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Overview

When comparing pageview numbers between Permutive and Google Analytics (GA), you may notice discrepancies. This is expected because these platforms are fundamentally different systems that track and report data differently.
Important: Direct comparisons between Permutive and GA are only valid for pageviews. Never compare user counts between platforms, as different identity models make this comparison invalid.

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Fundamental Differences

Permutive and GA use different tracking mechanisms and apply different filtering rules. Here’s a comparison:
AspectGoogle AnalyticsPermutive
TrackingUses analytics.jsUses Permutive SDK
ConsentDepends on your GA setupMay rely on consent-by-token
Bot TrafficMay include bots unless filteredAutomatically excludes known bot traffic
User IdentityCookie/session-basedFirst-party identity models
When It FiresAt page load (if script loads)When SDK loads AND consent is given
These differences mean that even on the same page, GA and Permutive may record different numbers of pageviews.
Several factors can contribute to differences in pageview counts:
  • SDK Loading Timing: The Permutive SDK and analytics.js may not load at the same time or on the same pages
  • Consent Models: Especially on EEA/UK domains, consent requirements can significantly reduce trackable pageviews in Permutive while GA may still collect data
  • Bot Filtering: GA may include bot or spam traffic unless explicitly filtered, while Permutive applies stricter automatic filters
  • AMP/FIA Traffic: AMP and Facebook Instant Articles traffic may not be captured by Permutive unless explicitly implemented
  • Deployment Coverage: Permutive may not be deployed on all pages, domains, or subdomains where GA is present

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Comparing Pageviews Accurately

To make an accurate comparison, you need to ensure a like-for-like scenario. This means:
Always compare pageviews, not users. User counts cannot be compared between platforms because they use fundamentally different identity models.
Best practices for comparison:
  1. Use the same fixed time period for both platforms
  2. Compare the same domains and subdomains
  3. Account for consent differences (Permutive may only track consented users)
  4. Consider traffic type differences (Web, AMP, FIA)
  5. Check if bot filtering is applied consistently
When preparing GA data for comparison, please provide:
  1. Fixed Timeframe: Set specific start and end dates
    • Break down by month if possible
    • Day-by-day breakdown helps speed up analysis
  2. Bot Traffic Status: Does your GA traffic include bot traffic?
  3. Domain Breakdown: Split traffic by domain
    • Include subdomain breakdown if relevant
  4. Browser Split: Break down by browser type (Safari, Firefox, Chrome, Other)
  5. Traffic Type: Split by type (Web, AMP, FIA)
  6. Pageviews Only: Share pageview counts, not unique user counts
  7. Data Format: Provide data as Google Sheets, CSV, or Excel
When preparing Permutive data for comparison, identify where you retrieved the numbers from:If using Audience Insights:
  • Which cohort(s) are you analyzing?
  • What dimension filters are applied (e.g., domain, device type, browser)?
  • What date range is selected?
If using Planning:
  • Which plan are you referencing?
  • What audiences are included in the plan?
  • What date range does the plan cover?
Additional information needed:
  • Consent Status: Is consent-by-token active on the domains being compared?

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Troubleshooting Discrepancies

A common cause of discrepancies is incomplete deployment. Check:Domains and Subdomains:
  • Is Permutive active on all domains where GA is deployed?
  • Are subdomains like forum.example.com or blogs.example.com included?
Page Types:
  • Is Permutive firing on all page types (articles, index pages, section pages)?
  • Some implementations may only track certain page types
Platforms:
  • Do you have AMP implementation?
  • Is Facebook Instant Articles (FIA) configured?
  • Are mobile apps instrumented with Permutive?
Temporary issues can cause pageview gaps:
  • Permutive Outages: Check if there were any service disruptions during the comparison period
  • Deployment Changes: Was Permutive temporarily removed or modified?
  • Tag Manager Issues: If deployed via a tag manager, were there configuration changes?
Historical deployment data can often be investigated using monitoring tools.