Machine-readable attribution keeps you portable
A number you can only see in an interface is a number you cannot move. Structured output is an ownership question before it is an automation one.
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By Joris van Huët, Founder & CEOUpdated 2 min read
A number you can only see in an interface is a number you cannot move, and portability is an ownership question before it is an automation one.
What structured output buys
| Benefit | Why it is an ownership property |
|---|---|
| Re-readable later | Your history survives a vendor change |
| Comparable across methods | You can run a second read on the same window |
| Auditable | Somebody else can check your work |
| Composable | It goes into a warehouse, a memo, or an agent |
The second is the one that matters most and gets discussed least. If you can hold last year's per-channel estimates as structured records, you can run this year's method against last year's window and see whether the two agree. That is the only real validation available for a measurement approach.
What a rendered chart takes away
A screenshot of a chart preserves the impression and loses the record.
So a year of chart exports is not a year of measurement history. It is a year of pictures. The general argument is in you do not own your attribution data.
Why this connects to agents
Because an agent is simply the most demanding consumer of the same property. Anything structured enough for a model to reason over correctly is structured enough for you to re-read in three years. Designing for the first gets you the second for free, which is the practical reason to care about the format even if you never run an agent.
The source stays yours either way
A window you can export is a window any method can read, now or later. The €99 one-time read is deliberately the version with no standing connection at all, refundable within 30 days, no questions asked.
Related answers
Frequently asked questions
Why does machine-readable output matter if I do not use agents?
Because the same structure that lets a model reason correctly lets you re-read your own history in three years. Portability, auditability and the ability to run a second method on the same window all depend on it.What is wrong with exporting charts?
A chart preserves the impression and loses the record. A year of chart exports is a year of pictures, not measurement history.
Go deeper: Causal attribution, explained.
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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Terms in this article
- AttributionAttribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
- ImpressionAn Impression counts each time an ad or content displays on a user's screen. It measures exposure, not engagement.
- ShopifyShopify is an ecommerce platform for creating online stores and selling products. Attribution modeling shows which marketing channels drive traffic and conversions within Shopify.