AI NativeA scan can produce hundreds of prompt results across multiple personas, funnel stages, and AI engines. Not every gap is equally worth closing. The opportunity score takes three inputs, value, gap, and winnability, and multiplies them together to produce a single ranked number for each prompt cell. The result is a prioritised queue that separates the gaps worth acting on from the long tail.
The three inputs
Value is how much it would matter to close this gap if you could. It is the product of the prompt's demand signal, its commercial intent weight, its funnel stage weight, and the business weight of the persona it belongs to. A high-value cell is one where the buyers are numerous, their intent is close to a decision, and they are a persona the business cares about. See Demand and value scoring for how value is computed.
Gap is how far your current visibility position is from the top of the ladder. It is calculated as 1 minus your ladder mean for this prompt. A ladder mean of 0 (brand absent) produces a gap of 1.0: the maximum. A ladder mean of 1.0 (consistently recommended first) produces a gap of 0: nothing to close. Gap also incorporates sentiment and accuracy deficits. If you appear in answers but are framed negatively or factually incorrectly, the effective gap is treated as larger than the ladder position alone would suggest, because presence with bad framing is not worth as much as presence with positive framing.
Winnability is how much your actions can realistically influence the outcome. It is estimated from the source landscape: which domains are cited in the answers for this prompt, whether your own domain is already among them, and how many distinct third-party sources are currently shaping those answers. More entrenched third-party sources that you do not control means lower winnability. If your own domain is already cited, you have a foothold and winnability is higher, because deepening existing presence is more tractable than building from nothing.
How the score is computed
The opportunity score for a prompt is:
value × gap × winnability
All three inputs are between 0 and 1. A prompt where you are absent (gap = 1.0), the buyers are high-value (value near 1.0), and the source set is influenceable (winnability near 1.0) produces a score close to 1.0 and appears at the top of the queue. A prompt where you already lead (gap near 0) produces a near-zero score regardless of the other inputs, because there is little left to close.
Prompts are sorted by score descending. The top of the list is where to start.
Attack and defend queues
The opportunity list is split into two queues: attack and defend.
Attack covers prompts where your current position is weak (ladder mean below 0.75) or where you are absent. These are gaps to close. The action plan for attack prompts focuses on getting into the conversation, moving up the ladder, and displacing the sources that are currently shaping the answer without you.
Defend covers prompts where you are already strong (ladder mean at or above 0.75) but competitors are present in the same answers. These are positions to protect. The action plan for defend prompts focuses on maintaining source relationships, deepening the presence you have, and monitoring for competitors moving against you.
The action plan attached to each opportunity
Every scored prompt carries three specific action lines.
The content action describes what to create or strengthen on your own properties. For unbranded prompts, this typically means a page targeting the head keyword with the proof, schema, and structure that earns citations for that question type. For branded prompts, it typically means strengthening owned pages and structured data so the AI's branded answers draw on your own authoritative content.
The distribution action identifies the third-party domains currently shaping the answer for this prompt and frames the task as earning a citation there, through coverage, a reference link, reviews, or outreach. If no third-party sources are present yet, the task is building first-mention presence on the category sources that cover this topic.
The entity and reputation action applies to branded prompts. If the answers in this cell are negatively framed or factually inaccurate, this line tells you whether to address the framing through reviews and PR, correct errors through schema and knowledge-panel work, or defend against an alternatives or comparison framing with differentiation content.
Reading the score as a relative rank, not an absolute number
The opportunity score is a comparative ranking, not a standalone threshold. A score of 0.4 does not mean "forty percent opportunity"; it means this prompt ranks above every prompt with a lower score and below every prompt with a higher score in your set. The value of the score is in the ordering it produces, and in how that ordering changes when you re-scan after taking action.
When a cell moves from absent to mentioned after a content action, its gap shrinks and its score falls. That fall is the signal the action worked. The score reacts to changes in your ladder position because gap is computed fresh on every scan.
Questions
Why does a cell with a small gap still score highly?
A small gap means you are close to the top of the ladder in that cell. But if value and winnability are both high, the score can still be meaningful. The score reflects that there is a real commercial return for the incremental improvement available. Finishing off a cell where you are already strong but not yet consistently recommended first can be as valuable as starting from absent in a lower-value cell.
Why would a high-gap cell score lower than a lower-gap cell?
Gap is one of three factors. If value is low (low demand, weak intent, peripheral persona) or winnability is low (heavily entrenched third-party sources, no existing foothold), the score will be low even with a large gap. A gap that is theoretically large but practically not addressable does not deserve to be at the top of the action queue.
What determines winnability?
Winnability is estimated from the source set for each prompt: how many distinct third-party domains are cited in the answers, whether your own domain already appears among them, and optionally the authority level of the domains in the set. Fewer entrenched third-party sources and your own existing foothold both push winnability up. Many high-authority third-party sources with no foothold for your own domain push it down.
Does the opportunity score account for competitors?
Competitors are surfaced in the diagnosis line attached to each opportunity (which rivals are present in the answers), and competitor presence is what triggers the defend queue classification. The raw score itself does not include a direct competitor term, but the gap input reflects that competitor presence tends to push your ladder mean down, which widens the gap and raises the score.
How does sentiment affect the score?
Sentiment and accuracy deficits widen the effective gap for a prompt. If you are present in answers but framed negatively, your ladder position is technically non-zero, but the opportunity score treats the situation as a larger gap than the raw ladder mean would suggest. This reflects the practical reality that presence with bad framing is worth less than presence with favourable framing, and the gap to close includes fixing the framing, not just the appearance.
Can I see the inputs behind a score?
Yes. The opportunity detail view shows value, gap, winnability, and score for each prompt, along with the diagnosis and the three action lines. You can trace each input back to its source: the demand and funnel calculations behind value, the ladder mean that defines the gap, and the citation domains that feed winnability.
Where can I read about the underlying ladder that gap is computed from?
See The visibility ladder for how mention states map to ladder positions, and Demand and value scoring for how the value input is computed.