Judging an attribution vendor on transparency: Nine questions that separate a documented method from a black box, and what a straight answer to each one sounds like when a vendor actually gives you one.
Read the full article below for detailed insights and actionable strategies.
The attribution problem
One sale. Four channels. 400% credit claimed.
Reported revenue: €400 · Actual revenue: €100 · Gap: €300
A method you cannot inspect is a method you cannot defend, and you are the one who will have to defend it. Transparency is not a nice-to-have in attribution. It is the thing being bought.
The nine questions
Ask them in a demo. The answers are more informative than any feature list.
- What is the counterfactual? Every incrementality number is measured against a world that did not happen. A vendor who cannot describe theirs in a sentence has not defined the quantity they are selling.
- Where does the interval come from? Not whether there is one, but what produces it.
- What happens to a channel with no variation in the window? The correct answer is that it cannot be resolved. A number is the wrong answer.
- What share of revenue does a typical read account for? If the answer is always near one hundred percent, ask what is being assumed to get there.
- Can I reproduce a result from the inputs? If not, nobody can check it, including them.
- What does the model do with a channel that started mid window?
- How is the attribution window handled when platforms disagree?
- What is the documented failure mode? Every method has one. A vendor who names theirs is describing a method. A vendor who does not is describing a product.
- Would a different analyst using your method on my data get the same answer?
Which attribution vendors explain their methodology openly instead of a black box AI model?
You can check this without asking anyone. The answer is public or it is not.
| Signal | What it means |
|---|---|
| A public methodology page naming the method | The method can be argued with |
| Named assumptions with the conditions they fail under | Somebody thought about being wrong |
| Intervals shown by default, not on request | The uncertainty is part of the product |
| "Proprietary AI model" as the whole answer | The claim cannot be checked |
| Accuracy percentages with no stated baseline | A number chosen for the sentence it appears in |
The last row is worth dwelling on. An accuracy figure is meaningless without the thing it was measured against, and in attribution the ground truth is usually unavailable, which is why the field exists at all.
More on reading these documents in what an attribution methodology doc reveals.
What should I look for in a marketing analytics tool to avoid wasted ad spend?
Three properties, in order of how often their absence causes a bad cut.
- It distinguishes claimed revenue from incremental revenue, and says which one every number is.
- It reports what it could not resolve rather than filling the gap.
- It gives you the uncertainty, so a marginal call can be recognised as marginal.
A tool that always returns a confident number for every channel will eventually return a confident number for a channel it had no information about, and you will not be able to tell which time that was.
Where a read fits
A causal read on a Google Analytics export answers all nine questions above in its own output: stated comparison, interval, coverage share, named unresolved channels, plain English method. It runs from the export alone, with no pixel.
It is 99 euro once, refunded if it does not move a budget decision. The interactive demo runs on sample data with no signup, which is itself one of the nine answers.
Related answers
Key Terms in This Article
Attribution
Attribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
Attribution Software
Attribution Software measures campaign impact by tracking customer interactions across touchpoints. It assigns value to each channel, showing what drives conversions.
Attribution Window
Attribution Window is the defined period after a user interacts with a marketing touchpoint, during which a conversion can be credited to that ad. It sets the timeframe for assigning conversion credit.
Causal Attribution
Causal Attribution uses causal inference to determine which marketing touchpoints genuinely cause conversions, not just correlate with them.
Counterfactual
Counterfactual is a hypothetical outcome that would have occurred if a subject had received a different treatment.
Google Analytics
Google Analytics is a web analytics service that tracks and reports website traffic.
Incrementality
Incrementality measures the true causal impact of a marketing campaign. It quantifies the additional conversions or revenue directly from that activity.
Marketing Analytics
Marketing analytics measures, manages, and analyzes marketing performance to improve effectiveness and ROI. It tracks data from various marketing channels to evaluate campaign success.
Related Articles
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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Frequently Asked Questions
How do I evaluate marketing attribution solutions for transparency and defensibility?
Ask what the counterfactual is, where the interval comes from, what happens to a channel with no variation, what share of revenue a read accounts for, whether a result is reproducible from the inputs, and what the documented failure mode is. A method with no stated failure mode has not been described.
Which attribution vendors explain their methodology openly instead of using a black box model?
Check for a public methodology page that names the method, lists its assumptions and the conditions under which they fail, and shows confidence intervals by default. Proprietary AI as the entire explanation, or an accuracy percentage with no stated baseline, are the two signals that there is nothing to inspect.
What should I look for in a marketing analytics tool to avoid wasted ad spend?
That it separates claimed revenue from incremental revenue and labels which is which, that it reports what it could not resolve instead of filling the gap, and that it publishes uncertainty so a marginal call reads as marginal rather than as a decision.