Cookieless Tracking Solutions: Analytics After Third-Party Cookies

Third-party cookies did not disappear the way everyone expected, and cookieless tracking matters anyway. Safari and Firefox have blocked them by default for years, which alone makes roughly 17 to 20 percent of global traffic cookieless. Chrome, the exception, abandoned its deprecation plan in 2024, confirmed in April 2025 that it would not add a removal prompt either, and then began retiring the Privacy Sandbox replacements it had built: they were deprecated in Chrome 144 in January 2026, and Chrome 150 in July 2026 was the target for removing the Protected Audience API. So the browser with the most users kept the cookie and dropped the alternative. Meanwhile GDPR, the CPRA and their successors still require valid consent for tracking cookies regardless of what any browser does. The tracking infrastructure that powered digital marketing for two decades is not gone, but it is unreliable, partial and consent-gated, and building on it is a bet against the trend.

But analytics and attribution don't have to disappear with cookies. Multiple cookieless tracking solutions exist, each with different trade-offs. Understanding your options lets you maintain marketing effectiveness while respecting the privacy-focused direction of the web.

Why Cookies Are Going Away

Browser Restrictions

Safari's Intelligent Tracking Prevention (ITP) has blocked third-party cookies since 2017 and now limits first-party cookies set by JavaScript to 7 days. Firefox Enhanced Tracking Protection blocks third-party trackers by default. Chrome, after multiple delays, dropped its plan to remove third-party cookies and lets users block them instead.

These restrictions break traditional cross-site tracking. Retargeting pixels, audience sharing, and multi-touch attribution across properties all depend on third-party cookies that browsers no longer support.

Regulatory Pressure

GDPR requires consent for cookies that aren't strictly necessary. ePrivacy Directive mandates cookie consent banners. CCPA gives users the right to opt out of data sales facilitated by cookies.

Consent requirements reduce cookie effectiveness even where technically possible. Typical opt-in rates of 30-60% mean significant tracking gaps.

User Expectations

Privacy awareness has increased. Users understand tracking more than before, and many actively avoid it. Ad blockers, privacy browsers, and tracker-blocking extensions are mainstream.

Building your business on tracking that users actively resist is not sustainable.

Where Things Stand in September 2026

Three facts set the boundaries. First, about a fifth of your visitors arrive without third-party cookies no matter what you do, and on mobile Safari the share is higher. Second, Chrome users can now opt out of third-party cookies through a browser setting, and privacy-conscious segments do. Third, consent laws apply to first-party analytics cookies too, so a cookie that survives the browser still needs a yes from the visitor. The approaches below are ranked by how much of that lost signal they recover: server-side tracking and first-party identifiers recover the most, and the industry data suggests teams that moved to them recover 60 to 75 percent of the signal they lost. The right tool makes the difference between building this yourself and getting it by default; Arktis tracks without cookies through a first-party identifier, captures ad click IDs on landing, and connects Shopify and Stripe orders server-side, and the free UTM builder covers the campaign-tagging discipline every approach depends on.

Cookieless Tracking Approaches

Server-Side Tracking

Instead of client-side JavaScript sending data to third parties, your server processes and forwards events. This approach works regardless of browser restrictions on cookies or JavaScript.

How it works: User interacts with your site, your server logs the event, your server sends relevant data to analytics and ad platforms via server-to-server APIs. The browser never connects to third-party tracking domains.

Benefits include bypassing ad blockers and browser restrictions, more reliable data collection, better data control, and reduced page load impact.

Trade-offs: Implementation complexity increases. You need server infrastructure. Real-time capabilities may be limited compared to client-side.

First-Party Cookies

First-party cookies (set by your domain) still work in most scenarios. They're subject to some restrictions (like Safari's 7-day limit for JavaScript-set cookies) but far less than third-party cookies.

For tracking users across sessions on your own site, first-party cookies remain viable. The limitation is cross-site tracking - first-party cookies can't follow users to other domains.

Browser APIs: Privacy Sandbox Is Retired

Google's Privacy Sandbox was meant to replace cookies with browser APIs for interest groups, remarketing and attribution. In 2026 Google retired most of it. Topics, Protected Audience, Attribution Reporting and related APIs entered deprecation in Chrome 144 in January, with removal of Protected Audience targeted for Chrome 150 in July, after low adoption and sustained antitrust pressure, and Google chose to keep third-party cookies in Chrome instead. Do not build anything on these APIs. The durable approaches are the ones that never depended on the browser vendor: server-side events, first-party identifiers, click IDs and modeled attribution.

Topics API categorizes users by interests based on browsing history, with the browser controlling access. Protected Audience (formerly FLEDGE) enables on-device auction for retargeting without sharing browsing history. Attribution Reporting API provides conversion measurement with aggregation and noise to prevent individual identification.

These APIs are still evolving. Adoption is uncertain. They work only in Chrome initially. But they represent one possible future for privacy-preserving advertising.

Probabilistic Attribution

Without deterministic cookie matching, probabilistic methods infer user identity from available signals: IP address, device type, browser version, time of interaction, and other contextual factors.

Probabilistic matching is less accurate than cookie-based tracking but provides directional insights. Accuracy varies from 60-85% depending on methodology and use case.

Contextual Targeting

Instead of tracking users across sites, target based on the content they're currently viewing. A user reading about running shoes sees running shoe ads - no tracking required.

Contextual targeting has experienced a renaissance as behavioral targeting faces restrictions. Modern contextual solutions use machine learning to understand content deeply and match relevant advertising.

Authenticated Traffic

When users log in, you have deterministic identity without cookies. Email addresses (hashed) serve as cross-site identifiers with user consent.

Building authenticated traffic requires value exchange - reasons for users to log in. Loyalty programs, personalization features, and member benefits drive authentication rates.

Platform-Specific Solutions

Facebook Conversions API

Facebook's server-side API sends conversion events directly from your server to Facebook, bypassing browser restrictions on the Facebook pixel.

Implementation requires server infrastructure capable of sending events when conversions occur, proper event matching (email, phone, Facebook click ID), and deduplication between browser and server events.

Conversions API improves match rates compared to pixel-only tracking, especially for iOS users affected by App Tracking Transparency.

Google Enhanced Conversions

Google's Enhanced Conversions sends hashed first-party customer data with conversion events. Google matches this data against signed-in users for improved attribution.

Implementation adds hashed email, phone, or address to conversion tags. Google handles matching while maintaining user privacy.

TikTok Events API

Similar to Facebook, TikTok's Events API enables server-side conversion tracking. This bypasses browser restrictions and improves match rates for TikTok advertising attribution.

Attribution Without Cookies

Multi-Touch Attribution Challenges

Traditional multi-touch attribution relies on tracking users across touchpoints. Without cookies, connecting early touchpoints to later conversions becomes difficult.

Solutions include first-party identity (requiring login earlier in journey), click identifiers (fbclid, gclid) captured at each touchpoint, incrementality testing to measure channel contribution, and media mix modeling using aggregate data.

Click Identifier Tracking

Ad platforms include click identifiers in URLs: fbclid for Facebook, gclid for Google Ads, ttclid for TikTok. Capturing these identifiers enables direct attribution without third-party cookies.

When a user clicks an ad and later converts, match the stored click identifier to attribute the conversion. This works even with cookie restrictions.

fbclid tracking and gclid tracking are essential capabilities for cookieless attribution.

UTM Parameters

UTM parameters don't require cookies at all. They persist in URLs and can be captured server-side. For session-level attribution, UTM tracking continues to work.

The limitation is connecting sessions - without cookies, you can't link a user's first visit (from paid ad) to their later conversion (from direct visit). First-party identity or click identifier matching bridges this gap.

Implementation Priorities

Step 1: Audit Current Cookie Dependencies

What tracking currently relies on third-party cookies? Which capabilities will break as restrictions tighten? Prioritize based on business impact.

Step 2: Implement Server-Side Tracking

Move event collection to your server. This provides the foundation for cookieless tracking regardless of which specific solutions you adopt.

Step 3: Set Up Conversions APIs

Implement Facebook Conversions API, Google Enhanced Conversions, and equivalent solutions for other ad platforms you use. These improve attribution immediately.

Step 4: Capture Click Identifiers

Ensure your site captures and stores fbclid, gclid, and other click identifiers. Link them to conversions for direct attribution.

Step 5: Build First-Party Identity

Create reasons for users to log in. Authenticated users provide deterministic identity across sessions without cookies.

Step 6: Test and Validate

Compare data from cookieless sources against historical cookie-based data. Identify gaps and refine implementation.

Measuring Success

Attribution Coverage

What percentage of conversions can you attribute to marketing touchpoints? Coverage may decrease during transition but should stabilize as cookieless solutions mature.

Data Accuracy

Compare attributed conversions against actual business outcomes. First-party and server-side data is often more accurate than cookie-based tracking.

Platform Alignment

Do your internal numbers match platform-reported numbers? Server-side tracking typically improves alignment by providing more complete data to platforms.

Getting Started

Start with server-side tracking infrastructure - this enables everything else. Then implement conversions APIs for your advertising platforms.

Arktis provides cookieless attribution using server-side tracking, click identifier capture, and first-party data collection.

Sources

1. WebKit, "Intelligent Tracking Prevention"

2. Google, "Privacy Sandbox"

3. Meta for Developers, Conversions API documentation

4. Google, "Enhanced Conversions"

5. Google, Google shares update on next step toward phasing out third-party cookies in Chrome

6. Consenteo, Third-Party Cookies in 2026: What Actually Happened After Google's Reversal

7. JENTIS, Google keeps third-party cookies alive: what it really means