Keeping the attribution record in your own Notion: Your vendor's history ends with the contract. A decision log in your own workspace does not. What to record per read, and why the date matters most.
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 decision log in a workspace you control outlives every measurement vendor you will use, and it is the only artefact that lets you audit your own judgement later. The vendor's history ends with the contract. Yours does not have to.
What to record, per read
| Field | Why it matters in a year |
|---|---|
| Date of the read and the window it covered | Everything else is meaningless without it |
| Per channel: estimate, interval, coverage | Distinguishes strong calls from lucky ones |
| The design label | Lets you rank this evidence against later tests |
| The decision taken | The point of the exercise |
| What would have changed it | The only field that tests your reasoning |
The last field is the one that makes the log worth keeping. Recording that you would have decided differently if the interval had sat below a certain point turns a note into a testable commitment, and next year you can check whether you honoured it.
Why the vendor's history is not enough
Most tools let you export a summary. Few let you reconstruct why a decision was taken, because they hold numbers and not reasoning. And when the contract ends, even the numbers usually stop being reachable in a usable form, which is argued in you do not own your attribution data.
A log in your own workspace has neither problem. It is portable by construction and it holds the part no vendor stores.
The retrospective this enables
Once a year, take the decisions you logged and check them. Which channels did you scale, and did revenue follow. Which did you cut, and did anything change. Which reads had wide intervals that you acted on anyway, and how did those turn out.
That review is the only reliable way to find out whether your measurement is any good, and it is impossible without the log. It is also the exercise that most reliably improves a marketing team's judgement, because it converts opinions about method into evidence about outcomes.
Getting the data in
There is no Notion connector. The routes are the export, or on Pro at €299 a month, developer API keys and an MCP server that lets an agent already in your workspace write the entry. The comparison of routes is in putting attribution results into Slack or Notion.
For most teams the manual entry is fine and arguably better, because writing the decision by hand is the part that requires thought, and automating it removes exactly the step that has value.
What the read gives you to record
Causality Engine returns per channel: the estimate, its confidence interval, the coverage share of your orders, and a design label, plus a named list of channels below the measurable floor. From a Google Analytics export, at €99 for a first read, refundable if it does not move a budget decision.
The interactive demo shows the output shape so you can design the log template before you have anything to put in it.
The one habit
Never take a budget decision on a measurement without writing down the number it rested on and the date. Two minutes, and it is the difference between a team that learns and a team that repeats.
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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.
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.
Google Analytics
Google Analytics is a web analytics service that tracks and reports website traffic.
Revenue
Revenue is the total income generated by the sale of goods or services related to a company's primary operations.
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Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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