What multi-touch attribution never actually measured: Set coverage aside. Even working perfectly, a path model answers a different question from the one you are asking. The gap is older than the privacy problem.
Read the full article below for detailed insights and actionable strategies.
Customer journey
How attribution misses the real journey
One conversion. Five touchpoints. Last-click credits the final touch with 100%.
Last-click attribution
Every other channel gets zero credit, even though they created the demand.
Causal inference
Set the coverage problem aside entirely and assume a path model sees every touch. It still answers a different question from the one a budget owner asks. That gap predates the privacy problem by a decade and is the more interesting of the two.
Two different questions
| The question a path model answers | The question you are asking |
|---|---|
| Given that this order happened, how should credit be divided among the touches on its path? | If I had not run this channel, would this order still have happened? |
The first is an accounting exercise. It takes conversions as given and allocates them, which means the total is fixed in advance and channels compete for shares of a number that does not depend on whether they existed.
The second is a counterfactual question. It asks whether the total itself would have been different, which is what a budget decision needs, because budget changes whether the channel runs.
Why the difference is not academic
Consider a channel that only ever appears on paths belonging to people who were going to buy anyway. Under any credit-allocation rule it receives credit, because it was present. Under a counterfactual question it receives close to nothing, because removing it would not have changed the outcome.
Branded search and retargeting are the two textbook cases, and they are textbook cases because they are common. The mechanism is set out in the correlation versus causation problem.
The part that surprises people
Changing the credit rule does not fix this. First-touch, last-touch, linear, time-decay and data-driven are all allocation rules, and every one of them divides a fixed total among observed touches. Moving from last-click to a data-driven rule changes who gets the credit; it does not change the question being answered.
That is why teams who upgrade their attribution model repeatedly and remain dissatisfied are not doing it wrong. They are solving the allocation problem better and the causal problem not at all. The comparison is in causal inference versus rule-based attribution.
What answering the real question requires
A comparison against something that did not happen. Constructed by randomisation in a holdout test, by an accident that behaved like one, or by modelling variation that already exists. All three are harder than allocation, and all three answer the question you actually have.
The weakest of them, the observational read, is still answering the right question, which is why it can be more useful than a sophisticated allocation rule despite being less precise.
Where this leaves the tooling
Path models remain useful for describing behaviour and for diagnosing where journeys break. They are the wrong instrument for allocating budget, and they were the wrong instrument before consent rates started falling.
Causality Engine returns a per-channel causal estimate with its confidence interval, coverage and design label from a Google Analytics export, at €99 for a first read, refundable if it does not move a budget decision. The interactive demo shows what a counterfactual answer looks like next to an allocation, with no signup.
The one-sentence version
Allocation asks who to thank. Causal inference asks what to fund. Only one of those is a budget question.
Related answers
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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 Model
An Attribution Model defines how credit for conversions is assigned to marketing touchpoints. It dictates how marketing channels receive credit for sales.
Causal Inference
Causal Inference determines the independent, actual effect of a phenomenon within a system, identifying true cause-and-effect relationships.
Confidence Interval
Confidence Interval is a statistical range of values that likely contains the true value of a metric. In marketing analytics, it quantifies uncertainty around estimates, indicating the precision of an outcome or causal effect.
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.
Holdout Test
A holdout test is an experiment where a portion of the audience does not see a campaign. This measures the campaign's true incremental impact.
Multi-Touch Attribution
Multi-Touch Attribution assigns credit to multiple marketing touchpoints across the customer journey. It provides a comprehensive view of channel impact on conversions.
Related Articles
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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