Causal Attribution
for Ecommerce Brands

Incremental marketing, holistically. Not just paid ROAS.

Paid-channel ROAS is the first place teams look, but it is not the whole picture. Organic search created demand. Email captured it. Brand recognition shortened the consideration window. Retention spend defended the LTV. Each activity has an incremental component, and most of them are invisible to a last-click dashboard.

Causal attribution treats marketing as one connected system and estimates the incremental contribution of every input the data allows. Paid channels, organic, email, content, brand, retention. The question is always the same: what would have happened without this activity? The answer is always a number with a confidence interval, never a guess in a suit.

Platform attribution tells you who to thank. Causal attribution tells you what to cut.

Last-click gives the sale to whatever the buyer touched last. Multi-touch spreads credit by a formula you did not choose. Each ad platform counts the same conversion in its own dashboard, so your channels routinely add up to more than 100% of the orders you actually shipped, which is mathematically impossible and extremely common.

That overcounting is invisible until you measure incrementality. The practical cost is that budget moves to the channel reporting the biggest number, not the channel causing the most sales.

How causal attribution works

Causal attribution treats your marketing like an experiment that already happened. Instead of trusting each platform's self-report, it models what would have occurred in the counterfactual (the world where a given channel's spend was lower), using your real sales history and the natural variation already in your data.

The output is incremental ROAS: revenue caused, not revenue claimed. Because the method leans on causal inference (the same statistical machinery used to evaluate medicine and policy), every number ships with a confidence interval, so you know how sure the model is before you move money.

What you get

• Incremental impact per activity, not just paid channels

  Per-channel paid ROAS, plus the incremental contribution of organic, email, brand, and retention. The whole marketing system, measured causally.

• Wasted spend and under-invested channels surfaced

  Both the budget propping up channels that are not causing incremental sales and the under-invested channels that are quietly driving demand.

• Confidence intervals on every estimate

  Honest uncertainty, not a single confident-looking number.

• A plain-English report

  What to scale, what to cut, what to test next. No statistics degree required.

From data to decision in three steps

1. 01

   Upload

   Export 90 days of Shopify and GA4 data. No pixel to install, no tag manager, no engineering ticket.

2. 02

   Analyze

   The causal model runs in about 90 seconds and estimates each channel's incremental contribution.

3. 03

   Decide

   You get incremental ROAS with confidence intervals and a clear read on where spend is working and where it is not.

Why brands trust the numbers

We document every assumption: prior, functional-form choice, covariate, robustness check. A causal claim is only as good as the assumptions under it. Our methodology document is available to any customer who asks. The goal is not a prettier dashboard. It is a number you can defend in a budget meeting.

Who it is for

DTC and ecommerce brands on Shopify, the performance marketers running their paid channels, and the agencies who answer to clients for ROAS. If you are making budget decisions off three dashboards showing three different numbers, causal attribution is the tie-breaker.

Frequently asked questions

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Where this fits in the causal stack