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.
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By Joris van Huët, Founder & CEOUpdated 3 min read
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 |
| 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 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: what it caused next to what last-click gave it, a data-health score for how far to trust it rather than a confidence interval, and a next step. From a Google Analytics export, at €99 for a first read, refundable within 30 days, no questions asked.
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.
Related answers
Frequently asked questions
What should a marketing decision log contain?
The date and window of the read, per channel the estimate with its data-health score, the decision taken, and what would have changed it. The last field is what makes the log testable rather than merely archival.Why keep the record outside the attribution tool?
Because the tool holds numbers and not reasoning, and because access usually ends with the contract. A log in a workspace you control is portable by construction and holds the part no vendor stores, which is why the decision was taken.Is it worth automating the decision log?
The numbers, perhaps. The reasoning, no. Writing the decision by hand is the step that requires thought, and automating it removes precisely the part that has value. Most teams find manual entry both sufficient and better.
Go deeper: Causal attribution, explained.
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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Keep reading
Terms in this article
- AnalyticsAnalytics is the systematic computational analysis of data. It reveals customer behavior and measures campaign performance.
- AttributionAttribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
- CausalityCausality is the relationship where one event directly causes another, essential for identifying specific actions that drive desired outcomes in marketing.
- ClickClick is the action a user takes to interact with a digital advertisement, redirecting them to a website or landing page. Clicks are a fundamental metric for measuring ad engagement and a primary input for click-based attribution models.
- Confidence IntervalConfidence 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 AnalyticsGoogle Analytics is a web analytics service that tracks and reports website traffic.
- RevenueRevenue is the total income generated by the sale of goods or services related to a company's primary operations.