Studying methods to determine content material gaps for AI visibility begins with a query that conventional content material hole evaluation doesn’t all the time reply: the place might a model contribute one thing helpful to an AI-generated reply, however isn’t being talked about or cited?
With AI and web optimization tendencies altering how individuals uncover manufacturers, discovering a content material hole isn’t all the time so simple as figuring out a lacking article. An organization would possibly have already got a related web page that AI serps aren’t citing, or an AI assistant would possibly point out the model whereas counting on one other web site for supporting info. Creating extra content material with out understanding what’s lacking might not resolve the issue.
This information walks by means of methods to use prompt-tracking information and current content material to determine AI visibility gaps, evaluate competing sources, and determine what to do subsequent.
Desk of Contents
What’s an AI visibility content material hole, and the way is it totally different from web optimization gaps?
An AI visibility content material hole is a related info want, represented by a immediate or group of prompts, for which a model is absent from AI-generated solutions, lacks supporting citations, or doesn’t have content material that adequately addresses the query.
AI visibility analysis examines a different set of outcomes. A page can rank well in Google and still be absent from a ChatGPT, Gemini, or Perplexity response. And, to add to the complexity, an AI assistant may mention a brand without directing readers to the brand’s website at all. That difference makes it important to understand three types of AI visibility gaps:
For marketers, treat these categories as diagnostic rather than mutually exclusive. A page may cover the right topic but lack both a direct answer and sufficient evidence to support a citation. The objective is to identify what is missing from the answer ecosystem, not simply what is missing from a content calendar.
AI Mentions vs. AI Citations
An AI mention occurs when an answer names a brand, product, or company. An AI citation occurs when the answer provides a source reference or link supporting information in the response. For marketers, understanding these differences in outcomes makes it easier to identify content opportunities.
For example, an AI assistant answering “What are the best project management tools for agencies?” might recommend a particular platform but cite an independent review site for its pricing, integrations, or suitability. The brand has achieved mention visibility, but its own content is not necessarily being used as the supporting source.
A citation to the brand’s website provides a different signal. A citation to the brand’s website means the assistant has surfaced that website as a source for the answer. However, a citation alone does not establish that the assistant recommended the brand, that a reader clicked the link, or that the answer generated revenue.
Pro tip: Record mentions and citations separately. A brand can be mentioned in an AI answer without citing a single piece of its own content as a source. That is a completely different situation with a completely different fix, and combining them into a single visibility metric can conceal whether the opportunity involves brand inclusion, source selection, or both.
How to Identify Content Gaps for AI Visibility Step-by-Step
An AI visibility content gap analysis follows four steps: collect relevant prompt data, classify mentions and citations, map findings to existing content, and prioritize gaps by business relevance and the work required to address them. The content gap analysis process begins by reviewing the answers AI platforms currently produce, rather than assuming which articles a company should publish. Here’s what to do.
1. Pull prompt tracking data to see where the brand is missing.
Prompt tracking establishes where a brand appears in AI-generated answers and where relevant questions produce no brand mention or citation.
Start with a defined set of prompts representing the questions prospective customers ask during discovery, evaluation, and purchase. Group prompts by topic and intent so that individual wording variations do not become separate content projects.
I use HubSpot AEO to determine teams of prompts associated to the providers I provide by means of Cassie Clark Advertising. HubSpot’s AEO instrument tracks prompts throughout ChatGPT, Gemini, and Perplexity, exhibiting how a model seems in solutions alongside opponents and citations. The advertising and marketing crew can then use these observations to determine which prompts or matter areas want a better look.
Utilizing Cassie Clark Advertising for instance, HubSpot AEO instructed a number of prompts associated to answer-engine optimization consulting:
- “Which answer-engine optimization consulting providers ought to enterprise manufacturers evaluate?”
- “How ought to a CMO select answer-engine optimization consulting providers?”
- “Which answer-engine optimization consulting providers assist giant content material operations groups?”
These prompts replicate a associated want (discovering an answer-engine optimization accomplice), however they method that want from totally different angles. One asks for providers to check, one other focuses on choice standards, and the third introduces a selected operational requirement.
Grouping associated prompts helps determine the broader matter a model must cowl with out treating each variation as a separate content material alternative. The following step is to look at the AI-generated solutions and decide whether or not current content material addresses these wants, and the place protection could also be lacking.
2. Monitor model mentions and citations in a spreadsheet.
The following step is to file what occurs when an AI assistant solutions every immediate. Run the prompts by means of an AI reply engine and word which manufacturers seem within the response and which web sites are cited as sources.
I like to make use of a spreadsheet to maintain monitor of those observations. For instance, after I examined the immediate “Which AI search visibility audit service fits a scaling B2B SaaS startup?” ChatGPT really useful Profound, Otterly, Scrunch, and Peec AI. Cassie Clark Advertising was absent from the reply, and the cited sources centered on AI visibility software program.
I’d file that commentary in my spreadsheet, together with the immediate, platform, date, manufacturers talked about, and cited sources. From there, I can use the next outcomes to determine what wants a better look:
The purpose of this step is a shortlist of immediate teams with a clearly documented visibility sample. The classification system prevents groups from treating each lacking quotation as a purpose to write down one other article.
As an illustration, if a model is repeatedly talked about however a third-party listing provides its function info, the rapid activity could also be to evaluate the model’s product documentation. A brand new thought-leadership article would possibly do little to deal with that exact hole.
3. Map prompts to current content material and confirm true gaps.
Immediate-to-page mapping determines whether or not the corporate already has content material that solutions the questions surfaced by immediate monitoring. For every precedence immediate group, search the corporate’s web site and content material stock for pages that handle the underlying info want. Embody product pages, documentation, comparability pages, case research, analysis, and related weblog posts. Assess every candidate web page towards 4 questions.
- Protection: Does the web page reply the precise query, together with its vital subquestions?
- Readability: Can a reader rapidly determine the reply and the details supporting it?
- Proof: Does the web page present verifiable particulars, examples, information, or firsthand expertise the place the subject requires them?
- Alignment: Does the web page handle the identical viewers, use case, and determination standards because the immediate?
Think about the immediate, “Which AI search visibility audit service fits a scaling B2B SaaS startup?”
After I examined this immediate, ChatGPT really useful a number of AI visibility software program platforms, together with Profound, Otterly, Scrunch, and Peec AI. Cassie Clark Advertising didn’t seem within the reply, despite the fact that I provide AI search visibility audits for startups and enterprise manufacturers.
The response revealed a possible content material alternative past merely getting my model talked about. ChatGPT handled the query primarily as a request for software program slightly than a consultant-led service. The cited sources had been additionally comparisons of AI visibility instruments.
My current audit providers web page clearly explains who the service is for and what it contains. Nonetheless, the reply I captured centered on software program slightly than consultant-led audits, despite the fact that the immediate particularly requested about audit providers.
With that in thoughts, I’d take into account creating content material that explains the distinction between an AI visibility monitoring platform and a consultant-led audit, together with what every offers and when a scaling B2B SaaS firm would possibly want one, the opposite, or each.
The purpose is to deal with an info want that my current providers web page doesn’t totally cowl, slightly than creating one other web page just because my model was lacking from the reply.
Professional tip: Earlier than assigning a content material undertaking, affirm that the corporate truly presents the services or products the immediate asks about. A lacking point out might level to a content material hole, nevertheless it might additionally reveal a functionality hole, a positioning situation, or a query that isn’t related to the enterprise.
Document probably the most related current URL for every immediate group, if one exists. If no web page adequately addresses the query, take into account whether or not new content material might fill that hole earlier than including one other article to the calendar.
4. Prioritize which gaps to shut first.
Content material gaps must be prioritized in line with enterprise relevance, the energy of the noticed visibility sample, the character of the lacking info, and the trouble required to deal with it. To determine which content material gaps to sort out first, take a look at two issues:
- Enterprise impression. How intently does the immediate align with the corporate’s audience, precise capabilities, and vital buyer selections? Is the subject related to a product, service, or downside the corporate needs to be recognized for?
- Effort. Can an current web page be improved with a centered replace, or does the hole require new analysis, product documentation, skilled interviews, technical adjustments, or a brand new web page?
This creates a call matrix:
For Cassie Clark Advertising, the immediate about AI search visibility audit providers for scaling B2B SaaS startups is intently aligned with my audience and the providers I provide. The reply additionally suggests an info hole value exploring: it recommends monitoring software program with out clearly addressing consultant-led audits.
Making a useful resource that explains the distinction would take extra work than updating a paragraph on my providers web page, however the matter is straight related to potential purchasers evaluating their choices. I’d prioritize it as a possible new content material undertaking.
Professional tip: Remember to additionally take a look at the third-party sources ChatGPT cited. Since these sources concentrate on software program comparisons, there could also be a possibility to contribute a advisor’s perspective to related trade publications. That may be a separate effort from creating content material for a model web site, however each initiatives might assist handle the data hole recognized within the reply.
Easy methods to Analyze AI and Search Outcomes to Perceive What to Repair
- Review the AI-generated answer.
- Open the cited sources.
- Compare those findings with conventional search results for the underlying topic.
Don’t copy a competing article. The objective here is to understand what the answer requires and whether the company’s existing content satisfies that need. Look for recurring patterns:
- Do cited pages define a term more directly?
- Do they provide product-specific documentation, current data, worked examples, or independent evidence?
- Are they addressing a different audience or use case than the company’s page?
Then compare those findings with the page mapped to that prompt.
For example, with Cassie Clark Marketing, if an AI answer explains how an enterprise content team can build an AI visibility strategy, but my services page only says I offer AI search consulting, I may need to explain what that work looks like for a large team. That could mean adding details about the discovery process, how I work with internal stakeholders, and what the team can expect from the engagement.
Make your pages easy for AI to extract.
Extractability describes how clearly a page presents information that can be identified, understood, and reused in an answer. Improving extractability requires making the useful information accessible without forcing readers — or retrieval systems — to reconstruct it from vague or scattered statements. Here are a few ways to improve an existing page.
- Answer the main question directly. State what a product, process, or concept does before expanding into benefits and context.
- Use descriptive headings. Organize sections around distinct questions, capabilities, or steps rather than generic labels.
- Keep related information together. Place a claim near the explanation, qualification, or evidence needed to understand it.
- Use appropriate formats. Tables can clarify comparisons, while numbered steps can explain workflows. Prose remains useful for nuance and interpretation.
- Define unfamiliar terms. Avoid relying on internal product language when the intended audience uses broader industry terminology.
- Make important content accessible. Review whether essential information is available in the page content rather than only in images, inaccessible interfaces, or elements that may not be available to every crawler.
- Check factual consistency. Align product descriptions, specifications, and terminology across relevant pages.
The goal is a page that communicates clearly to its intended reader. Better structure supports that goal, but structure alone doesn’t establish that an AI platform will retrieve or cite the page.
Add originality signals that LLMs value.
Original information gives a page something substantive to contribute beyond a summary of material available elsewhere. Depending on the topic, that contribution may come from:
- Firsthand experience
- Original research
- Expert analysis
- A documented customer example
- Detailed product information that only the company can provide
Originality should help answer the reader’s question. A proprietary statistic with no explanation of how it was collected may be less useful than a real example that shows exactly how a user can apply the information.
For research-led content, include the source, date, sample size, and methodology. For client examples, confirm permission and remove confidential information. When incorporating expert commentary from an interview, preserve the guest’s intended meaning rather than stretching a quote to support a broader claim.
While adding original information can’t guarantee an AI citation, it makes the content more useful and gives readers a clearer basis for evaluating its claims.
How to Create or Update Content to Close AI Visibility Gaps
Once a gap has been identified, the next step is to determine whether an existing page can address it or new content is needed. A missing brand mention alone isn’t enough to make that decision.
Before creating a new page, check whether the topic belongs within an existing resource. Several closely related articles may fragment a useful explanation across URLs without addressing the underlying question more effectively.
- For a net-new page: Build the content brief around the prompt group and the information missing from current answers. Specify the intended audience, the question the page must answer, the evidence required, and how the page differs from existing resources.
- For an update: Preserve material that already serves the reader. A focused improvement may be more appropriate than rewriting an entire article around an AI-generated answer.
After publishing, keep a record of what changed, including the URL, the gap the update addressed, the publication date, and the prompt group being tracked. This makes it easier to measure the results over time.
Pro tip: Recommendations in HubSpot’s AEO tool might assist groups determine potential actions, comparable to creating new content material, updating current pages, or fixing technical gaps. Take a second to evaluate these suggestions towards the present content material stock and the underlying immediate proof earlier than assigning work to the content material crew.
Easy methods to Measure Outcomes and Iterate
Monitor how typically the model is talked about, how typically its content material is cited, and the way nicely AI-generated solutions handle the prompts being monitored to measure whether or not content material updates are making a distinction.
Remember that AI-generated solutions can fluctuate from one run to the following. Competitor exercise and platform adjustments may also have an effect on visibility, so a rise in mentions or citations doesn’t essentially imply a content material replace brought on it.
Begin with a baseline measurement from earlier than the replace, then monitor the identical immediate teams throughout the identical platforms utilizing constant measurement strategies. This makes it simpler to identify adjustments over time with out drawing conclusions from a single response. Listed below are a couple of AEO metrics to trace.
Model Point out Presence
Model point out presence measures how typically a model seems in solutions to the prompts being tracked. The calculation is easy: Point out presence = Solutions mentioning the model ÷ Complete tracked solutions × 100
For correct comparisons, use the identical methodology every time. For instance, determine whether or not every commentary represents a single immediate run or a singular immediate, and follow that method.
If point out presence will increase after a content material replace, take a look at the precise solutions to see how the model is being mentioned. A passing point out or an inaccurate description is probably not the consequence the crew hoped for.
For prompts the place the model continues to be absent, evaluate the related pages to see whether or not they clearly clarify how the corporate pertains to the subject or use case.
Quotation Presence
Quotation presence measures how typically a model’s personal content material is cited within the solutions being tracked. The calculation is: Quotation presence = Solutions citing at the very least one brand-owned URL ÷ Complete tracked solutions × 100
Quotation presence measures the share of solutions that cite the model, not the full variety of citations. If one reply cites three pages from the identical web site, it nonetheless counts as one reply with a model quotation.
When quotation presence adjustments, take a look at which pages are being cited and the way the AI assistant is utilizing them. Extra citations might imply the model’s content material is showing as a supply extra typically, however the context of these citations issues, too.
For prompts the place the model is talked about however its content material isn’t cited, evaluate the model’s pages with the sources the assistant makes use of. Search for info which may be lacking, explanations that could possibly be clearer, or supporting proof that could possibly be added.
AI Share of Voice
AI share of voice measures how typically a model seems in contrast with different manufacturers throughout the prompts being tracked. There are two methods to calculate it, relying on what’s being measured.
- Entity-based share of voice: The model’s appearances ÷ Complete appearances of all tracked manufacturers × 100
- Quotation-based share of voice: The model’s citations ÷ Complete citations throughout the outlined supply set × 100
Entity-based share of voice and citation-based share of voice measure various things, so it’s vital to decide on one and use it constantly.
When share of voice adjustments, look past the general proportion. Verify which manufacturers are showing, how they’re being mentioned, and which immediate teams are driving the change. A rise in a single matter doesn’t essentially imply the model is gaining visibility throughout each matter being tracked.
AI-Referred Visitors and Downstream Outcomes
AI-referred visitors will help present whether or not AI search visibility is translating into web site visits. When referral information is out there, take a look at which pages guests land on, how they interact with the positioning, and whether or not they take actions comparable to submitting a kind or changing into a buyer.
HubSpot’s traffic analytics tool classifies visits from AI assistants as AI Referrals, and speak to data can carry that supply, so groups can join AI-referred visits to the contacts they create.
However that information solely captures a part of the image. Some AI-driven visits is probably not recognized as referrals, and somebody who discovers a model by means of an AI reply would possibly return later by means of one other channel. Referral and CRM information are finest used alongside point out and quotation monitoring to know how AI visibility pertains to enterprise outcomes, slightly than to attribute a sale to a selected content material replace.
Professional tip: Hold a log of content material updates, main product adjustments, and adjustments to the monitoring course of. Overview tendencies throughout a number of observations as a substitute of assuming that the newest content material edit brought on a change in AI visibility.
Widespread Pitfalls to Keep away from When You Determine Content material Gaps for AI Visibility
The commonest errors in AI visibility content material hole evaluation come from treating observations as directions. A lacking point out doesn’t robotically justify a brand new web page, and a cited competitor doesn’t robotically reveal what the model ought to publish.
As an AI search visibility advisor, I’ve seen how straightforward it’s for manufacturers to concentrate on the obvious gaps slightly than those that truly want consideration. Listed below are a couple of errors to keep away from when deciding what content material to create or replace.
1. Don’t chase each immediate.
A single immediate can produce totally different solutions from one run to the following. If each lacking point out triggers a brand new article or content material replace, the editorial calendar can rapidly change into a response to regular AI reply variation.
As a substitute, group associated prompts and search for patterns over time. Earlier than assigning work, take into account how related the underlying query is to the enterprise. If the outcomes are inconsistent, hold monitoring the immediate till there’s sufficient info to decide.
2. Give attention to high-impact subjects and audiences.
Not each visibility hole deserves the identical consideration. A model may be lacking from solutions to a broad, standard query however seem repeatedly in solutions to extra particular questions its potential prospects are asking.
Give attention to prompts that relate to the corporate’s providers, audience, and shopping for selections. Overview the wording of every immediate and the solutions it produces to ensure the content material alternative is related to the individuals the enterprise needs to achieve.
3. Write for individuals first and keep away from over-optimizing construction.
Making content material straightforward for AI assistants to extract shouldn’t come on the expense of creating it helpful for readers. A web page stuffed with brief definitions, repetitive headings, and fast solutions may be straightforward to scan, however it will probably omit the examples and context individuals want to know the subject.
Use headings and formatting to arrange the data, not change it. Hold the reasons, firsthand expertise, and particulars that make the content material value studying within the first place.
Tools and Templates to Speed Up Content Gap Analysis
Content gap analysis gets easier when there’s a consistent way to track prompts, review AI-generated answers, and decide what to do next.
A spreadsheet is a good place to start tracking for content gap analysis. It brings prompt data, existing content, and proposed updates into one place so the team can see which gaps need attention and which can wait. Here’s what to include in the audit and how to turn the findings into a content brief.
AEO Gap Audit Checklist
An AEO gap audit checklist helps organize the findings from each prompt group and connect them to a potential content update. Set up two tabs in the spreadsheet: one to record individual prompt responses and another to summarize the gaps identified across related prompts. Use one row per prompt group in the summary tab, and keep individual observations in the first tab so changes between runs don’t get lost. Set up the AEO audit gap checklist like this:
Content Brief Template
Once a gap has been prioritized, the next step is to turn the findings into a content brief. The brief should give the writer enough context to understand the customer’s question, what the existing content is missing, and what the finished page needs to accomplish. Open a new word processing document and use this template:
The most important part of the brief is explaining why the content needs to be created or updated. A list of prompts might tell a writer what people are asking, but it doesn’t explain what’s missing from the existing content or how the new piece should address that gap.
AI Search Grader
HubSpot’s free AI Search Grader offers a place to begin for understanding how AI reply engines signify a model. AI Search Grader evaluates areas comparable to model recognition, sentiment, presence high quality, share of voice, and market place.
For instance, if AI Search Grader’s outcomes counsel {that a} model has restricted visibility in AI-generated solutions, the following step could be to look at the precise prompts, sources, and current content material behind that consequence. That preliminary evaluation will help determine areas value investigating earlier than transferring right into a extra detailed content material hole evaluation.
HubSpot AEO
HubSpot’s AEO tool helps entrepreneurs monitor how their manufacturers seem in AI-generated solutions throughout ChatGPT, Gemini, and Perplexity, together with the prompts being monitored, model mentions, citations, and competitor visibility. For content material hole evaluation, this will help determine prompts the place a model is lacking, seems with no quotation, or is described otherwise from competing manufacturers. The following step is to check these findings with the corporate’s current content material:
- Does a related web page exist already?
- Does it totally reply the query?
- Is there a possibility so as to add extra helpful info or supporting proof?
That evaluate helps decide whether or not entrepreneurs have to replace an current web page, create one thing new, or handle a unique situation altogether.
For Advertising Hub Skilled and Enterprise customers, the AEO instrument may also use CRM information to tell immediate ideas. HubSpot’s SEO marketing software can add to this evaluation by surfacing web optimization suggestions, matter alternatives, and natural search efficiency.
Collectively, HubSpot’s instruments will help entrepreneurs perceive how their pages carry out in conventional search and the way their model and content material seem in AI-generated solutions.
Incessantly Requested Questions About Figuring out Content material Gaps for AI Visibility
How typically must you run an AI visibility content material hole evaluation?
Begin with a full content material hole evaluation to determine a baseline. From there, revisit it when priorities shift, the content material technique adjustments, or an vital matter wants one other look. After publishing or updating a web page, proceed monitoring the related prompts to see whether or not any adjustments in visibility maintain up over time. How typically this occurs will depend upon the variety of prompts being tracked, how regularly content material is up to date, and the way a lot time the crew has to behave on the findings.
Which AI platforms must you monitor for immediate protection?
Begin with the platforms your viewers is more than likely to make use of. ChatGPT, Gemini, Perplexity, and Google’s AI Overviews are good places to start, but the right mix depends on where prospective customers go to research options and make decisions.
Review results from each platform separately before combining them. The same prompt can produce different answers and citations across platforms, and their measurement capabilities may differ.
When should you ignore a detected AI visibility gap?
Not every gap needs to become a content project. If a prompt has little to do with the target audience, asks about a service the company doesn’t offer, or only shows a missing mention in one or two responses, it may be worth setting aside.
The same goes for a gap that would take significant time and resources to address but has limited relevance to the business. Keep a record of why the gap was deprioritized so the team can revisit it if priorities or circumstances change.
How do you measure AI visibility when your analytics doesn’t segment AI assistant traffic?
Website analytics aren’t required to track brand mentions, citations, or AI share of voice. These metrics can be measured by monitoring a consistent set of prompts and recording the answers each platform produces. For each observation, capture the prompt, platform, date, answer, and cited sources. This provides a record of how the brand appears in the answers being tracked, even when AI-referred website traffic can’t be identified.
Keep in mind that prompt tracking measures visibility within the selected sample. It doesn’t show how many people saw those answers or visited the website afterward.
What’s the fastest way to get started if you have limited resources?
Start small. Choose a handful of prompts that relate directly to the company’s services and the questions prospective customers are asking. Track where the brand is mentioned or cited, review the sources AI assistants use, and compare those findings with the company’s existing content. Look for one clear gap that can be addressed by updating a relevant page rather than taking on a large content project right away.
Record the baseline before making the update, then revisit the same prompts afterward to see what changes.
Close the gaps that matter, not every gap that appears.
Identifying content gaps for AI visibility starts with connecting what AI assistants say to real customer questions and the information a company can credibly provide. Prompt tracking shows where a brand is absent or isn’t being cited, while reviewing existing pages helps determine whether the issue is missing information, unclear explanations, insufficient evidence, or something content alone can’t fix.
The advice I give each of my clients is to resist the urge to create content every time a brand is missing from an AI answer. Focus on the gaps that matter to the business and where there’s a genuine opportunity to provide a better answer.
HubSpot AEO can support that process by bringing prompt, visibility, competitor, and citation data into the analysis. For teams beginning to build an AEO practice, HubSpot’s Early-Signs Guide to AEO presents a subsequent step, with early methods, page-level finest practices, and survey information on how different entrepreneurs are approaching AEO.
