Attribution Models in Google Analytics 4 (GA4)
GA4 primarily uses three attribution models: Data-driven (default for event-scoped dimensions), Paid and organic last click, and Google paid channels last click. These models assign credit to marketing touchpoints along a user's path to conversions (now called key events), excluding direct visits unless the entire path is direct. Traditional models like linear, position-based, time decay, and first click have been deprecated and are no longer available in GA4 properties.
Key Attribution Models and How They Work
| Model | Description | Default Usage | Best For Marketers |
|---|---|---|---|
| Data-driven | Uses machine learning and historical conversion data to assign credit based on touchpoint impact, factoring in time, device, ad interactions, and counterfactual analysis (what would happen without a touchpoint). | Event-scoped dimensions; recommended by Google as it adapts automatically. | Complex, multi-channel campaigns where AI identifies high-impact channels without manual rules. |
| Paid and organic last click | Gives 100% credit to the last paid or organic touchpoint before conversion. | User- and session-scoped dimensions. | Simpler analysis focusing on closing channels. |
| Google paid channels last click | Gives 100% credit to the last Google-paid channel (e.g., Search, Display) before conversion. | Available in Attribution reports. | Evaluating Google Ads performance in paid-only contexts. |
You can switch models for event-scoped reports in GA4, applying changes retroactively to historical data. Google recommends data-driven for most scenarios due to its algorithmic precision over rule-based alternatives.
Practical Tips for Marketers
- Compare models in GA4's Attribution reports to reveal channel contributions (e.g., data-driven may credit upper-funnel channels more than last-click).
- Limitations: Models exclude direct traffic; data-driven requires sufficient conversion volume for accurate ML. Test models against your goals before optimizing budgets.
- Deprecated UA models (e.g., linear: equal credit across all touchpoints; position-based: 40% each to first/last, 20% middle) are replaced by data-driven for better multi-device accuracy.
Cohort Analysis in GA4
Cohort analysis groups users by shared characteristics (e.g., acquisition date) to track retention and behavior over time. In GA4, access it via Reports > Retention > Cohort exploration, using the default acquisition date cohort with metrics like retention rate or revenue per user.[Search results lack direct GA4 cohort details; based on standard GA4 functionality as of 2025.]
How Cohort Analysis Works for Marketers
- Setup: Select cohort type (e.g., acquisition date, first session source), date range, and metric (e.g., active users, event count).[Standard GA4 knowledge]
- Key Insights:
- Identify drop-off patterns (e.g., Day 0: 100% retention; Day 7: 30%).
- Compare cohorts (e.g., organic vs. paid traffic) to optimize channels.
- Combine with attribution: Use data-driven model to see which touchpoints drive retained cohorts.
- Advanced Use: Export to BigQuery for custom cohorts; integrate with attribution paths for journey-retention analysis.[GA4 standard]
When to Use for Marketers
- Retention-focused campaigns (e.g., apps, e-commerce) to measure lifetime value beyond one-off conversions.
- Pair with attribution: Data-driven cohorts reveal long-term channel ROI, unlike last-click's short-term bias.[Inferred from ]
For hands-on setup, navigate GA4's Explorations; sufficient data (e.g., 1,000+ users/cohort) ensures reliability.[GA4 standard]
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