Machine readable attribution for AI agents: What an attribution result has to carry to be usable by an agent, a Slack bot or a Notion database, rather than only by a human reading a chart image.
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 chart is a dead end for an agent. A row with an estimate, an interval, a coverage share, and a window is not. The difference decides whether a result can be acted on automatically or has to be retyped by a person first.
How to export machine readable attribution data for AI agents
The format matters less than the fields. JSON, CSV, or a table in a database all work. What has to survive the export is everything a human would have needed to judge the number.
| Field | Why an agent needs it |
|---|---|
| Channel identifier | To join against spend and campaign data |
| Estimate | The value itself |
| Interval bounds | To refuse to act on a result that includes zero |
| Coverage share | To know how much of revenue the read explains |
| Resolution status | So unresolved is distinguishable from zero |
| Date range | To avoid comparing across different windows |
| Attribution window per platform | The most common reason two rows are not comparable |
The row that changes agent behaviour most is resolution status. Without it, a channel the method could not resolve arrives as an absence, and an absence reads as a zero. An agent that treats unresolved as zero will recommend cutting the channels it knows least about, which is precisely backwards.
How to integrate attribution results into Slack or Notion
Both are consumers of the same structured output, and both introduce the same failure if you let them.
- Post the table, not a chart image. An image loses the interval, and the interval is the reason the number is trustworthy.
- Keep the window on the message. A Slack post that says a channel is at 2.1 without a date range will be quoted three weeks later as though it still holds.
- Make unresolved visible in the message body, not in a thread reply nobody expands.
- In Notion, give interval bounds their own properties rather than putting them in a text field, so views can filter on them.
How to monitor channel performance continuously without manual uploads
Continuous monitoring is a data pipeline question rather than an attribution question. The requirement is that the inputs arrive without a person exporting them: scheduled Google Analytics exports, platform spend via API, and order data from the store.
It is worth naming the trade honestly. Continuous monitoring is genuinely valuable when someone acts weekly on what it shows. When the real decision cadence is quarterly, a continuously updating dashboard mostly produces noise to react to, and a defensible read before each decision is the better shape. That cost comparison is in the real cost of causal attribution software.
Why this matters more than it used to
Assistants now read pages and answer buying questions from them. A measurement result published as a chart image is invisible to that entire class of reader. A result published as structured data with its uncertainty intact can be quoted correctly, which is the only kind of quoting worth having.
The same logic applies to your own methodology documentation, as in what an attribution methodology doc reveals.
Where a read fits
A causal read on a Google Analytics export returns exactly the seven fields in the table above, per channel, in a form that survives being passed to something other than a human. The interactive demo shows the shape on sample data, with no signup.
A read is 99 euro once, refunded if it does not move a budget decision. Connections to the data sources are listed on the integrations page.
Related answers
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.
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.
Correlation
Correlation is a statistical measure showing a relationship between variables; it does not imply causation.
Google Analytics
Google Analytics is a web analytics service that tracks and reports website traffic.
Related Articles
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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Frequently Asked Questions
How do I export machine readable attribution data for AI agents?
Export one row per channel carrying the channel identifier, the estimate, both interval bounds, the coverage share, a resolution status, the date range, and each platform's attribution window. Format is flexible. Dropping any of those fields is what makes the export unusable without a human.
How do I integrate attribution results into Slack or Notion?
Send the table rather than a chart image, keep the date range in the message itself, and make unresolved channels visible in the body rather than in a thread. In Notion, store interval bounds as their own properties so views can filter on them.
How can I monitor channel performance continuously without manual uploads?
Schedule the GA4 export, pull platform spend by API, and read orders from the store, so no person exports anything. Worth checking first that your decision cadence is actually weekly. If budget decisions are quarterly, continuous updates mostly create noise between them.