Core product · Marathon
Marathon connects analytics to the next approved action. It learns what moved recommendations, what stalled, and where the content program should go next.
The operating principle
Analytics should change what the system does next.
Marathon carries outcomes from one cycle into the next. It does not treat every month as a blank content calendar.
The control room
Choose what happened after publishing. Marathon weighs the evidence, proposes a response, and leaves the final decision with you.
Expand
The page earned a new recommendation and two citations. Continue the working angle with a narrower follow-up question.
Recommendation gained · traffic rising · evidence collected for four weeks
Memory across cycles
Every recommendation keeps its inputs, rationale, approval, shipped work, and later outcome. Marathon can use that history without hiding the decision trail.
What enters the decision
Question-level positions across ChatGPT, Gemini, and Perplexity.
The article, target question, sources, approval, and date it went live.
Traffic and engagement show whether the page is earning attention.
Your approval and feedback become context for the following cycle.
What “learns” means here
Marathon continuously uses prior outcomes as context for the next recommendation. You can inspect the evidence, correct the direction, and approve what happens.
Every question, every week
Keep the work moving in the right direction
Marathon measures the result, proposes the move, and carries the lesson into the next cycle.
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