Competitor Gap Analysis–Simplified to be Efficient and Effective

Key takeaways

• Competitor gap analysis requires systematically mapping entities, intent stages, and answer patterns scored against business value—not just listing topics competitors rank for that you don't cover.
• AI systems like ChatGPT, Grok, and Claude cite content with complete entity coverage and clear structures. Traditional Google rankings don't guarantee AI citations, which increasingly influence prospect awareness.
• Manual gap analysis takes days or weeks per topic cluster, causing teams to abandon systematic approaches. Automation compresses this work into minutes while maintaining methodological rigor.

By Jayne Schultheis — Most teams think competitor gap analysis means "check what topics they rank for that we don't." That's not gap analysis. That's a topic list!

Real gap analysis requires mapping entities, intent stages, answer patterns, and scoring all of it against business value. It's rigorous work that produces an impact on your pipeline when done right. The problem? Doing it manually burns out your team after analyzing just a few competitors.

Here's what proper gap analysis actually involves, why the manual approach falls apart at scale, and what you need instead. Let's dive in.

What real gap analysis looks like

A competitor content gap is a specific place where your ideal customers are asking questions, your competitors are being cited as authoritative sources, and you're nowhere to be found.

The game has changed. It's not necessarily about ranking in Google's top 10 anymore. AI systems like ChatGPT, Perplexity, and Claude are synthesizing answers from multiple sources and citing the ones with the most complete, entity-rich coverage. If your content is thin on entities or missing key relationships, you won't get cited, even if you technically "rank."

There are four types of gaps worth tracking:

• Topic gaps. These happen when competitors are cited for questions your ideal customer profile actively asks, but you don't have content that addresses them. Think "how to reduce customer churn in SaaS" when you sell a retention platform but only have product pages.
• Intent gaps. These occur at specific funnel stages. Maybe you have strong product comparison pages but weak "solution" content that helps buyers understand their options before they're ready to evaluate vendors. Competitors own the problem and solution stages while you only show up at product evaluation.
• Entity gaps. These are the specific people, products, standards, frameworks, or places that competitors consistently cover but you ignore. In an AEO world, entity completeness determines whether AI systems can extract and cite your content. If, for example, you mention "customer lifetime value" but don't explain cohort analysis, net retention, or expansion revenue, you're not a complete source.
• Format gaps. These occur when your competitors are winning with specific content types that match how AI systems retrieve information. They structure content with clear claim-evidence pairs, define entities explicitly, and use formats that are easy to parse and cite.

Why does this matter for your pipeline? Because gaps that align with high-intent queries and common sales objections create a bridge from discovery to revenue. When a prospect asks an AI system "pricing models for [your category]" and gets an answer synthesized from your competitor's content instead of yours, you've lost the chance to frame that conversation early.

Now let's look at what it takes to find these gaps systematically and where the manual process breaks down.

The data you need before you start

You can't find gaps without baseline inputs. Here's what proper gap analysis requires:

• A topic and entity model for your market. This includes the questions your ICP (Ideal Customer Profile) asks, the entities (people, products, concepts, standards) that matter in your space, and how well competitors cover each one. You need to know what "complete coverage" looks like before you can spot where you're thin. In AEO, this means mapping entity relationships—not just listing keywords.
• Competitor content analysis showing entity density and claim structure. Which entities do they define? What relationships do they establish? How do they structure evidence to support claims? This tells you whether their content is optimized for AI retrieval and citation.
• Google Search Console data for your domain. Specifically: queries where you're getting impressions, pages that rank, click-through rates by position, and current rankings. This shows where you have weak coverage versus no coverage at all. But remember: GSC is a lagging indicator. Just because you rank doesn't mean AI systems will cite you.
• AI citation tracking for your target queries. When prospects ask ChatGPT, Perplexity, or Claude about topics in your space, which sources get cited? If it's always your competitors, you have a gap (even if you rank well in traditional search).

Getting this data manually means exporting GSC reports, running dozens of queries through multiple AI systems, analyzing competitor content for entity coverage, and cross-referencing what gets cited. For a single topic cluster, you're looking at several hours of data collection before you've identified a single gap.

Why this doesn't scale: If you're analyzing 5 competitors across 10 topic clusters, you could be spending weeks just gathering baseline data.

Finding gaps: the manual workflow of the past

Here's what the gap analysis discovery process looks like when done thoroughly:

Start with seed topics

Write down 5-10 core problems your product solves as plain-language questions. What are buyers trying to accomplish when they search or ask an AI system?

Expand the list

Check "People also ask" boxes in Google for your seed topics. Look at related searches at the bottom of SERPs. Query AI systems with your seed topics and see what follow-up questions they generate. Pull top-ranking URLs from competitors and reverse-engineer the topics and entities they cover.

This step could take several hours for a single topic cluster. You're manually clicking through Google results, querying multiple AI systems, copying questions into a spreadsheet, visiting competitor pages, and trying to spot entity patterns.

Map each candidate to three dimensions

There are three issues here to consider:

• Which entities does this content require and how are they related (specific frameworks, tools, regulations, methodologies, and the connections between them)?
• What intent stage does it serve (problem awareness, solution exploration, or product evaluation)?
• What answer structure do AI systems expect (definition with examples, comparison with trade-offs, step-by-step process, calculation with worked example)?

Now you're analyzing 25-40 candidate topics, checking each one against your entity model, evaluating what AI systems synthesize, and documenting answer structures. That takes several hours, too.

Mark the gaps

A gap exists when:

• You have no content addressing the query at all.
• You have content but it's thin on entities, missing key relationships, or structured in ways that AI systems can't easily parse and cite.
• Competitors are consistently cited by AI systems for this topic and you're not.

Why this doesn't scale: It could take someone a few days to analyze one topic cluster. If you need to cover 10 clusters to match your competitors' footprint, that's a lot of manual work.

How do I decide which gaps to fill?

Not all gaps are created equal. You need a scoring system that balances opportunity against effort. Here's what rigorous scoring looks like:

• Ideal customer profile fit (1-5): How tightly does this query map to your ideal customer profile? A 5 means "only our ICP would ask this." A 1 means "tangentially related at best."
• Intent stage (1-5): Where does this sit in the funnel? Weight the later stages higher because they're closer to revenue. Problem awareness scores 2-3. Solution exploration scores 3-4. Product evaluation scores 4-5.
• Leverage (1-5): Can you bring a unique point of view, proprietary data, or product advantage that competitors can't match? High leverage means you can become the authoritative source even if competitors currently dominate.
• Demand signal (1-5): Combine search volume with AI citation frequency. High volume queries where competitors are consistently cited score higher than low volume queries with sparse AI coverage.
• Entity completeness gap (1-5): How much more entity coverage do competitors have than you do? If they define 8 entities and establish 12 relationships while you cover 2 entities with no relationship mapping, that's a high-value gap.
• Effort (1-5): Estimated hours to research entities, structure claims and evidence, draft, review with SMEs, and produce assets. Lower effort scores higher.

Now you need to calculate:

Business Value = (Ideal customer profile fit × Intent stage × Leverage) + Demand signal + Entity completeness gap
Priority Score = Business Value - Effort

Quick Example

Let's say you're evaluating "how to calculate customer lifetime value for SaaS":

• ICP fit: 5 (only SaaS companies ask this)
• Intent stage: 4 (solution exploration)
• Leverage: 4 (you have a built-in calculator and proprietary benchmark data)
• Demand signal: 4 (decent volume, AI systems cite this frequently)
• Entity completeness gap: 5 (competitors cover cohort analysis, net retention, expansion revenue, churn by segment—you don't)
• Effort: 3 (needs SME input, custom asset)

Business Value = (5 × 4 × 4) + 4 + 5 = 89
Priority Score = 89 - 3 = 86

Why this doesn't scale: Scoring 40 gaps manually takes several hours and introduces human error at every step. If you're working across multiple topic clusters, you need scoring consistency across hundreds of gaps. Manual scoring could become a bottleneck.

This is why many teams abandon systematic gap analysis after the first attempt. They run one manual sprint, burn out, and go back to topic selection based on vibes.

What actually works: Automated gap analysis

The workflow described above is a solid method for proper gap analysis. It's also very time consuming. And after that comes the time and effort of writing briefs and producing the actual content.

The problem isn't necessarily the methodology. It's that you can't do this manually at scale. You need automation that:

• Builds and maintains your topic and entity model automatically. It should map your market's questions, identify relevant entities and their relationships, and track competitor entity coverage without manual data collection.
• Discovers gaps in minutes, not days. It should analyze competitor content for entity density and relationships, track what AI systems cite, evaluate your existing coverage, then show the gaps ranked by business value.
• Generates briefs and outlines that writers can execute immediately. Automated briefs can include pre-populated entities and answer patterns pulled directly from your topic model.
• Monitors both traditional search performance and AI citation patterns. You need to see which gaps are driving impressions and clicks in GSC, but also which pieces are being cited by AI systems when prospects ask questions in your space.

This is why we built Rex. It simplifies the entire workflow (discovery, scoring, briefing, and monitoring) in minutes instead of weeks. You get the rigor of systematic gap analysis optimized for how AI systems retrieve and cite content, without the manual labor that makes it unsustainable.

Rex's "Smart Cards" distill all this data into easy-to-read charts and flows, and will even automatically suggest next steps for content pipeline.

If you want to see what your market's gaps look like, give Rex a spin. We'll show you exactly where your competitors are being cited by AI systems and you're missing.

FAQ

What's the difference between topic gaps and entity gaps?

Topic gaps occur when competitors have content addressing questions your ideal customers ask, but you don't have any content covering those topics at all. For example, if you sell a customer retention platform but only have product pages without educational content on "how to reduce customer churn in SaaS," that's a topic gap.

Entity gaps are more nuanced—they exist when you have content on a topic but you're missing the specific concepts, frameworks, methodologies, or relationships that make content authoritative. If you write about customer lifetime value but don't explain related entities like cohort analysis, net retention, or expansion revenue, AI systems won't consider your content complete enough to cite.

Topic gaps mean you're absent from conversations entirely, while entity gaps mean you're present but not authoritative enough to be cited by AI systems synthesizing answers.

Do AI citation patterns matter more than traditional Google rankings?

They both matter. Google rankings show where you appear in search results, but and systems like ChatGPT, Grok, and Claude synthesize answers from multiple sources and only cite content that provides complete, entity-rich coverage with clear claim-evidence structures.

You can rank on page one of Google but still get zero citations from AI systems if your content is thin on entities or doesn't establish relationships between concepts. Since prospects increasingly ask AI systems questions rather than clicking through Google results, being cited by AI helps to determine whether you're part of the conversation when buyers are forming opinions and evaluating options.

AI citation tracking reveals which competitors are shaping prospect understanding at critical research stages, showing you where you're losing the opportunity to frame conversations early in the buying journey before prospects even know your name.

Can I do effective gap analysis with just Google Search Console data?

Google Search Console is necessary but insufficient for modern gap analysis. GSC shows you where you're getting impressions, which pages rank, and your click-through rates—this reveals weak coverage versus no coverage.

However, GSC is a lagging indicator that only tracks traditional search performance, not whether AI systems cite your content when prospects ask questions. You also need competitor content analysis showing entity density and claim structure, AI citation tracking across platforms like ChatGPT and Clause, and a topic and entity model mapping the questions your ICP asks plus the concepts and relationships that define complete coverage in your space.

Without these additional inputs, you're only seeing part of the picture. You might think you have adequate coverage based on rankings while competitors are being cited as authoritative sources by the AI systems your prospects actually use during research.