Third-party cookies used to be the backbone of tracking online ads and user behavior across sites, but browsers and regulations are now steadily curbing their use in the name of privacy.

This “cookie crackdown” means marketers have a tougher time tracking who saw their ads and who eventually made a purchase. At the same time, third-party cookies have not disappeared completely: Chrome has delayed full deprecation, Privacy Sandbox is still evolving, and many platforms continue to use cookies in parallel with newer, privacy-focused APIs.

In this article, we’ll break down how measurement accuracy has been impacted by these growing restrictions, and what major platforms like Google and Facebook are doing to help advertisers adapt.

1. Definition of Third-party Cookies

A third-party cookie is a small piece of data placed on your browser by a website other than the one you’re currently visiting — usually by an advertising or analytics service — so it can track your activity across multiple sites for purposes like ad targeting and performance measurement.

2. Why Are Third-Party Cookies Being Restricted?

Some main reasons for restricting third-party cookies:

• Privacy concerns: They track users across websites without clear consent, exposing sensitive personal data.
• Security risks: Cookies can be hijacked or misused to steal user information or impersonate accounts.
• Regulatory pressure: Laws like GDPR and CCPA require explicit consent and transparency, making uncontrolled tracking illegal.
• Industry shift: Major browsers and platforms are adopting privacy-first standards, phasing out third-party cookies in favor of safer, consent-based methods.

3. What Are The Impacts On Digital Advertisers?

The reduction and restriction of third-party cookies is reshaping how marketers target, measure, and optimize campaigns. Here are 3 key impacts:

• Retargeting limitations: When third-party cookies are blocked or limited, advertisers can no longer easily identify users who previously interacted with their website. This means fewer personalized ads and a weaker ability to bring back visitors who abandoned their carts or browsed products without purchasing.
• Reduced audience acquisition accuracy: Lookalike or similar-audience campaigns rely on tracking browsing behavior to find people who resemble a brand’s best customers. With less accessible historical data, algorithms struggle to recognize these “digital twins,” leading to less efficient targeting and higher acquisition costs.
• Weakened performance measurement: Attribution models that used to track which ads users saw before converting (post-view conversions) now face major blind spots in more restricted environments. Marketers can no longer clearly connect ad exposure to purchase, resulting in incomplete or misleading ROI calculations.

Imagine a simple example: You run an online shoe store and launch a Facebook ad campaign. A customer sees your ad, clicks it, browses your site, but doesn’t buy immediately. A week later, they return directly to your site and purchase a pair of shoes.

→ In the past, a third-party cookie could have linked that purchase back to the Facebook ad, giving you credit for the conversion.

→ Now, if that customer’s browser blocked cookies or they opted out of tracking, your analytics might show the sale as “direct” or unattributed – as if the ad had nothing to do with it. The measurement isn’t as accurate as it used to be, and your marketing ROI looks lower even though the ad actually worked.

Overall, the impact is clear: advertisers have less visibility into user journeys, making it harder to prove the value of digital media investments.

4. How Major Platforms Are Adapting Measurement Strategies

Google, Meta, and Apple have rolled out new tools and approaches to maintain advertising effectiveness and measurement accuracy with fewer reliable third-party cookies. Let’s look at some of the major measures and recent updates.

Google

• Consent Mode v2: This tool dynamically adjusts tag behavior in websites and apps based on user consent for cookies and identifiers. It communicates consent status to Google services, ensuring data collection only occurs with permission. In order to use Consent Mode v2, you need to have a cookie banner or a Consent Management Platform on your website.

• Dynamic tag behavior: Consent Mode v2 automatically adjusts how Google tags behave based on a user’s consent choices — covering analytics, advertising storage, and the two new mandatory signals: ad_user_data (whether Google can send identifiable user data) and ad_personalization (whether personalized/remarketing ads are allowed).
• Full vs. limited functionality:
• When users grant consent, Google tags operate normally, allowing cookies, identifiers, enhanced measurement, and personalized ads.
• When users deny consent, tags switch to a restricted mode that prevents advertising cookies and user-level tracking, but still allows privacy-safe measurement through anonymous, aggregated signals.
• Modeled conversions when consent is denied:
• Even without cookies, Consent Mode v2 sends minimal “cookieless pings” so Google can model conversions using machine learning. This prevents major gaps in reporting and keeps bidding strategies functional without violating user privacy.

Meta

• Conversions API (CAPI): This enables direct server-to-server event transmission from advertisers to Meta, bypassing browser issues like ad blockers or cookie restrictions. It enhances event match quality (EMQ)—a score measuring alignment with Meta accounts—by delivering stable, complete data.
• Advanced Matching: This feature enriches events with hashed first-party data (e.g., emails or phone numbers), boosting ID resolution and attribution accuracy. When paired with CAPI, it captures more parameters, minimizes data loss from interruptions, and significantly improves EMQ.

Apple

• App Tracking Transparency (ATT): Requires explicit user opt-in for cross-app/site tracking via the Identifier for Advertisers (IDFA), leading to reduced data availability and a shift to privacy-preserving tools.
• SKAdNetwork (SKAN): Provides aggregated, anonymized reports on app installs and post-install events with built-in delays (at least 24 hours) to prevent fingerprinting and protect anonymity.

5. Practical Takeaways

• Implement server-side infrastructure: Adopt Meta Conversions API, Google server-side tagging, and Enhanced Conversions to improve event match rates and reduce data loss caused by browser restrictions.
• Deploy Consent Mode v2 with a proper CMP: Using Consent Mode v2 (paired with a compliant CMP) allows Google to legally receive consent signals and model missing conversions when users deny advertising cookies.
• Strengthen your first-party data foundation: Focus on building direct relationships with users through logins, email capture, and phone numbers. Combine this with advanced matching to stabilize cross-platform attribution as third-party identifiers fade.
• Segment performance benchmarks by platform: Evaluate iOS separately from Android and Web, as iOS relies more heavily on modeled conversions and aggregated reporting. This will help to set realistic expectations and prevent misinterpreting campaign performance.

Conclusion

In Vietnam, CMP adoption is still low and most local websites do not implement cookie banners, while Android holds a dominant share of mobile traffic. This means browser-level cookie restrictions and iOS tracking limits tend to have a smaller overall impact on measurement compared to more iOS-heavy markets.

In the end, third-party cookies are becoming more restricted and less reliable as a primary signal, but good measurement doesn’t have to disappear with them. By using tools like Consent Mode v2, server-side tracking, and first-party data with advanced matching, brands can still understand which ads work — just in a more privacy-friendly and future-proof way.