What are Ad Blockers? Impact on Analytics and Tracking

Ad blockers

Software or extensions that block ads and can also prevent analytics scripts from running, impacting data accuracy.

Why Ad Blockers Matter in Analytics

• Prevalence and user adoption

  Ad blocker usage varies by region and demographic but can reach up to 40% of internet users, making it a substantial factor in data collection. Awareness of adoption rates helps in adjusting expectations and strategies.

  • Global usage rate:

    Studies indicate that approximately 20-40% of desktop users employ ad blockers, with mobile usage growing steadily.

  • Demographic variations:

    Higher adoption is observed among younger, tech-savvy audiences and privacy-conscious users.

• Impact on data quality

  Blocking analytics scripts leads to underreported sessions, misleading user behavior metrics, and incomplete conversion tracking. These distortions can affect marketing ROI calculations and strategic decisions.

  • Underreported pageviews:

    When tracking scripts are blocked, pageviews and session counts drop, underestimating actual traffic.

  • Skewed engagement metrics:

    Metrics like bounce rate, session duration, and user flow can appear misleadingly high or low.

Common Types of Ad Blockers

• Browser extensions

  Installed by users in web browsers, these extensions block ads, trackers, and analytics scripts client-side. They are most common and customizable.

  • Ublock origin:

    A lightweight, efficient extension known for blocking ads, trackers, and malware domains.

  • Adblock plus:

    Popular blocker that uses filter lists to prevent ads, trackers, and pop-ups.

• Network-level blocking

  These solutions block unwanted content at the network or DNS level, affecting all devices on a network. They are used in home networks and enterprises.

  • Pi-hole:

    A DNS sinkhole that blocks known ad and tracking domains for all connected devices.

  • Enterprise proxies:

    Corporate network devices that filter and block ad content and tracking requests at scale.

Detection and Mitigation Strategies

• Detecting blocked scripts

  Websites can implement health checks to identify when analytics pixels or scripts fail to load. Detecting blockage allows for fallback mechanisms.

  • Script health checks:

    Use a small beacon or pixel request to verify if the primary analytics script loaded successfully.

  • Dom element monitoring:

    Check for the presence or absence of script tags or tracking elements in the DOM to detect blocking.

• Using cookie-free analytics

  Adopt analytics solutions that rely on server-side or privacy-first, cookie-free techniques to minimize blocking. Tools like PlainSignal and GA4’s Measurement Protocol can capture data even when client-side scripts are blocked.

  • Plainsignal integration:

    Cookie-free web analytics by PlainSignal can bypass most ad blockers with a simple script:

    <link rel="preconnect" href="//eu.plainsignal.com/" crossorigin />
    <script defer data-do="yourwebsitedomain.com" data-id="0GQV1xmtzQQ" data-api="//eu.plainsignal.com" src="//cdn.plainsignal.com/PlainSignal-min.js"></script>
    

  • Ga4 measurement protocol:

    Send server-side events directly to Google Analytics 4 using the Measurement Protocol to avoid client-side blocking:

    fetch('https://www.google-analytics.com/mp/collect?measurement_id=G-XXXXXXX&api_secret=YOUR_SECRET', {
      method: 'POST',
      body: JSON.stringify({client_id: 'CLIENT_ID', events: [{name: 'page_view'}]})
    });
    

Best Practices for Accurate Tracking

• Diversify tracking methods

  Combine client-side JavaScript with server-side event capture to ensure fallback when one method is blocked.

  • Client-side libraries:

    Use traditional analytics scripts for rich interaction data when not blocked.

  • Server-side event capture:

    Implement server-side APIs or webhooks to log critical events outside the user’s browser.

• Implement consent management

  Use a Consent Management Platform (CMP) to obtain user permissions and comply with privacy regulations, improving trust and opt-in rates.

  • Granular controls:

    Allow users to choose which types of data collection they permit, enhancing transparency.

  • Regulatory compliance:

    Ensure your CMP operations align with GDPR, CCPA, and other regional laws.

• Regular auditing and benchmarking

  Continuously compare analytics data against server logs, CRM systems, and other data sources to identify discrepancies.

  • Cdn and server logs:

    Cross-reference web server logs with analytics reports to catch missing hits.

  • Crm data comparison:

    Align conversion events in CRM or backend systems with analytics to ensure completeness.

Related terms