Each firm’s rivals are displaying up in AI-generated solutions, however do entrepreneurs know which of them, for which queries, and why? That’s precisely what AEO competitor evaluation is designed to inform groups. 
Reply engines like ChatGPT, Perplexity, and Google’s AI Overviews don’t rank pages. They cite sources. That shift modifications the whole lot about how aggressive visibility works. A model can maintain a top-three natural rating and nonetheless be utterly absent from the AI reply a prospect reads first.
If manufacturers usually are not monitoring who’s incomes these citations and the way, they’re making content material and search engine marketing selections with out half the image. This information walks via the way to run an AEO competitor evaluation from scratch — what to measure, which instruments to make use of, and the way to flip findings into content material that closes the hole.
Desk of Contents
What’s AEO competitor evaluation?
AEO competitor evaluation is the method of figuring out which manufacturers, pages, and sources reply engines cite in AI-generated responses — and benchmarking a model’s personal visibility towards rivals throughout those self same queries.
“AEO” stands for Reply Engine Optimization: the follow of structuring content material in order that AI platforms like ChatGPT, Perplexity, Google’s AI Overviews, and Gemini floor it as a trusted reply.
AEO competitor evaluation extends that follow outward — as a substitute of promoting groups simply optimizing their very own content material, they’re systematically monitoring who else the engines are citing, why, and what gaps they will shut.
I’ve discovered that groups usually confuse AEO with conventional search engine marketing aggressive analysis. The important thing distinction: Conventional search engine marketing competitor evaluation tracks key phrase rankings and backlinks. AEO competitor evaluation tracks quotation frequency, reply share, entity protection, and QA content material depth throughout AI-generated solutions. The items of measurement are completely different as a result of the underlying competitors is completely different — entrepreneurs and search engine marketing leaders usually are not preventing for a rank place, they’re preventing to be the supply an LLM trusts.
HubSpot AEO helps entrepreneurs observe how their model seems throughout reply engines, displaying which prompts cite rivals as a substitute and the place they’re utterly absent, so groups can benchmark visibility towards rivals in a single view.
Why AEO Competitor Evaluation Issues Now
Rising channels could favor front-running adopters.
Reply engine search just isn’t a future pattern, so cease pondering that method. It’s a present channel with accelerating adoption. In line with Search Engine Land, 58.5% of U.S. Google searches and 59.7% of EU searches end in zero clicks. In the meantime, ChatGPT has surpassed 900 million weekly lively customers.
Groups that construct AEO measurement and content material infrastructure now are establishing quotation authority earlier than most rivals have even began monitoring it.
I’ve spoken with search engine marketing leaders who deal with AI visibility as a “wait and see” channel. My expertise has taught me that’s a mistake. Quotation patterns in LLMs are typically sticky — as soon as a mannequin associates a model with authority on a subject, that affiliation persists throughout queries and mannequin updates.
AI solutions compress conventional SERPs.
Google’s AI Overviews push natural blue hyperlinks additional down the web page, usually beneath the fold. For prime-intent queries — “what’s the finest CRM for startups,” “how do I calculate buyer lifetime worth” — the AI reply is the SERP end result for many customers. If a competitor is persistently cited in these solutions and a model just isn’t, that model is successfully invisible for these queries, no matter its rankings.
Visibility shifts to citations, entities, and QA patterns.
Conventional search rewards pages. Reply engines reward entities and solutions. Reply engines consider content material primarily based on:
- Quotation frequency. How usually is a model or URL cited for a given subject set?
- Entity protection. Does the content material clearly set up what the model is, what it does, and who it serves?
- QA depth. Does the content material immediately and utterly reply the questions customers are literally asking?
Competitor evaluation on this atmosphere means understanding not simply what a model’s rivals are publishing, however how their content material is structured and why LLMs favor it.
HubSpot AEO breaks down which domains, content material sorts, and sources reply engines are citing most frequently, giving entrepreneurs clear perception into what content material is presently favored and what they should create or optimize to enhance visibility.
Influence on pipeline affect, help deflection, and model authority.
AEO visibility has a downstream enterprise impression past site visitors. Manufacturers that seem persistently in AI solutions for buying-stage queries — “finest [category] software program,” “how to decide on a [tool],” “[brand A] vs [brand B]” — affect buy selections earlier than a prospect ever visits an internet site.
Groups monitoring AEO competitor knowledge are additionally utilizing it to establish help and product FAQ alternatives, deflecting inbound questions by proudly owning AI-generated solutions to widespread buyer points.
HubSpot AEO and AEO options in Advertising Hub Professional and Enterprise present a prioritized record of suggestions primarily based on visibility and quotation knowledge, serving to groups flip competitor insights into a transparent plan for enhancing their presence in AI-generated solutions.
The way to Run an AEO Competitor Evaluation Step by Step
Step 1: Accumulate precedence questions that reply engines should resolve.
Begin by constructing a question set, which is a consultant record of questions your target market asks that reply engines are more likely to resolve with a generated reply. These ought to span:
- Consciousness-stage questions – “What’s [category]?” / “How does [process] work?”
- Consideration-stage questions – “Finest [tool type] for [use case]” / “[Brand A] vs [Brand B].”
- Determination-stage questions – “How a lot does [product] price?” / “Is [brand] proper for [company type]?”
- Help and FAQ questions – Widespread points clients seek for after buy.
Professional tip: Pull questions from the present key phrase analysis, buyer help tickets, gross sales name transcripts, and “Folks Additionally Ask” packing containers in Google. Entrepreneurs need 30 to 100 queries throughout their core subject clusters to get a statistically significant view of reply share. For HubSpot customers, built-in AEO options in Advertising Hub Professional and Enterprise recommend prompts to trace primarily based on its data of their enterprise and clients.
Step 2: Check queries throughout chatbots and AI Overviews.
Run every question manually or with an AEO instrument throughout a number of reply engines: ChatGPT, Perplexity, Google AI Overviews, and Gemini. Report:
- Which sources are cited (URLs and domains).
- Which manufacturers are talked about by title (even and not using a quotation hyperlink).
- The construction and format of the reply (record, paragraph, desk, step-by-step).
- Whether or not your model seems in any respect.
At scale, that is the place AEO instruments develop into important — guide testing throughout 50+ queries on 4 platforms isn’t sustainable. However I like to recommend beginning with guide testing for a model’s prime 10 to fifteen queries. It builds instinct for why sure content material will get cited that dashboards alone received’t offer you.
With HubSpot AEO, entrepreneurs can routinely observe prompts throughout ChatGPT, Perplexity, and Gemini, seeing which responses cite their model, which cite rivals, and the way visibility modifications over time with out guide testing.
Step 3: Extract cited sources and entities.
For every question in a set, doc each cited supply and named entity. Entrepreneurs are constructing a map of:
- Which competitor domains are most incessantly cited (quotation frequency by area).
- Which particular pages or content material sorts win citations (weblog posts, documentation, touchdown pages, analysis experiences).
- Which entities are persistently talked about (model names, product names, individuals, organizations).
Search for patterns. If a competitor’s weblog persistently will get cited whereas their product pages don’t, that tells entrepreneurs one thing about what content material format LLMs favor. If a direct competitor is showing for his or her core queries, that’s a brand new aggressive menace value monitoring.
Step 4: Map rivals by subject cluster and reply share.
With quotation knowledge collected, entrepreneurs ought to manage it by subject cluster — not simply by competitor. Calculate a tough reply share for every model: the share of queries in a subject cluster the place that model is cited.
This map reveals two issues:
- The place rivals dominate. Subject clusters the place a rival has a excessive reply share, and you’ve got low or none — these are precedence hole areas.
- The place the sphere is open. Subject clusters the place no model dominates — these characterize fast-mover alternatives the place sturdy content material may rapidly set up quotation authority.
Right here’s an instance of an AEO competitor evaluation chart:
Step 5: Diagnose why rivals win.
That is the step most groups skip — and it’s probably the most useful. Don’t simply establish that a competitor wins citations. Diagnose why.
For every competitor web page that persistently earns citations, analyze:
- Content material format. Is it a listicle, a long-form information, a FAQ web page, a comparability article?
- QA construction. Does the web page immediately reply the query within the first 1–2 sentences, then present supporting element?
- Entity readability. Does the web page clearly state what the model/product/subject is, who it’s for, and what downside it solves?
- Freshness. When was the content material final up to date? LLMs usually favor lately up to date content material for fast-moving subjects.
- Schema markup. Does the web page use FAQ, HowTo, or different structured knowledge?
- Backlink authority. Is the web page well-cited by different authoritative sources?
What I like: Probably the most actionable diagnostic query is: “If I had been a language mannequin attempting to reply this query, would this web page give me a transparent, reliable, full reply?” That framing cuts via a lot of the complexity.
AEO in HubSpot Advertising Hub generates prioritized, plain-language suggestions with clear subsequent steps, serving to groups transfer from perception to motion. Groups get useful insights within the interface they already know.
AEO Competitor Evaluation Instruments and Workflows
1. HubSpot AEO
HubSpot AEO offers entrepreneurs a transparent view of how their model is displaying up throughout main reply engines, like ChatGPT, Perplexity, and Gemini. It tracks share of voice on the immediate stage, displaying precisely which prompts cite a model, which cite rivals, and the place a model is totally absent. As an alternative of requiring AEO experience, it interprets complicated visibility knowledge into plain-language insights that groups can act on instantly.
The instrument additionally connects that visibility knowledge to a concrete technique. Entrepreneurs can observe precedence prompts, analyze which sources and content material sorts AI engines cite, and establish the place rivals are gaining share of voice. From there, HubSpot AEO generates prioritized suggestions with clear subsequent steps, serving to groups transfer from “we’re not displaying up” to an outlined plan for enhancing visibility.
What I like: HubSpot AEO doesn’t simply floor gaps — it reveals entrepreneurs precisely the place they’re shedding floor to rivals and gives a prioritized, plain-language motion plan they will use straight away.
Finest for: Entrepreneurs who need a quick, accessible option to perceive how their model reveals up in AI-generated solutions and get a transparent motion plan.
2. HubSpot AEO Options in Advertising Hub
AEO options in Advertising Hub Professional and Enterprise give entrepreneurs a transparent view of how their model seems throughout reply engines. Markteres may also get a method for enhancing visibility and the instruments to implement it — multi functional end-to-end system.
As a result of it’s related to HubSpot CRM, the Advertising Hub routinely suggests probably the most related prompts primarily based on an organization’s industries, rivals, and buyer segments, making insights extra particular and actionable from day one. Suggestions additionally get sharper over time as extra CRM knowledge informs the system.
HubSpot AEO surfaces visibility gaps throughout prompts and rivals, tracks reply share tendencies over time, and connects AI visibility knowledge to contact and pipeline reporting in HubSpot CRM — so entrepreneurs can tie AEO efficiency to precise enterprise outcomes, not simply impressions.
What I like: Groups with a number of hubs can take AEO solutions from Advertising Hub and implement them in Content material Hub. When the AEO instrument surfaces a niche, entrepreneurs can transient and publish new content material.
Finest for: Advertising groups that wish to join AI visibility insights on to execution utilizing their CRM knowledge and current advertising workflows.
3. HubSpot AEO Grader
HubSpot AEO Grader benchmarks reply engine visibility by measuring how usually a model seems in AI-generated solutions relative to rivals. It offers groups a snapshot of their share of voice throughout key prompts, together with perception into how their model is being represented in these solutions. This makes it simpler to know not simply whether or not a model is seen, however the way it compares in aggressive contexts.
The instrument acts as an entry level into AEO by serving to entrepreneurs rapidly assess the place they stand and establish whether or not visibility gaps exist. From that preliminary benchmark, groups can begin to perceive which questions matter most for his or her enterprise and the place they could want to enhance their presence in AI-generated responses.
AEO Grader can be utterly free, making it a fantastic place to begin for entrepreneurs simply dipping their toes into AEO.
What I like: It gives a fast, low-friction option to perceive how usually a model seems in AI solutions and the way it stacks up towards rivals, with out requiring any setup or prior AEO expertise.
Finest for: Entrepreneurs benchmarking AI visibility throughout the funnel.
4. Perplexity
Operating precedence queries immediately in Perplexity offers entrepreneurs a quick, free view into what sources are being cited and the way solutions are structured. Perplexity reveals citations inline, making it simple to establish which competitor URLs are incomes placement.
Professional tip: Use Perplexity’s “Focus” modes (Internet, Educational, Writing) to check how reply sources fluctuate by question context.
Finest for: Fast qualitative spot-checks.
5. ChatGPT with Browse
ChatGPT’s looking mode surfaces citations for present queries. It’s significantly helpful for testing consideration-stage and comparability queries (“finest X for Y” codecs), the place model mentions in AI solutions have the very best buy affect.
Finest for: Testing conversational and mid-funnel queries.
6. Ahrefs
Conventional search engine marketing instruments stay useful for diagnosing why sure pages earn AI citations — backlink authority, on-page optimization, and topical authority indicators all contribute to LLM quotation patterns.
Use Ahrefs to audit competitor pages that persistently earn citations, and establish the search engine marketing components which may be reinforcing their AI visibility.
Finest for: Pairing conventional search engine marketing knowledge with AEO insights.
7. BrightEdge or Conductor
Enterprise search engine marketing platforms are starting so as to add AI Overview and reply engine monitoring options. These are finest fitted to massive groups managing tons of of subject clusters that want automated quotation monitoring and executive-ready reporting.
Finest for: Enterprise groups working AEO at scale.
AEO Competitor Evaluation Metrics and Dashboards
Measure reply share and quotation frequency.
Reply share is the foundational AEO metric: the share of queries in an outlined set the place a model is cited within the AI-generated reply. It’s the AEO equal of natural market share.
Monitor reply share at three ranges:
- General. Throughout a full question set.
- By subject cluster. To establish the place a model is profitable and shedding.
- Over time. To measure whether or not content material investments are enhancing visibility.
Quotation frequency is the uncooked depend behind reply share — what number of instances a site or URL is cited throughout the question set. Excessive quotation frequency on a small variety of pages could point out over-reliance on a couple of content material belongings; broad quotation frequency throughout many pages indicators sturdy topical authority.
Monitor entity protection and QA depth.
Entity protection measures whether or not a model, product, and key subjects are explicitly acknowledged and related accurately by reply engines. Check this by asking LLMs immediately: “What’s [your brand]?” / “What does [your brand] do?” / “Who makes use of [your product]?” If solutions are obscure, incomplete, or incorrect, entrepreneurs have an entity readability downside that can suppress citations throughout their full question set.
QA depth measures how utterly a model’s content material solutions the particular questions in its question set. Rating competitor content material and your individual on a easy rubric:
- Does the web page reply the query immediately within the opening part?
- Does it cowl the query comprehensively (together with follow-up questions and edge instances)?
- Is the reply structured for simple extraction (headers, bullets, numbered steps)?
Join AI reply visibility to conversions.
The toughest — and most necessary — AEO measurement problem is connecting AI visibility to the pipeline. I like to recommend a multi-touch method:
- UTM monitoring on cited URLs. Guarantee all high-priority content material belongings have UTM parameters so groups can observe site visitors from AI-referred clicks in HubSpot or GA4.
- Self-reported attribution. Add “How did you hear about us?” fields to kinds and observe “AI search” or “ChatGPT/Perplexity” as a supply possibility. This captures influenced pipeline that by no means generates a tracked click on.
- Darkish social monitoring. Monitor branded search quantity and direct site visitors tendencies in parallel with AEO investments — AI reply visibility usually drives model searches that convert via direct channels.
Professional tip: In HubSpot, create a customized contact property for AI-attributed first contact. Over time, this builds a dataset that correlates AEO content material investments with precise contact and deal creation.
AEO in HubSpot Advertising Hub Professional and Enterprise connects AI visibility monitoring to CRM knowledge, making it attainable to tie reply engine efficiency to contacts, pipeline, and income in the identical reporting system.
Flip AEO Competitor Insights Into Actions
As soon as your evaluation is full, translate findings right into a prioritized motion record. Listed here are the commonest and highest-impact actions I’ve seen AEO competitor evaluation floor:
- Create direct-answer content material for high-gap queries. If a competitor earns citations on 8 out of 10 queries in a subject cluster and also you earn 0, the quickest path to closing that hole is publishing purpose-built QA content material that immediately solutions these questions — structured with a transparent query because the H2, a direct reply within the first 1–2 sentences, and supporting element beneath.
- Replace and restructure current pages. Many quotation wins come from reformatting current content material somewhat than creating new content material. Add direct solutions, FAQ sections with schema markup, and clearer entity statements to pages which can be already listed and authoritative.
- Construct entity disambiguation content material. If LLMs give incomplete or inaccurate solutions about your model, publish an authoritative “About” or “What’s [Brand]?” web page with structured entity info. Reinforce entity indicators throughout your website and in third-party sources (Wikipedia, Crunchbase, press protection).
- Prioritize subject clusters the place reply share is low however competitor content material is weak. Not each hole requires competing with a dominant rival. Search for clusters the place no competitor has sturdy AEO content material — these are the quickest paths to establishing first-mover quotation authority.
- Add comparability and “finest for” content material. Comparability queries (“X vs. Y,” “finest [tool] for [use case]”) are high-intent and incessantly answered by LLMs. If rivals are profitable these queries and also you’re not, comparability content material is a high-priority hole to shut.
- Strengthen inside linking between high-performing and low-performing pages. LLMs index topical authority indicators throughout a site. Pages that aren’t incomes citations could profit from stronger connections to your most-cited content material.
- Submit up to date content material to Google for re-indexing. For pages you’ve up to date to enhance QA depth or entity readability, use Google Search Console to request re-indexing so up to date indicators are picked up rapidly.
- Monitor modifications at month-to-month intervals. AEO aggressive dynamics shift as rivals publish new content material and as LLMs replace. Construct a month-to-month cadence of working your precedence question set and updating your reply share benchmarks.
Steadily Requested Questions About AEO Competitor Evaluation
How usually do you have to run AEO competitor evaluation?
I like to recommend a full AEO competitor evaluation — working your full question set, documenting citations, and updating benchmarks — on a month-to-month cadence for many groups.
For aggressive markets or throughout lively content material campaigns, biweekly monitoring of top-priority question clusters is well worth the funding. In contrast to conventional search engine marketing rankings, which replace repeatedly, AI quotation patterns can shift meaningfully after a competitor publishes new content material or after a mannequin replace — so common snapshots are essential to detect modifications.
How do you attribute pipeline impression from AI solutions?
Pipeline attribution for AI solutions requires a mix of strategies as a result of AI-generated solutions don’t all the time generate trackable clicks.
Use UTM-tagged URLs on cited content material to seize direct referral site visitors, add reply engines as a self-reported attribution possibility on kinds and in gross sales conversations, and monitor branded search and direct site visitors tendencies as a proxy for AI-influenced consciousness.
In HubSpot, customized contact properties and deal supply fields allow you to construct a longitudinal view of an AI-attributed pipeline over time. Inside HubSpot Advertising Hub, entrepreneurs can use CRM knowledge, customized properties, and reporting instruments to trace AI-influenced contacts and construct a clearer view of how AEO contributes to pipeline over time.
What’s the easiest way to construction QA content material for LLM citations?
The content material format most persistently cited by LLMs is the direct-answer construction: the goal query seems verbatim (or near-verbatim) as an H2 or H3 heading; the primary 1–3 sentences present an entire, direct reply to that query; supporting element, examples, and nuance comply with in clearly organized subsections.
FAQ schema markup reinforces this construction for Google’s AI Overviews. HowTo schema works equally for process-oriented content material. Keep away from burying the reply in prolonged preambles — LLMs favor content material that will get to the purpose instantly.
When do you have to prioritize AEO over conventional search engine marketing?
AEO and conventional search engine marketing usually are not mutually unique — the identical content material high quality indicators that drive rankings (authority, depth, structured formatting, freshness) additionally drive AI citations.
Nonetheless, if analytics present declining natural click-through charges regardless of steady or enhancing rankings, that’s a sign that AI solutions are intercepting clicks to your goal queries. In that state of affairs, investing in AEO content material construction and quotation optimization is more likely to have the next marginal return than chasing extra rating enhancements.
Extra broadly, for any question sort the place AI Overviews or LLM solutions are already dominant, AEO must be the first optimization lens.
From Evaluation to Motion: Turning AEO Insights Into Aggressive Benefit
AEO competitor evaluation offers entrepreneurs one thing conventional search engine marketing by no means absolutely may: a direct view into how manufacturers are literally really useful in the meanwhile of decision-making. As an alternative of optimizing for rankings alone, groups can now measure quotation frequency, reply share, and entity presence — and perceive precisely why rivals are being surfaced in AI-generated solutions.
The actual worth, nonetheless, comes from what occurs subsequent. Figuring out gaps is barely helpful if groups can act on them rapidly and persistently. That’s the place instruments like HubSpot’s AEO Grader present an accessible place to begin, serving to entrepreneurs benchmark their present visibility and perceive how they examine. From there, HubSpot AEO and AEO options in HubSpot Advertising Hub allow ongoing monitoring, competitor evaluation, and prioritized suggestions — whereas additionally connecting these insights on to content material execution, CRM knowledge, and pipeline reporting.
For groups investing in AEO, the trail ahead is evident: Construct a dependable question set, observe reply share over time, and repeatedly refine content material primarily based on what AI engines truly cite. The businesses that operationalize this course of early received’t simply sustain with rivals — they’ll outline how their class is represented in reply engines.
