Not a keyword alert. An opinion about every post.
Every post that matches your theses gets read, judged, and either dropped or drafted against. Nothing posts without your approval.
Watches X, LinkedIn, and Reddit for the conversations your theses care about: your category, your competitors, the problems you solve. Reading the feeds all day is the job description.
Every matched post gets typed: a complaint about a competitor, a buying question, a hot take in your category, noise. Not a keyword hit, a judgment about what the post is.
How much does this one matter? Fit against your ICP and personas, reach of the author, heat of the thread. High scores surface, low scores die quietly.
A reply drafted in your voice, grounded in your profile and the thread it answers. Written to join the conversation, not to hijack it.
Drafts land in a Slack channel with the post, the classification, and the score. Approve, edit, or kill without leaving Slack.
Someone complains about your competitor on LinkedIn today; Distrosauce researches them, matches a persona, and writes the sequence.
A real artifact, sanitized. Names and specifics changed, the machinery as it ran.
Sooner or later, every CMO ends up in this meeting.
The CFO wants to know which marketing programs actually produce revenue. The truthful answer is some flavor of: we can’t trace it cleanly. So the budget conversation happens without real data behind it.
That isn’t a reporting problem.
It’s a decision infrastructure problem.
At Brightpath Health, wiring AI call analytics into our campaign stack drove a 10x lift in qualified leads. Same campaigns as before. We just finally had the signal to optimize toward what mattered downstream.
Next time your CFO asks which programs drive revenue, what can your attribution model actually back up?
Mark Ellison articulates a decision infrastructure problem: CMOs can’t cleanly connect marketing programs to revenue, so budgets get decided on activity metrics instead of impact. He frames it as measurement architecture, not reporting, and backs it with a concrete outcome. High-confidence prospect signal from someone who understands the stakes.
Watched this exact movie at a client last year - 30-some paid search campaigns burning about $40K a month, and only a handful were producing revenue. The rest graded out fine on activity metrics (CTR, CPM, even last-touch conversions) with zero connection to what landed in the bank account. Building the measurement architecture before the campaign runs is the right framing - the failure mode is attribution that reconciles to platform-reported numbers instead of real revenue, which just hands you a more confident version of the wrong answer.
Feeds outbound: promoted posts become signals, and signals become sequences.