Content Revenue OS
An operating layer for content teams: the decision that sits between the systems they already have, measured by Content Yield.
Explore the logic
Same page, different signals, different answer
The interesting part of this product is not a dashboard — it is what it refuses to recommend. Select the signals below and watch the reading change. Some combinations produce a priority. Some are honestly not enough to act on, and say so.
Explorable model — illustrative logic, not live product output
2 / 4 signals
Signals available
Select the ones you have. Two or more from different sources are needed before the model will say anything.
The explanation sales repeats by hand is also being searched for
- Why it surfaced
The same question shows up in evaluation conversations and in search.
Work that serves both is cheaper to justify than work that serves one.
- Still needs verification
That the manual explanation and the searched question are actually the same question
Whether sales would use a public page or prefer to keep it in conversation
- Possible next action
Write it once as a public page, and give sales a link instead of a paste.
- Measure afterwards
Whether the page is used inside deals, and whether it holds the query group.
The problem
A content team of any maturity already owns four or five systems: a search console for query data, an analytics tool for behavior, a CRM for pipeline, spreadsheets for planning, and a task tracker for execution.
None of them answer the operating question — what should we do next, and why that instead of the other twelve things. Prioritization then falls back to intuition, seniority, or whoever asked most recently.
Dashboards do not fix this. They report state. They do not produce a decision, and they do not close the loop between an action and its effect.
The concept
Three layers, deliberately in this order:
Content operations
Inventory, ownership, briefs, refresh queue, and workflow state.
Performance
Query, behavior, and pipeline signals joined at the page level.
Delegated execution
Structured work handled by agents under human review.
North star: Content Yield
Content Yield is the return a page or cluster produces relative to the effort invested in it — expressed in qualified progression, not sessions. It makes refreshing an existing page comparable to writing a new one, which is the comparison most teams never get to make. It is stated here as a definition, not a score: the inputs are exactly the things a team has to agree on for itself.
Architecture
- 01
Signals
Query data, on-page behavior, pipeline movement, and backlog state, joined by consistent page identity.
- 02
Opportunities
Where those signals disagree or converge: overlap, decay, unserved intent, missing next steps.
- 03
Priorities
Ordered by expected yield against effort, so the backlog reflects a decision rather than a wish list.
- 04
Actions
Briefs, merges, internal linking, and enablement changes — with structured work delegated under review.
- 05
Measurement
Each action tied back to what was supposed to move, which is the loop a spreadsheet normally breaks.
What is and is not true today
True today
- The operating logic exists and is published, above, in explorable form.
- The concept and data model are being worked on.
Not true today
- There is no software to use, and no availability or pricing.
- No customer data has been processed by any of it.
- Nothing on this page reports real output from a running system.
Opportunity queue mockup
Content Yield model