YouTube shows 1.9x ROAS.
Meta shows 4.1x.
One of them is folklore.
For Dutch Shopify ecommerce brands scaling from €50K to €500K/month. Upload your GA4 data. Know which channels drive incremental sales in minutes.
No signup for the demo · Full refund guarantee · GA4 CSV upload, 5–10 min insights · See full pricing · Book a 20-min call
The €160K question: Is your upper funnel working or stealing credit?
You're spending €200K/year on upper-funnel channels. Video reviews. Unboxings. Product discovery content.
YouTube dashboard says 1.9x ROAS. Meta dashboard says 4.1x. But what if YouTube creates the demand that Meta converts weeks later?
The math:
YouTube "attributed" revenue: €200K × 1.9x = €380K
YouTube incremental revenue: Test market shows 32% drop = €160K lost
Difference: €160K revenue YouTube drives but doesn't get credit for.
Cut YouTube = save €200K, lose €160K revenue, collapse funnel. Keep YouTube = justify "low ROAS" to CFO forever.
You're stuck. Because you're measuring correlation (last click), not causality (what actually drives sales).
Twinkels found 28% of ad spend going to the wrong channels. See where yours should go.
Correlation vs Causality: The difference is €160K
Traditional attribution (correlation):
Meta had the last click → Meta gets 100% credit → YouTube shows "low ROAS" → Cut YouTube → Funnel collapses.
Behavioral intelligence (causality):
YouTube creates demand → Google captures the search → Meta converts the retarget → That's a 28-day customer journey → YouTube drives €160K incremental revenue → Keep YouTube, scale it.
Incremental Sales = (Revenue with Channel) - (Revenue without Channel)
Traditional attribution: Measures correlation (which channel touched the customer last)
Behavioral intelligence: Measures causality (which channel drives incremental sales)
Confidence-scored results with data health indicators. Named Dutch ecommerce brands already see their real numbers.
How ecommerce customers actually buy: The paths your dashboard hides
Path #1: YouTube → Google → Meta
Customer watches a review (Day 1) → Googles the product (Day 9) → Clicks Meta retargeting ad (Day 24) → Buys.
Last-click: Meta gets 100% credit. YouTube shows "low ROAS."
Causal analysis: YouTube drives 30-40% of this sale. Cut YouTube = lose the demand that feeds every other channel.
Path #2: TikTok → Branded search → Email
Customer sees TikTok UGC (Day 1) → Searches your brand on Google (Day 6) → Joins the list → Buys from a Klaviyo flow (Day 18).
Last-click: Email gets 100% credit. TikTok shows near-zero ROI.
Causal analysis: TikTok started this journey. Branded search and email harvested demand TikTok created.
Path #3: Meta prospecting → Direct → Meta retargeting
Customer clicks a prospecting ad (Day 1) → Returns direct on mobile (Day 12) → Clicks a retargeting ad (Day 20) → Buys.
Last-click: Retargeting gets 100% credit and looks unbeatable.
Causal analysis: Prospecting found the customer. Retargeting mostly harvested a sale that was already coming.
Pattern: Demand-creating channels (YouTube, TikTok, prospecting) show "low ROAS" in dashboards. Demand-harvesting channels (retargeting, branded search, email) look unbeatable. Cut the creators to fund the harvesters = collapse your funnel.
From upload to causal read
Setup time
Per causal read, no subscription
Why ecommerce attribution is broken by default
Self-attribution inflation
Meta, Google, and TikTok each grade their own homework — click a Meta ad once and Meta claims every sale for days. Add the platform numbers together and they routinely exceed your actual Shopify revenue.
Cross-device, cookieless journeys
Customers discover on mobile, compare on desktop, and buy days later. iOS privacy changes and cookie loss broke the tracking thread pixels depend on. Last-click fills the gap with whatever touched the sale last.
Retargeting and branded search take the credit
The channels that close journeys look unbeatable in dashboards. But most of those customers were already coming. Incrementality — revenue with the channel minus revenue without it — is the only number that answers "what actually moved sales?"
Every dashboard tells a different story
GA4, Meta, Google Ads, and Shopify report four different revenue numbers for the same day. Teams argue about whose dashboard is right instead of deciding where the next euro goes.
Bottom line: Every ecommerce vertical shares the same failure mode: dashboards measure correlation, budgets need causation. Causal analysis on your own GA4 data closes that gap.
Dutch ecommerce teams already see their real numbers
5–10 minutes
from CSV upload to causal read
No pixel
no SDK, no data team needed
€99
full refund if it doesn't move a decision
Dutch DTC teams switched to behavioral intelligence. Not because we are great salespeople. Because once you see which channels drive incremental sales, you cannot unsee it.
Learn the fundamentals
Marketing Attribution
Identifying which marketing actions contribute to conversions and revenue.
Incrementality
The lift in outcomes caused by a marketing activity versus not running it.
ROAS
Measures the revenue earned for every euro spent on advertising.
Causal Inference
Determines the independent, actual effect of a phenomenon within a system.
Data-Driven Attribution
Platform ML model that distributes conversion credit based on observed data patterns.
Multi-Touch Attribution
Assigns credit to multiple marketing touchpoints across the customer journey.
Customer Acquisition Cost
The cost to convince a consumer to buy a product or service.
Cross Channel Attribution
Determines how different marketing channels contribute to customer conversions.
Incrementality Testing
Experiments that measure the true lift a channel delivers.
Counterfactual
What would have happened without the marketing activity — the baseline causal analysis compares against.
Compare attribution tools
Further reading
Shopify Attribution Guide
The complete guide to attribution for Shopify brands
The DTC Playbook
How DTC brands allocate budget when dashboards disagree
Why Your Meta ROAS Is Inflated
Self-attribution, explained with numbers
Marketing Attribution Guide 2026
Attribution models, their failure modes, and what to use instead
Ecommerce attribution questions, answered
Why is my Meta ROAS higher than what Shopify shows for my ecommerce brand?
Meta grades its own homework: click one ad and Meta claims every sale for days, including sales other channels closed. Causal analysis measures incremental sales — revenue with Meta minus revenue without it — on your own GA4 data.
Do I need to install a pixel or change my Shopify theme to get causal attribution?
No. It runs on a GA4 export you upload — no pixel, no SDK, no theme changes. Setup takes about 2 minutes, results arrive in 5–10 minutes, and it works on historical data.
Which of my channels are incremental and which just harvest demand?
Demand creators (YouTube, TikTok, prospecting) start journeys and look weak in dashboards. Harvesters (retargeting, branded search, email) close journeys and look unbeatable. Causal analysis separates them with confidence-scored incremental revenue per channel.
How is this different from Triple Whale, Northbeam, or another attribution dashboard?
Attribution dashboards track clicks and re-assign credit with rules — still correlation. Causality Engine applies Bayesian causal inference to the GA4 history you already have: no pixel, results in 5–10 minutes, no annual contract.
How much data do I need for reliable causal attribution?
A 40–90 day GA4 window is the sweet spot. Every result ships with a confidence score and data-health indicators, so you know how much weight each conclusion can bear.
Skip the queue
Get called in 60 seconds, or take the ecommerce brands playbook.
Leave your number and we call you back within a minute to qualify fit and book a demo. Email-only is fine too: one short note a week, tuned to ecommerce brands, unsubscribe in one click.
Or, if you are ready, book a 20-min call.
Know which channels drive incremental sales
Not guesses. Not correlations. Upload your GA4 data and see the real numbers in minutes.
€99. Results in minutes. Full refund if you don't see it.
Real reports from ecommerce brandss
See what causal attribution looks like for a ecommerce brands.
Anonymised reports from ecommerce brandss already using Causality Engine. Per-channel incremental ROAS with confidence intervals.
Browse Ecommerce-Brands reports in the Attribution Library →