Attribution belongs where the decision happens: The distance between where a number is produced and where a decision is made is where most measurement work dies. Closing it is a design problem, not a tooling one.
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The attribution problem
One sale. Four channels. 400% credit claimed.
Reported revenue: €400 · Actual revenue: €100 · Gap: €300
A measurement that lives in a tool nobody opens has the same effect on your budget as no measurement at all. The distance between where a number is produced and where a decision is taken is where most analytics work quietly dies.
Closing that distance is a design problem rather than an integrations problem, and treating it as an integrations problem is how teams end up with a Slack channel full of ignored charts.
Three places a number can live
| Where it lives | What has to happen for it to matter | Failure mode |
|---|---|---|
| A dashboard | Someone remembers to open it | Nobody opens it |
| A pushed message | Someone reads it in the moment | It becomes wallpaper |
| The document where the decision is written | Nothing extra | Somebody has to put it there |
The third works and it is the one nobody builds, because it requires deciding where budget decisions are actually recorded. In most brands that is a recurring document or a meeting agenda, not a tool.
What to say plainly about tooling
Causality Engine has no native Slack app and no Notion integration. What exists on Pro at €299 a month is a set of developer API keys and an MCP server, which is the tier where results can be moved programmatically into whatever system you use. Below that, at the €99 one-time read, the route is the export.
That is a smaller claim than "integrates with your workflow" and it is the accurate one. An MCP server means a model or agent can call the read and put the output wherever it already has access, which covers Slack, Notion and most other destinations without us building a connector for each.
Why the pushed message so often fails
A recurring automated post becomes background within about three weeks. The predictable fix is to post less: only when something crosses a threshold agreed in advance, and never on ordinary movement in a causal estimate, which is supposed to move.
The related discipline, alerting on the job rather than on the metric, is covered in automating attribution reporting in one afternoon.
What belongs in the decision document
Per channel: the estimate, its confidence interval, the coverage share of orders, and the design label. Then the decision, the date, and what would change it. That last line is what makes the document worth returning to, and it is the part every template omits.
The structure is set out in a defensible attribution report you can export.
The test worth applying
For each place you are considering putting a number, ask what happens if nobody looks at it for a month. If the answer is "nothing", it is a dashboard. If the answer is "the budget review has a hole in it", it is the right place.
Where to start
Before building any pipe, run one read and put the output by hand into wherever your budget decisions are recorded. If it changes the conversation, automate it. If it does not, the pipe would not have helped. That first read is €99 once at Causality Engine, on a Google Analytics export, refundable if it does not move a decision, and the interactive demo shows the output first.
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.
Attribution Report
Attribution Report shows which touchpoints or channels receive credit for a conversion. It identifies which campaigns drive desired actions.
Causality
Causality is the relationship where one event directly causes another, essential for identifying specific actions that drive desired outcomes in marketing.
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.
Dashboard
A dashboard is a visual display of key information required to achieve specific objectives. It consolidates data onto a single screen for quick review.
Google Analytics
Google Analytics is a web analytics service that tracks and reports website traffic.
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