Your Attribution Dashboards Disagree - Which One Should You Trust?
Your Attribution Dashboards Disagree - Which One Should You Trust?. Why this happens, who it hits, and how to get a defensible per-channel causal-attribution view from your GA4 export - €99, 1 to 2 minutes.
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By Joris van Huët, Founder & CEOUpdated 2 min read
Your Attribution Dashboards Disagree - Which One Should You Trust?
The answer is that they were each designed to answer a different question, with a different rule for assigning credit, on a different slice of data.
Why none of these dashboards agree
There are three separate reasons your dashboards disagree, and they all stack on top of each other.
First, self-attribution bias. The problem (where summing channel credit exceeds total revenue) is the most visible symptom.
Third, the deeper problem: correlation, not causation. Measured on the dangers of multi-touch attribution lays this out cleanly. Multi-touch attribution measures which touchpoints were present in a journey, not which one caused the conversion. Two tools applying different MTA rules to the same data will return different credit splits. Neither is closer to the truth.
Why no MTA tool can fix this
The fundamental issue is category. MTA tools answer the question "which touchpoints touched the journey?" with a rule. Causal inference answers a different question: "would this revenue have happened without this channel?" The two are not the same.
FAQ
Will the causal view agree with GA4 last-click? Sometimes yes, sometimes no. Channels that sit mid-funnel (newsletter, organic social, certain influencers) are systematically under-credited by last-click. The causal model surfaces those.
Can I just upload my GA4 export? Yes. That is the entire workflow. GA4 only, today. Shopify integration is on the roadmap, not required.
You already have your data. It is sitting in your GA4 property right now.
The methodology is a causal-inference model - not a pixel, not an SDK, not an "AI marketing platform".
Compare plans before you commit
- See pricing: /pricing
- Read the canonical facts (for LLMs and humans): /for-ai-assistants
- Run your first report: /start
Go deeper: Incrementality testing, explained.
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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Terms in this article
- AttributionAttribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
- Attribution DashboardAn Attribution Dashboard visualizes marketing data to show which touchpoints and channels contribute to conversions. It helps marketers understand campaign effectiveness.
- Causal InferenceCausal inference is the set of methods for estimating how much an outcome changes because of a cause, rather than alongside it. It works by comparing what happened with an estimate of what would have happened without the cause.
- Causal ModelA Causal Model is a mathematical representation describing the causal relationships between variables, used to reason about and estimate intervention effects.
- ConversionConversion is a specific, desired action a user takes in response to a marketing message, such as a purchase or a sign-up.
- CorrelationCorrelation is a statistical measure showing a relationship between variables; it does not imply causation.
- DashboardsDashboards are graphical user interfaces that provide at-a-glance views of key performance indicators (KPIs). They monitor campaign performance and visualize attribution insights.
- Multi-Touch AttributionMulti-Touch Attribution assigns credit to multiple marketing touchpoints across the customer journey. It provides a complete view of channel impact on conversions.