swan-gtm Use when brand presence in AI answers is being scored, compared, or reported over time. Produces a per-question presence verdict with vote counts, backed by the full answer text as evidence.
Sample before scoring
Answer engines are stochastic: the same question, asked twice in the same hour, returns different answers with different names in them. A score built from one ask per question is noise wearing a number. Ask every question at least three times per engine, capture each full answer verbatim, and let a majority vote decide presence. Keep the vote visible in the output — a question won three-of-three is a different fact from two-of-three, and the margin is where next month's movement shows first.
Define what counts as present
Three different events hide inside "the brand showed up": named as a recommendation (the engine offers the brand as an answer to the buyer's question), mentioned in passing (the name appears without endorsement), and cited as a source (the brand's site fed the answer). Decide the tiers before scoring and apply them mechanically. The strictest tier — named as a recommendation — is the one that predicts buyers arriving; report it separately, never blended.
Score questions, not brands
One aggregate score hides everything an operator can act on. Build the readout as a grid: question by engine, vote count in each cell. The aggregate can sit on top, but the grid is the product — it shows which questions are won, which are contested, and which engine disagrees with the rest.
Read movement honestly
Re-measure the same question set on a fixed cadence. A flip from zero-of-three to three-of-three is movement; a wobble at the margin is weather. When the question set has to change because the buyer's language moved, mark a break in the series and restate the baseline. Splicing old and new sets into one line manufactures trends that never happened.
What good looks like
The best operators read variance before they read the score: an unstable answer means the engine is undecided, and undecided questions are the winnable ones — that instability list is the work queue. The mediocre version is a single-shot scan producing one aggregate number, no raw text kept, and a question set that quietly changes between measurements, so nothing can be traced or compared. Good output lets a skeptic pick any cell in the grid and be shown the dated, verbatim answers behind it in one step.
Rules
- MUST keep the full verbatim text of every sampled answer, dated.
- MUST show vote counts, never just the verdict.
- NEVER score from a single ask per question.
- NEVER splice measurements across a changed question set without marking the break.