visibility: Keeps measurement alive even with limited identifiers.
• Resilient to privacy restrictions: Uses aggregated and anonymized data.
• Platform-integrated: Supported by most major ad ecosystems by default.
• Scalable: Works across large datasets and multiple channels.

Cons

• Not exact: Attribution is inferred, not directly observed.
• Opaque models: Algorithms are often proprietary, limiting transparency.
• Harder to validate: Comparing modeled and observed data can be challenging.
• Limited actionability: Less useful for granular optimization decisions.

Common tools

These methods are built into many leading ad and analytics platforms, including:

• Google Ads Modeled Conversions: Uses privacy-preserving modeling to fill attribution gaps in GA4 and Ads.
• Meta Aggregated Event Measurement (AEM): A probabilistic framework for post-iOS14 tracking and reporting.
• AppsFlyer or Adjust: Attribution platforms offering both deterministic and probabilistic models for app campaigns.

When combined with server-side tracking, probabilistic models become far more accurate, because the server feed supplies cleaner, verified event data for platforms to model from. 

However, it’s not recommended to rely entirely on modeled data. Real, consent-based data should always be used whenever available, while probabilistic or modeled data should only complement the parts where direct collection or usage isn’t possible. 

This approach not only ensures compliance and accuracy but also enables platforms to receive verified event data, allowing marketing algorithms to optimize more effectively. Purely modeled data can’t provide that benefit.

5. Contextual & cohort approaches

When user-level data isn’t available, marketers can still target by focusing on context rather than individuals. Contextual and cohort-based methods don’t track people: they analyze the environment or group behavior patterns to deliver relevant ads and insights in a fully privacy-safe way.

In the cookieless era, these approaches have regained importance, offering compliant ways to reach audiences and measure engagement without identifiers.

How it works

Contextual targeting matches ads to the content a user is currently viewing. For example, showing a running shoe ad on a fitness article.

Cohort-based targeting groups users with similar interests or behaviors into anonymized segments (cohorts). Ads are then shown based on these collective patterns, not individual histories.

Modern systems like Google’s Topics API use recent activity within the browser to determine relevant ad themes, while remaining privacy-compliant and browser-native.

Pros

• Fully privacy-safe: Doesn’t rely on personal identifiers or cookies.
• Naturally compliant: Meets GDPR and CCPA standards by design.
• Brand-safe and relevant: Aligns ads with page content or interest clusters.
• Future-proof: Supported by major browsers and ad ecosystems.

Cons

• Limited personalization: Can’t target specific users or sequences.
• Less precise measurement: Harder to tie conversions to individual actions.
• Performance variability: Depends on context quality and content match.

Common tools

Contextual and cohort-based platforms include:

• Google Topics API: The successor to FLoC, enabling privacy-compliant ad relevance within Chrome.
• Seedtag: AI-powered contextual advertising that analyzes content meaning, not just keywords.
• Quantcast: Offers cohort-based audience insights and contextual measurement tools.

Contextual and cohort strategies work best as complements (not replacements) for first-party and server-side tracking. Together, they let you balance personalization, compliance, and scalability in a cookieless world.

6. Universal IDs

Universal IDs are one of the industry’s biggest attempts to rebuild identity in a cookieless world. Instead of relying on third-party cookies, they use hashed identifiers, typically derived from email addresses, phone numbers, or other first-party signals. These can be shared across participating ad platforms and publishers.

The idea is simple: create a standardized, privacy-compliant way to recognize users across the web, as long as they’ve consented and logged in somewhere.

How it works

When a user signs in or provides an email on a website, that information is hashed (converted into an irreversible string). This hashed ID becomes a universal token that multiple partners in the ad ecosystem can recognize and match, enabling cross-site targeting and measurement. All without relying on traditional third-party cookies.

Different Universal ID frameworks (like Unified ID 2.0 or ID5) act as intermediaries, helping advertisers and publishers sync these tokens securely.

Pros

• Cross-platform identity: Enables consistent targeting and attribution across multiple sites and devices.
• Privacy-conscious: Based on explicit user consent and encrypted identifiers.
• Improved interoperability: Helps rebuild connections between advertisers, publishers, and ad tech platforms.

Cons

• Requires wide adoption: Effectiveness depends on how many partners use the same framework.
• Login dependency: Works best for logged-in or authenticated users.
• Regulatory uncertainty: Still under scrutiny from privacy regulators.

Common tools

The Universal ID landscape is led by a few major initiatives and providers:

• Unified ID 2.0 (The Trade Desk): An open framework using hashed email identifiers, widely supported in ad tech.
• ID5: Focused on privacy-first identity solutions for publishers and advertisers.
• LiveRamp ATS: Connects first-party data to advertising platforms using secure identity resolution.

Universal IDs can extend your first-party and server-side strategy, but they’re not a standalone fix. A strong first-party foundation with server-side tracking at its core ensures you control your data before connecting it to broader identity systems.

Choosing the right cookieless tracking solution

With so many options on the table, it’s easy to wonder which method to invest in. The truth is, there’s no single solution that fits all cases perfectly. The strongest cookieless setups combine multiple methods that complement each other.

Still, every effective data strategy today starts with a server-side foundation. That’s the layer that guarantees you own your data, control its flow, and feed every other system with clean, compliant signals.

Here’s a recommended path that works for most brands:

1. Start with server-side tracking

Your data pipeline begins here. It ensures events are captured accurately and sent securely to the tools you use, from analytics platforms to ad networks. Think of server-side tracking as the central nervous system of your measurement stack.

2. Layer in first-party data

Use CRM or login-based identifiers to enrich your view of known users. When managed properly, this turns basic tracking into a true customer data engine. Server-side setups can safely connect the dots in existing customers and their website behaviour, and pass these IDs to ad platforms without exposing raw user data.

3. Add modeled insights for scale

Probabilistic or modeled attribution fills in the blanks where identifiers can’t go. When fed with clean server-side data, these models become more accurate and actionable.

4. Complement with contextual targeting

For upper-funnel reach and privacy-first campaigns, contextual and cohort-based strategies add compliant targeting power without identifiers.

5. Evaluate universal IDs carefully

They can help maintain cross-platform visibility, but only make sense if your partners and users support them. Treat them as an optional layer, not the foundation.

Future-proof your tracking

The future of marketing isn’t about removing cookies, it’s about controlling them. By decreasing the reliance on cookies (and when possible, using first-party cookies), owning your data, and moving tracking logic server-side, you can achieve accurate and compliant measurement without giving third parties power over your data.

This forces brands to build smarter, privacy-first data systems that actually improve measurement accuracy and long-term performance. You also have the option to go fully cookieless, but as of now there’s rarely reason to ditch first-party cookies entirely. 

The best results come from combining methods:

• Server-side tracking as your data foundation
• First-party IDs for known users and personalization
• Modeled attribution and contextual signals to fill the gaps where identifiers can’t go

Everything starts with a solid server-side setup. And if this gives you sweaty palms, you should know that server-side tracking is no longer the cumbersome and tech-intensive setup it used to be.

Tools like Tracklution give marketing and analytics teams an easy and reliable way to move tracking server-side without needing to build complex infrastructure or depend on developers. 

Start for free and see how simple cookieless tracking can be.

FAQ

Is cookieless tracking only for large companies?

Not at all. While enterprise brands moved first, (third-party) cookieless tracking is accessible to any business that relies on digital marketing data. Tools like Tracklution make server-side setups possible for smaller teams, as they can move forward without developers or complex infrastructure.

How does cookieless tracking affect ad performance?

When done right, cookieless tracking can improve ad performance. By collecting cleaner, more reliable data through server-side and first-party methods, you can recover lost conversions, optimize campaigns more effectively, and reduce wasted spend caused by incomplete or inaccurate tracking.

What’s the difference between first-party and third-party cookies?

First-party cookies are set under your own domain and are an essential part of a healthy tracking setup. They allow your website to remember things like session data, cart contents, or returning visitors, all within your own controlled environment.

Third-party cookies, on the other hand, are placed by external domains (like ad networks) to track users across multiple sites. These cookies shouldn’t form your data foundation in the future.

What replaces third-party cookies?

There’s no single replacement for third-party cookies. Instead, marketers are using a mix of methods such as server-side tracking, first-party data, contextual targeting, and modeled attribution. Together, these solutions allow accurate measurement and targeting while respecting privacy laws.

Is server-side tracking GDPR-compliant?

Server-side tracking is GDPR-compliant, but only when implemented correctly. The real question is whether you are compliant without it. With client-side tracking, you can only trust what happens inside the browser: you can’t see exactly what data is collected and where it goes. 

Server-side tracking changes that by giving you full visibility and control over your data flows. You can verify what is being sent, to whom, and under what consent conditions. Like any tracking method, compliance depends on how it’s configured, and poor implementation can still make it non-compliant. 

Do I still need Google Tag Manager if I move to server-side tracking?

If you want to use it, yes, but its role changes a bit. You’ll still use Google Tag Manager (web or server) to manage event tags, triggers and data layer variables, while your server-side environment (like Tracklution) handles secure processing, forwarding, and privacy controls behind the scenes.

What happens when Google fully removes third-party cookies?

Chrome is currently the only major browser still allowing third-party cookies, which means roughly half of the internet is already operating without them. This isn’t a cookie apocalypse: First-party cookies remain fully available and are actually the preferred, more privacy-friendly option. 

While Chrome’s exact timeline may still evolve, the broader shift toward privacy-first tracking is already well underway. Even a full phase-out won’t “break” tracking; it will simply mark the end of unreliable third-party methods. If you’ve already adopted server-side tracking and first-party data, you’ll barely notice the change. In fact, your measurement accuracy and campaign efficiency may even improve.