For years, cookies quietly powered how marketers identified returning users, measured retention, and attributed conversions. Today, that foundation is collapsing – and most analytics stacks are already broken without teams realizing it.
As of 2025, over 65% of global web traffic is now cookieless by default, driven by browser restrictions, consent rejection, and tracking prevention mechanisms (Safari ITP, Firefox ETP, Chrome Privacy Sandbox). Safari and Firefox alone block third-party cookies for 100% of users, while Chrome – which represents ~65% of browser market share – has officially deprecated third-party cookies for advertising use cases (Google Privacy Sandbox documentation).
Even first-party cookies are no longer reliable. Safari’s Intelligent Tracking Prevention (ITP) limits many first-party cookies to 24 hours or less unless specific engagement criteria are met. As a result, a user who returns after a day, switches devices, or clears browser data is often counted as a brand-new visitor – even if they’ve visited your site multiple times before.
Jump ahead to:
Why Cookies Can’t Identify Returning Users Anymore
1. Third-Party Cookies Are Extinct
Major browsers like Safari and Firefox no longer support third-party cookies, and Google’s Chrome followed suit with Privacy Sandbox changes – effectively ending the use of third-party cookies for cross-site user identification. Wikipedia
This means traditional ad platforms and analytics tools that relied on cookie syncing and cross-site identifiers can’t stitch user journeys across sites anymore. They lose the ability to recognize user IDs once a visitor leaves a domain.
2. First-Party Cookies Are Limited and Unreliable
Even first-party cookies – traditionally more stable – are under pressure. Browser privacy features such as Safari’s Intelligent Tracking Prevention (ITP) drastically shorten cookie lifespans (sometimes to as little as a day), breaking long-term user recognition and session continuity.
In practice, this means returning visitors who accept cookies might still appear as new after cookies expire or are cleared – which skews retention and conversion metrics.
3. Consent Behavior Kills Cookie Coverage
Cookie consent banners – required by GDPR, CCPA and other privacy laws – have a dramatic impact on analytics accuracy:
- 40-60% of users ignore cookie banners entirely (banner ghosting), meaning no vote for cookies and no tracking starts.
- 25-35% explicitly reject cookies, and only ~10-20% accept tracking.
This leads to 80-90% of visitors becoming invisible to cookie-based tracking – a catastrophic loss of insights for marketers.
4. Browser Cookie Deletion and User Behavior
Many users routinely clear cookies or use private/incognito modes. Even with first-party cookies, if a user clears their browser data, they look like a completely new visitor. That makes traditional cookie-based returning-visitor calculations deeply flawed. Wikipedia
Why Returning User Identification Matters
Before we dive into solutions, it’s important to clarify why recognizing returning users is so critical:
Accurate Retention Metrics – Seen vs new user patterns help marketers understand customer loyalty.
Improved Attribution – Without consistent identifiers, last-touch and multi-touch path analysis breaks.
Personalization & Engagement – Returning users trigger different experiences (e.g., email targeting, offers).
Retention Marketing – You can’t measure repeat purchases if you can’t reliably tie visits together.
Read more: First-Click or Last-Click Attribution Models: What is Right for Your Marketing Strategy?
Building a First-Party Recognition System (Without Cookies)
Since cookies can’t reliably tie users together anymore, the alternative is a first-party, privacy-first identity system that brings together multiple touchpoints in a compliant way.
Below are key strategies:
1. First-Party Server-Side Tracking
If cookies are no longer reliable for identifying returning users, the solution isn’t another client-side workaround – it’s changing where and how identity is created.
At the core of every effective first-party recognition system is server-side tracking.
Server-side tracking shifts data collection from the user’s browser to your own controlled infrastructure. Instead of relying on JavaScript tags, cookies, and browser storage – all of which are restricted, blocked, or deleted – events are captured and processed on your server, where browsers and ad blockers have far less control.
Benefits:
- More complete event data
- No cookie banners needed if no personal identifier is stored
- Better control and compliance
2. Deterministic Identifiers (Hashed PII)
Use hashed email addresses, phone numbers, or CRM IDs captured at consented touchpoints (e.g., login, signup, form submission) to link visits across sessions.
How it works:
- User signs up → you capture email/phone with consent.
- You hash this data into a pseudonymous ID.
- This ID now acts as your primary user key across sessions.
Because the identifier is deterministic, you can recognize the same user across sessions without cookies – provided you have consent.
Note: Hashing still counts as personal data under GDPR, but when consented and stored securely, it’s one of the strongest ways to connect sessions.
3. Identity Graphs
An identity graph links multiple identifiers (email, device type, user agent, hashed PII) to build a consistent profile. More identifiers = higher match accuracy.
Use cases include:
- Email-driven recency/frequency analysis
- Cross-device session stitching
- Mapping offline conversions to online activity
Brands using identity resolution in a cookieless context have reported ~24% increases in engagement and ~15% better campaign open rates.
4. Privacy-First Browser APIs & Standards
Google’s Privacy Sandbox and successor APIs (like Topics, Attribution Reporting API) try to let advertisers measure without breaking privacy. While they don’t identify individuals, they help with aggregated campaign insights.
Important caveat: These APIs are limited in scope and mostly help with interest cohorts and attribution rather than returning user identification. Wikipedia
5. Hybrid Probabilistic Methods
Some systems combine multiple signals (IP, device type, hashed ID) to infer that two sessions belong to the same user. While not 100% accurate, these methods can improve user recognition in aggregate analytics.
Ethical & legal note: Probabilistic methods should be disclosed in privacy policies and allowed only with legitimate legal basis.
Cookie-Based Tracking vs First-Party Recognition Systems
| Dimension | Cookie-Based Identification | First-Party Recognition System |
| User Recognition Accuracy | Low and declining due to cookie expiry, deletion, and browser limits | High when using deterministic identifiers (hashed email, CRM ID) |
| Cross-Session Tracking | Breaks frequently (ITP, consent rejection, cookie clearing) | Stable across sessions when identity is consented |
| Consent Impact | Loses 60-90% of users due to cookie banners and rejection | Works with explicit consent at key touchpoints (forms, login) |
| Browser Compatibility | Severely restricted (Safari, Firefox, Chrome Privacy Sandbox) | Browser-agnostic (server-side, first-party controlled) |
| Ad Blocker Resistance | Often blocked | Largely unaffected with server-side implementations |
| Privacy Compliance | High risk if misconfigured | Stronger compliance when consent-first and pseudonymized |
| Attribution Accuracy | Inflated “new users,” broken paths | More accurate multi-touch and offline attribution |
| Data Ownership | Controlled by browsers and ad platforms | Fully owned and controlled by the business |
How EasyInsights Helps Build a First-Party Recognition System
Building a first-party recognition system sounds straightforward in theory – but in practice, most marketing teams struggle with fragmented tools, partial identifiers, and unreliable data flows.
This is exactly where EasyInsights fits into the stack.
Server-Side Data Collection Built for Identity
EasyInsights captures conversion and event data server-side, reducing dependency on:
- Client-side cookies
- Browser storage
- Third-party scripts
Instead of relying solely on browser-generated IDs, EasyInsights ensures that key identifiers (like click IDs, hashed user data, and transaction references) are collected, validated, and preserved even when cookies fail.
Strong First-Party Identifiers
EasyInsights helps brands work with deterministic, first-party identifiers, such as:
- Hashed email addresses
- Phone numbers (hashed)
- CRM or internal customer IDs
- Platform click IDs (gclid, fbclid, etc.)
Cleaner Signals for Meta & Google Algorithms
Modern ad platforms don’t just need conversions – they need high-quality identity signals.
EasyInsights helps improve:
- Conversion match rates
- Event deduplication
- Identifier consistency
Conclusion
Cookies are no longer a reliable way to identify returning users. Browser restrictions, consent behavior, and privacy regulations have fundamentally broken cookie-based identity – and this isn’t changing.
To move forward, marketers must shift identity from the browser to their own data layer. First-party, server-side tracking combined with consented, deterministic identifiers is now the only scalable way to recognize returning users, measure retention accurately, and feed clean signals to ad platforms.
Brands that build first-party recognition systems don’t just stay compliant – they gain clearer attribution, better optimization, and long-term control over their data.
Send clear signal to Meta and Google ads with EasyInsights – Book a demo
