Explaining a move away from multi-touch attribution: Someone in the room approved the tool you are replacing. Three arguments that work, one that never does, and how to structure it so nobody has to be wrong.
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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
Somebody in the room approved the tool you want to replace, so the argument has to be about what changed rather than about what was wrong. That framing is not diplomacy, it is accurate: multi-touch attribution worked considerably better in 2019 than it does now, and the reason is external.
Three arguments that land
| Argument | The evidence to bring |
|---|---|
| Coverage has eroded | Your own consent rate and unassigned traffic share, now against two years ago |
| The claims do not reconcile | Platform-claimed revenue against actual orders for one window |
| The method cannot answer the question asked | A channel where credit is high and the counterfactual is obviously weak |
The first is the strongest because it is measured on your own data and requires no theory. Pull your unassigned traffic share and your consent rate for this quarter and the same quarter two years ago. If the trend is what it is for most European brands, the slide writes itself, and the argument becomes "the instrument lost coverage" rather than "the instrument was bad".
The argument that does not land
"Causal inference is more rigorous." True and useless in a board meeting, because it asks a room of non-specialists to adjudicate a methodological claim they cannot evaluate. It also sounds like the pitch the previous vendor made.
Replace it with the reconciliation. Platform claims summing above actual revenue is a fact anyone can check, needs no statistics, and makes the point about credit allocation without requiring anybody to take a position on method. It is set out in the claim ratio.
The slide structure
Four slides. What we used and why it made sense. What changed externally, on our own data. What we propose, in one sentence, including its limits. What we will check in ninety days to know whether it was right.
The fourth slide is the one that gets approval, because it converts a permanent decision into a reversible one. Name the specific number you will report back on.
Pre-empt the two questions
"Will the numbers get worse?" Yes, in the sense that credit will be lower for channels that were being over-credited, and thresholds need resetting. Say this before you are asked, because discovering it in month two looks like concealment.
"How do we know the new number is right?" You do not, in the strong sense, and the correct answer is that the new number arrives with a confidence interval, a coverage share and a design label so its strength is visible, which the previous one did not. That is a better claim than accuracy and it is defensible. The mechanics are in how to prove marketing incrementality in budget meetings.
Making it cheap to try
The lowest-friction version of this proposal is a parallel read on a window already reported by the existing tool: a €99 one-time upload of a Google Analytics export, refundable if it does not move a decision. Bringing the comparison rather than the proposal changes the meeting entirely, and the interactive demo lets anyone in the room see the output beforehand.
The tone that works
Not "we were wrong". Something closer to: the ground moved, here is the measurement on our own data, here is what I propose, here is when I will report back. Nobody has to lose an argument for that to be approved.
Related answers
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Key Terms in This Article
Analytics
Analytics is the systematic computational analysis of data. It reveals customer behavior and measures campaign performance.
Attribution
Attribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
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.
Incrementality
Incrementality measures the true causal impact of a marketing campaign. It quantifies the additional conversions or revenue directly from that activity.
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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