The one-way pipe
Here is how most teams think about distribution. You build a feature. You ship it. Someone writes a blog post, sends an email, publishes a tweet. Traffic arrives, some of it converts, and you note the results in a spreadsheet that nobody looks at again. Then you build the next feature. The process repeats.
This model treats distribution as downstream of product. Product makes decisions about what to build. Distribution takes whatever product built and tries to generate attention for it. The arrow only points in one direction. Product informs distribution. Distribution does not inform product.
This is wrong, and the cost of getting it wrong is significant. Teams operating a one-way pipe make product decisions based on intuition, customer interviews, support ticket volume, and competitive analysis. These are all useful inputs. But they are all forms of stated preference — what people say they want, what they complain about, what competitors seem to prioritize. Stated preferences are noisy. They are biased by recency, by who speaks loudest, by what questions you happen to ask.
There is a better signal hiding in plain sight. Your distribution system, if it captures the right data, is running a continuous experiment on what your market actually values. Not what they say in interviews. What they do when you show them proof.
What proof-level attribution reveals
Most attribution is too coarse to be useful for product decisions. "Social media drove 30% of signups this quarter" tells you something about channel allocation. It tells you nothing about what to build.
Proof-level attribution is different. When you can trace a specific outcome — a signup, a demo request, a purchase — back to a specific proof (a particular claim, expressed in a particular way, shown to a particular segment), you learn something much more valuable than which channel works.
You learn which features your market cares about enough to act on.
Consider a concrete example. You ship three features in a month: a new integration, a performance improvement, and a collaboration tool. Your distribution system generates proofs for each one and distributes them across channels. A month later, the attribution data shows that proofs about the performance improvement converted at dramatically higher rates than proofs about the integration or the collaboration tool.
Campaign-level attribution would tell you: "The September campaign performed well." Proof-level attribution tells you: "Your market values performance more than features. The segment that responded most strongly to the latency benchmark was mid-market engineering teams evaluating alternatives to their current vendor. The framing that converted was the raw benchmark with methodology, not the narrative about engineering effort."