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The Gap Analysis page surfaces the topics and queries where your brand is absent from AI-generated answers — either because competitors are mentioned instead of you, or because the category is simply underserved in your content. Understanding these gaps is the most direct path to improving your AI visibility score, because it tells you exactly where to focus content creation and citation-building efforts.
You can filter all views on this page by domain using the domain selector at the top. This is useful when you are managing multiple brands or tracking visibility for a specific product line.

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What answer gaps are

An answer gap occurs when an AI model responds to one of your tracked prompts without mentioning your brand. Gaps are classified by severity:

Critical gaps

Your brand is absent from AI responses for high-intent queries where one or more competitors are actively mentioned. These represent the highest-priority content and citation opportunities because a competitor is already winning visibility you could capture.

Moderate gaps

Your brand is absent from AI responses, but no single competitor dominates the topic. These gaps represent emerging opportunities — you can establish your brand as the authoritative answer before competitors consolidate their position.
The gap list is sorted by severity and estimated reach by default. Each gap row shows the prompt text, which LLM providers returned no brand mention, and the competitor (if any) that appeared in the response instead. The Trends tab shows how your visibility for each topic has changed over time, broken down by LLM provider. Use this view to answer questions like:
  • Is a recent content publish improving my visibility on a specific topic?
  • Are there topics where my score is declining across all providers simultaneously?
  • Which providers are most consistent in mentioning my brand for a given query?
Each row in the heatmap represents a prompt. Columns represent time periods. The color intensity of each cell reflects your mention rate for that prompt in that period — darker means higher mention rate. Hovering over a cell shows the exact mention count and the providers that contributed.
If you publish new content targeting a specific gap topic, check back on the Trends tab 7–14 days later. LLM providers update their knowledge at different rates; Perplexity and Google AI Overviews tend to reflect new content faster than GPT-4 or Claude.

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Competitor gaps

The Competitor Gaps tab shows a ranked list of topics where a competitor domain appears in AI responses but your brand does not. Each row includes:
  • Topic / prompt: The query where the gap was detected
  • Competitor: The domain or brand name that was cited instead
  • LLM providers: Which providers showed this competitor
  • Gap score: A normalized score reflecting how consistently the competitor appears and how often your brand is absent
This view helps you prioritize which competitors are most actively displacing your brand in AI answers, and on which topics they have the strongest foothold.
Competitor data is only available when you have added competitor domains under Settings → Domains. Without competitor domains configured, the Competitor Gaps tab shows no data.

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Content opportunities

The Content Opportunities tab derives actionable recommendations from your gap data. Rankahead analyzes the topics and keywords that appear in AI responses for queries where your brand is absent, then groups them into opportunity categories:
Topics where AI models reference subject matter that your site does not adequately cover. The recommendation is to publish new content — a blog post, landing page, or resource — that addresses the topic comprehensively.See Content → Blog Posts to generate AI-optimized drafts based on these opportunities.
Pages that cover the right content but lack structured data markup. AI models that retrieve from the live web index — particularly Google AI Overviews — favor pages with schema markup because it makes content easier to parse. The recommendation is to add relevant Organization, FAQPage, HowTo, or Article schema to existing pages.
Topics where your content exists but is not being cited by AI models. This usually means your domain lacks sufficient third-party validation on this topic. The recommendation is to pursue backlinks and mentions from high-authority sites covering the same subject, which increases the likelihood of AI models including your domain as a trusted source.
Topics where AI models are surfacing community content — Reddit threads, Stack Overflow answers, Quora responses — rather than authoritative articles. Being active and cited in relevant forums can significantly improve your visibility for conversational queries, especially on Perplexity, which retrieves from community sources heavily.

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Using gap analysis to plan content

Gap analysis is most powerful when it directly informs your content calendar. Here is a recommended workflow:
1

Review critical gaps first

Open the gap list sorted by severity. Address critical gaps before moderate ones — these are the queries where a competitor is actively winning AI visibility at your expense.
2

Identify the opportunity type

For each gap, check the Content Opportunities tab to see whether the recommended action is to create new content, add schema markup, build citations, or increase forum presence. This determines which team owns the follow-up action.
3

Generate or update content

For content gap opportunities, navigate to Content → Blog Posts and create a new post targeting the gap topic. Rankahead pre-fills the topic brief based on what AI models are discussing in responses to that prompt.
4

Track improvement over time

After publishing, return to the Trends tab and monitor the mention rate for the corresponding prompt. It can take two to four weeks for content to be indexed and begin influencing AI model responses, particularly for GPT-4 and Claude.
Focus your first sprint on two or three high-priority critical gaps rather than spreading effort across many moderate ones. A concentrated push on a single topic — new content, structured data, and outreach for citations — produces faster measurable results than a broad shallow approach.