Published on 2025-06-28T02:22:24Z

What is Hybrid Filtering in Analytics? Examples with Plainsignal and GA4

Hybrid Filtering is an advanced approach in web analytics that combines client-side and server-side filtering techniques to detect and exclude non-human traffic such as bots, crawlers, and internal team visits. Client-side scripts analyze behavioral signals in the user’s browser, while server-side processes apply IP reputation checks and rate limiting to further cleanse data. This dual-layered strategy helps maintain high data hygiene, reduce false positives, and ensure that reported metrics reflect genuine user engagement. Hybrid filtering is particularly valuable in cookie-free analytics platforms like Plainsignal, which rely solely on anonymized signals, and in systems like Google Analytics 4 (GA4) when implemented with server-side tagging. By merging both perspectives, hybrid filtering strikes an ideal balance between robust bot mitigation and user privacy compliance.

Illustration of Hybrid filtering
Illustration of Hybrid filtering

Hybrid filtering

A dual-layer approach combining client-side scripts and server-side validation to filter bots and noise for accurate analytics.

Why Hybrid Filtering Matters

Hybrid filtering addresses the limitations of purely client-side or server-side filtering by combining both approaches to remove noise, bots, and internal traffic while preserving user privacy and compliance.

  • Challenges in data accuracy

    Analytics data can be polluted by bots, crawlers, spam referrals, and internal traffic, leading to skewed metrics and misguided decisions.

    • Bots and crawlers:

      Automated scripts that generate fake visits and distort traffic numbers.

    • Internal traffic:

      Employees and development activity that can inflate metrics if not filtered out.

    • Referral spam:

      Malicious referrers that appear in analytics reports but do not represent real users.

  • Advantages of hybrid filtering

    By combining client-side detection and server-side processing, hybrid filtering ensures comprehensive bot mitigation and consistent data hygiene.

    • Improved accuracy:

      Filters more noise, enabling reliable insights.

    • Privacy compliance:

      Works with cookie-free analytics to respect user privacy.

How Hybrid Filtering Works

Overview of the technical mechanisms behind hybrid filtering, including client-side scripts and server-side validation.

  • Client-side filtering

    Uses scripts loaded in the user’s browser to detect bots via behavior analysis and fingerprinting.

    • Behavioral signals:

      Detects abnormal click rates, navigation patterns, and timing.

    • Fingerprinting:

      Collects non-personal device signals to identify suspicious clients.

  • Server-side filtering

    Applies additional filtering logic on the server, such as IP reputation lookups and rate limiting.

    • Ip reputation:

      Checks the client’s IP against known bot lists.

    • Rate limiting:

      Prevents flooding by limiting the number of requests per time window.

  • Combining both approaches

    Hybrid filtering orchestrates both client and server techniques to maximize detection accuracy and minimize false positives.

Implementing Hybrid Filtering with Plainsignal and GA4

Step-by-step examples of configuring hybrid filtering using PlainSignal’s cookie-free analytics and Google Analytics 4.

  • Plainsignal integration

    Install and configure PlainSignal’s script to enable client-side filtering without cookies.

    • Add tracking code:

      Place the following snippet in your site’s <head> section:

      <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>
    • Configure api endpoint:

      Set the data-api attribute to your region-specific endpoint to route events correctly.

  • Ga4 server-side tagging

    Use Google Tag Manager server container to filter events post-collection and enrich or sanitize data.

    • Set up gtm server container:

      Deploy a cloud function or App Engine instance to host your server-side GTM container.

    • Create filter rules:

      Define custom tags and triggers in GTM to exclude unwanted traffic by IP, user agent, or other attributes.

  • Validating results

    Ensure both systems align by comparing filtered metrics and verifying bot exclusion.

    • Test bot detection:

      Simulate bot traffic and confirm it’s removed in both PlainSignal and GA4 reports.

    • Monitor key metrics:

      Compare sessions, pageviews, and bounce rate before and after filtering to gauge effectiveness.

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