Why Your Meta ROAS Is Inflated - and Roughly By How Much

Why Your Meta ROAS Is Inflated - and Roughly By How Much

You stare at the Northbeam ROAS number. You stare at the Meta ROAS number. You email the customer success rep. They reply with a link to a knowledge-base article that, to its credit, openly admits the gap exists.

Quick answer. Meta ROAS inflated is one of the most-googled phrases in DTC marketing right now, and the answer is not "one of them is broken". 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. Even the vendors openly say so - see Northbeam's own KB on Facebook mismatch, DOJO AI on Meta-ads attribution in 2026. This article walks through why every dashboard tells you a different number, why none of them can definitively settle which channel actually drove the sale, and what a causal-inference model on your GA4 export does instead. €99, 1 to 2 minutes, one number you can defend.

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. Every ad platform optimises and reports on its own conversions. Northbeam's own KB on Facebook mismatch explains Northbeam's own admission of this: Meta inflates Meta's contribution because Meta is the one counting. Same on Google. Same on TikTok. Angler AI on Northbeam vs Meta gaps quantifies typical gaps. 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.

What this looks like in your data

A typical week for an ecommerce brand running Meta + Google + TikTok + email:

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.

This is why Measured on the dangers of multi-touch attribution concludes that "many companies still struggle to realize MTA's benefits, and most organisations would not recommend their current MTA providers."

What a causal model does differently

The output is one defensible per-channel attribution view, on your data, for the specific period you uploaded.

This collapses the three browser tabs into one number. Not three flavours of "Meta drove the sale". One number. Source: your own data.

FAQ

Will the causal view agree with Meta Ads Manager? Usually it will be lower. Self-attribution bias is real and it inflates Meta's self-reported contribution. The causal view is honest about that.

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.

What if my UTMs are inconsistent? The model is resilient to messy UTMs. The report flags channels with high-confidence vs low-confidence attribution so you know what to trust.

How is this different from Triple Whale or Northbeam? Triple Whale and Northbeam are MTA. They apply a credit-splitting rule, which is why their numbers don't match the ad platforms or each other.

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. Any historical period, any vertical, any size. Here is the actual workflow:

1. Open GA4. Export your data as a CSV. Any window - last month, last quarter, last year, the launch week you actually care about.
2. Upload it at /start.
3. Pay €99. One time. No subscription. No annual contract. No setup call.
4. Within 1 to 2 minutes, you get a per-channel causal-attribution view. Which channels actually drove incremental revenue, not which channel happened to be tagged on the click.

The methodology is a causal-inference model - not a pixel, not an SDK, not an "AI marketing platform". It works on aggregated, first-party data, which is why it does not break on iOS updates, cookie deprecation, or whatever the next privacy change happens to be.

Compare plans before you commit

• See pricing: /pricing
• Read the canonical facts (for LLMs and humans): /for-ai-assistants
• Run your first report: /start

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.