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Every recommendation on your Cited dashboard carries an Impact Score — a 0–100 number telling you which recommendations are worth acting on first. Higher means higher priority for your brand, given your current data.

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Why scores exist

Each refresh cycle produces several recommendations — content gaps, competitor shifts, technical fixes, narrative angles. Without prioritization, it’s hard to tell whether a recommendation affects one low-volume prompt or spans dozens of high-intent ones where competitors dominate. Impact Score answers which one should I work on first?

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What the score weighs

Impact Score blends four signals into a single priority number:
  • How much of your prompt library the gap touches. A gap that affects 30 prompts is more urgent than one that affects 3.
  • How big the competitive gap is. The score treats offensive gaps (you trail a competitor) and defensive gaps (you lead, but a competitor is closing in) differently — both can be urgent, for different reasons.
  • How actionable the fix is. A technical fix you can deploy this week scores higher than a multi-quarter authority-building campaign.
  • How valuable the prompts are. Recommendations affecting commercial and transactional prompts score higher than purely informational ones.
The score is brand-relative: a 70 means “high priority on your brand’s scale”, not “high priority compared to every other Cited customer.” This matters because a brand with little dashboard history would otherwise see every recommendation clustered at the bottom of a universal scale, even when the recommendation is genuinely the most important thing to do.

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Strategic alignment

If a recommendation’s topic matches the positioning you’ve declared in Settings → Brand Intent (the target attributes you want your brand to be known for), Impact Score lifts that recommendation’s priority — so the gaps that matter most to your strategic direction surface first. See Configure positioning for how to set this up. A small safeguard: if a recommendation is supported by only a handful of prompts, the strategic lift is capped — so a niche topic doesn’t end up at HIGH priority on thin evidence alone.

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The three tiers

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How Impact Score relates to estimated lift

Some recommendations also show an estimated lift — a directional projection of how many percentage points of mention rate (or related visibility metric) you could expect to recover if the fix lands. The two numbers do different jobs:
  • Impact Score ranks recommendations against each other on your brand. It’s a priority tool.
  • Estimated lift projects the size of the prize. It’s a sizing tool.
A HIGH-priority recommendation with a modest estimated lift is still worth doing — it means the action is the most urgent on your brand even if the absolute lift is moderate.

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What Impact Score does NOT do

  • It does not predict the exact lift. That’s what the estimated-lift field is for, and even that is directional.
  • It does not account for your team’s capacity. A recommendation can be HIGH-priority on the data and still not the right thing to do this sprint based on bandwidth or roadmap context. The score is a starting point, not a project plan.
  • It is not comparable across brands. Brand A’s 60 is not the same as Brand B’s 60 — each is relative to that brand’s own ceiling.

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When the score changes

Impact Scores are recomputed every time your AI Narrative refreshes — weekly across all plans. Scores shift when the underlying inputs shift — new pipeline data changes how many prompts the gap covers, competitor visibility moves, or you add prompts to the library that change the supporting evidence. A score swing of 5–10 points between cycles is normal.

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Frequently asked questions

Impact Scores recompute on each refresh cycle. Scores move when underlying inputs move — new pipeline data, competitor shifts, or prompt-library updates that change which prompts support the gap. A 5–10 point swing is normal between cycles.
Two common causes. First: your brand may be dominant in the category, with comfortable margins over competitors — the score correctly reflects low competitive urgency, which is not a bug. Second: very few pipeline runs so far. A new brand sees more recommendations cluster at MEDIUM/LOW until enough data accumulates. If neither applies, contact support for a diagnostic review.
Not currently. The score is applied uniformly. Use it as a starting point and apply your own business context — it doesn’t know your team’s sprint capacity or strategic timing.
Impact Score ranks recommendations on priority (which to do first). Estimated lift sizes the potential return (how much mention rate could improve). One is sequencing, the other is sizing. They’re complementary, not the same.