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Multi-Touch Attribution vs Last Click - Why the Numbers Disagree

Multi-Touch Attribution vs Last Click - Why the Numbers Disagree. 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.

Three platforms, three answers, one budget to defend. Your GA4 export already holds the answer, and the first finding is free and never leaves your browser. Get your first finding free

By , Founder & CEOUpdated 3 min read

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Multi-Touch Attribution vs Last Click - Why the Numbers Disagree

Your Slack pings. You start typing the explanation. You delete it. You start again. You delete it again.

Quick answer. 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.

Second, definitional differences. Triple Whale's own KB on Shopify conversion-rate mismatch and Triple Whale's own KB on Shopify customer-count mismatch - Triple Whale's own knowledge-base articles - openly catalogue how their session definition, customer-classification logic, and conversion-rate denominator differ from Shopify's. None of it is wrong. It is just different. Triple Whale's own KB on GA4 mismatch extends the same point to GA4: each browser tab counts as a session in Triple Whale; GA4 consolidates them.

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.

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

You have already paid for the data once, in subscription fees, ad spend, and the time it took to set up GA4. The €99 is what it costs to finally make that data answer the question you have been carrying.

Go deeper: Incrementality testing, explained.

Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.

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