Using competitor gaps in AI answers to pick pages to write

What a competitor gap row is

A competitor gap is an answer that names a competitor and leaves you out, counted across your daily runs. Sort the rows by how often it happens and you have a ranked list of the questions that hand the decision to someone else.

In advisible, Competitor gaps is the starting list: one row per prompt where tracked rivals appear and you do not, with counts drawn from the runs already collected, so nothing new has to be set up before you can read it. What the row does not tell you is why the answer skipped you, and that is the next step, because two gaps with the same count can need completely different work.

Sort by frequency, then read the answers

The count tells you where the loss happens; the answer text tells you why. Read the actual answers behind the top rows before writing or briefing anything.

1. Sort by frequency. Also check the spread across engines. A gap on all five engines is a content problem; a gap on one engine alone is usually that engine's source mix.
2. Open the answers behind each row. Note the cited URL, the shape of the question (listicle request, comparison, definitional, pricing), and where your brand appears if it appears at all.
3. Read the competitor's cited page. What does it have in the first lines that yours does not: a worked price, a table, a direct definition, a date on it?
4. Write the reason next to the row. One line, because that line decides which of the three fixes applies.

Classify the gap before you write

Every gap is one of three problems: no page exists, a weak page exists, or the answer leans on third-party sources you are absent from. The fix differs, and writing is only the right answer for the first two.

The third class is the one teams skip because it feels outside the content queue. It is where a lot of recommendation prompts actually live; the category map is in which sources AI engines cite.

Which gap to work first

Score each gap on three things: how often it occurs, how close the prompt sits to a purchase decision, and whether a citable source can back the answer. Work the highest score first.

Frequency is the raw count and the spread across engines. Purchase proximity separates “best X for small teams” and “X vs Y pricing”, where the reader is about to buy, from “what is X”, which matters for presence but rarely for pipeline. Citable source asks whether the answer has something concrete to pull: your own docs can supply a price or a definition on demand, while a gap that needs an independent review needs that review to exist before writing alone will move anything.

HubSpot's competitor-analysis guide puts the same instinct as a rule of thumb: when a competitor earns citations on 8 of 10 queries in a topic cluster and you earn 0, purpose-built Q&A content is the fastest route in (their AEO competitor analysis guide). Worth adding: clusters where no rival has strong content are the cheapest wins of all, because the answer only has to find something citable.

Re-run, then watch the right number

After you publish or update, trigger fresh runs and watch Visibility on those specific prompts rather than the project average, which is diluted by prompts you did not touch.

In advisible, Run now gives you minutes-after answers instead of waiting for tomorrow's schedule; then leave the prompts in the daily runs and read them weekly. Two things to watch: whether the target prompts start mentioning you (each number shown with its sample size, so a quiet day cannot masquerade as a trend), and whether your domain starts appearing in the sources behind those answers, which moves before mentions usually do.

Be prepared for the gap to close on one engine first and to reopen. Answers are reassembled on each run, so the measure of progress is the count over a week of runs, not the answer you screenshotted on publication day. Sample-size basics are in how many answers before you trust a number.