How to run a competitive analysis for GEO

Generative Engine Optimization (GEO) is changing how brands earn visibility online. Showing up in answers from ChatGPT, Perplexity, or AI Overviews is now as strategic as ranking in classic search results. To make that shift, competitive analysis is the first step you cannot skip: it shows which players already hold the attention of generative models, and how you can stand out. This guide walks you through a structured method to run that analysis and build a complete GEO strategy.

What is a competitive analysis applied to GEO?

A GEO competitive analysis is the process of identifying and evaluating the players that appear in answers generated by AIs such as ChatGPT, Google AI Overviews, Perplexity, or Gemini. Unlike classic SEO, which measures ranking on results pages, this analysis looks specifically at the citations, mentions, and recommendations that language models weave into their automated summaries.

The goal is to understand which competitors hold the attention of LLMs, where your company sits relative to them, and where you can strengthen your presence in an ecosystem that is still expanding.

What makes competitive analysis different in GEO

Competitor analysis in GEO rests on criteria that are very different from traditional search. How often LLMs cite a brand becomes a central metric: a competitor mentioned systematically on your target queries holds authority in the eyes of the models. That authority is no longer a SERP position. It is recurring presence in generated answers.

The quality of the information attached to each brand also matters. Generative search engines favor structured, factual content they can use directly when they build a summary. A competitor cited positively in a recommendation context has a real advantage over one that is only mentioned in a neutral way.

Another specificity: GEO analysis needs active monitoring across several AI platforms. A brand can dominate on ChatGPT and stay invisible on Perplexity, which often points to a gap in content strategy or perceived authority from one model to the next.

How it differs from a classic competitive analysis

Where traditional SEO analysis watches SERP positions and organic traffic, GEO analysis concentrates on visibility inside synthesized answers. That distinction changes the whole analytical process.

The table below shows the operational differences:

Criterion Classic competitive analysis (SEO) GEO competitive analysis

Data source Google results pages, SERP positions Answers generated by ChatGPT, Perplexity, Gemini, AI Overviews

Key metrics Positions, organic traffic, backlinks, domain authority Citation frequency, mention quality, appearance context, associated sentiment

Update frequency Weekly to monthly (gradual change) Daily to weekly (high volatility in AI answers)

Tools used SEMrush, Ahrefs, Google Search Console Otterly.ai, Peec AI, manual LLM queries, Semji

This shift asks digital marketing teams to run a dual approach: keep the SEO strategy in place, and build GEO-specific expertise so they can capture visibility in generative answers.

Direct and indirect competition in AI results

Identifying your competitors in the GEO ecosystem takes a different approach than classic SEO analysis. LLMs do not simply copy SERP rankings. They synthesize answers from multiple sources and create a new form of competition for visibility.

The 3 types of competition to watch in GEO

In Generative Engine Optimization, you face three distinct categories of competitors. Direct competitors appear on the same queries as you and give LLMs similar answers. When a user asks ChatGPT or Perplexity about your industry, these brands are cited alongside yours.

Indirect competitors hold the attention of generative models on adjacent topics. They do not offer the same product, but they answer related needs that AIs associate with your domain. A CRM software vendor can thus compete indirectly with a marketing automation tool in LLM answers about customer relationship management.

Potential competition is the set of emerging players in AI answers: expert content creators, specialist media, community knowledge bases such as Reddit. These sources can gain citation visibility quickly without ever having been traditional SEO competitors.

Identify the players capturing AI visibility

To map your competitors in GEO, use a systematic collection method. Put your strategic queries to ChatGPT, Perplexity, and Gemini, then record which brands and sources are cited. Studies show that Perplexity leans heavily on Reddit (46.7% of its primary citations), while ChatGPT favors consensus sources such as Wikipedia and comparison sites.

The analysis often turns up surprises. Your direct SEO competitor may be absent from AI answers, while a specialist publication you were not watching captures most of the citations. Visibility in LLMs does not mechanically copy SERP positions. It reflects the authority the models perceive and the structural quality of the content.

Spot emerging potential competitors

Beyond established players, watch the sources LLMs are starting to favor in your field. Expert creators, specialist communities, and technical knowledge bases are gaining influence quickly. A well-documented Reddit thread can become a reference source for Perplexity. A technical blog post can be cited systematically by ChatGPT.

The analysis does not stop at presence. Look at the context in which each competitor appears: is the brand actively recommended, mentioned neutrally, or included in a comparison? An explicit recommendation (“X is widely recommended for…”) has a much stronger commercial impact than a plain factual mention. To go deeper on the classic competitive dynamics that sit alongside this GEO work, see our complete guide to SEO competitive analysis.

Key steps of a GEO competitive study

An effective GEO competitive analysis rests on a four-phase method. Each phase produces data you need to understand how competitors capture visibility in AI-generated answers, and where your openings sit.

Step 1: define the scope and objectives of the analysis

Before you collect a single data point, you need a precise frame. Start by identifying the target queries that match your audience’s search intent: frequent questions, solution comparisons, expertise searches in your field. Then pick the LLMs to query first. ChatGPT, Perplexity, Claude, and Gemini cover most of the market, and you can add Google AI Overviews depending on your sector.

Define your priority themes. You cannot analyze everything at once. Focus on the topics where AI visibility has the most commercial impact. Then set measurable goals: raise your citation rate by X%, appear in Y% of answers on your strategic queries, or identify Z opportunities competitors have not used.

Step 2: collect data on your competitors

Data collection in GEO is fundamentally different from classic SEO. You have to query the AIs systematically with each query on your list and document the results carefully. For every generated answer, note which sources are cited, which competitors appear, and in what context: direct recommendation, comparative mention, or a simple factual citation.

Measure how often each competitor appears. A player cited in 8 answers out of 10 has far more authority than one mentioned once. Also document the tone of the mentions: is the brand presented positively, neutrally, or in an unfavorable context?

Use a spreadsheet or a dedicated tool to centralize the data. Collection has to be regular, because AI answers change quickly. What is true today can shift in a few weeks. Plan monthly collection cycles at a minimum, with weekly checkpoints on your most strategic queries.

Step 3: analyze each competitor’s strengths and weaknesses

Once the data is in, move to qualitative analysis. Identify each competitor’s strengths: why do the LLMs cite them? Perceived authority plays a major role. Recognized brands and established sources have a natural advantage. Content quality matters too: in-depth, well-structured, regularly updated articles are favored.

Also look at data structure. Competitors that use clear formats (tables, lists, precise definitions) make the LLMs’ work easier. Then assess their E-E-A-T level (Experience, Expertise, Authoritativeness, Trustworthiness). Credibility signals such as named authors, verifiable references, and a solid publication history strengthen the models’ trust.

On the weakness side, look for absences on certain strategic queries, outdated information that hurts credibility, or a poor AI reputation (negative mentions or documented controversies). Those gaps are openings to position yourself as a credible alternative.

Step 4: synthesize the results and build an action plan

The last step turns your observations into a concrete strategy. Start by identifying opportunities competitors do not cover: which important queries produce no satisfactory answer? Which angles are missing from competitor content? Those empty spaces are your best entry points.

Then prioritize the queries to target by crossing two criteria: commercial potential (impact on your business goals) and feasibility (your ability to produce authoritative content on the topic). Set a realistic content production calendar, starting with high-impact, low-competition topics.

Your analysis plan should include clear tracking indicators: change in your citation rate, number of queries where you appear, sentiment attached to your mentions. Plan regular refresh cycles. AI answers move much faster than traditional SERP rankings. Agility and regularity are your best assets in this environment.

GEO competitive mapping: template and a concrete example

Build a competitive map adapted to GEO

GEO competitive mapping uses a different logic than the classic approach. Instead of crossing price and quality, you work on two axes specific to AI-generated answers: citation frequency by LLMs (x-axis) and quality of the associated sentiment (y-axis). That visual makes it immediately clear which competitors dominate AI conversations, and with what level of recommendation.

To structure the analysis, you need a systematic collection grid. Here is the template we recommend:

Competitor Target query LLM Mention type Frequency Sentiment Overall score

Competitor A “best AI SEO tool” ChatGPT Recommendation 8/10 Positive 8.5

Competitor B “best AI SEO tool” Perplexity Simple mention 3/10 Neutral 3.0

Competitor C “how to optimize for AI” Gemini Citation 6/10 Positive 7.0

This grid lets you document each competitive appearance in factual terms. Mention type distinguishes three intensity levels: a simple mention (the name appears), a recommendation (the LLM explicitly advises the brand), and a citation (the AI attributes a specific piece of information to the source). Frequency measures appearances across a sample of tested queries. Sentiment qualifies the context (positive, neutral, negative).

Example of a GEO competitive analysis for a B2B company

Take a B2B SaaS company specialized in marketing automation that wants to assess its position against five direct competitors. The team selects 10 strategic queries tied to its core offering (“how to automate email campaigns”, “best marketing automation tool for SMBs”, and so on) and systematically queries ChatGPT, Perplexity, and Gemini.

After compiling the results, the map shows three distinct zones. Competitor A appears in 80% of answers with a very positive sentiment and sits as the clear leader. Competitors B and C occupy a middle zone (40-50% frequency, neutral to positive sentiment), while competitors D and E stay barely visible. The company itself sits at 25% frequency, with a positive sentiment that is still underused.

The strategic reading becomes clear: several opportunities appear on queries where no player dominates strongly. On “marketing automation for startups”, for example, only two competitors show up occasionally, which leaves space to take. Crossing that analysis with identified strengths (recognized expertise on certain topics), the company can adjust its positioning and concentrate effort on those blank zones of the GEO map.

Tools for a GEO marketing competitive analysis

Specialized tools for analyzing AI visibility

To measure your presence in AI-generated answers, you need tools built for GEO. Semji offers full GEO visibility tracking, with AI citation analysis, competitive monitoring, and actionable recommendations to optimize your content. The platform lets you follow mentions across several AI engines and identify exactly which prompts generate citations of your brand.

Other solutions such as Otterly.ai and Peec AI focus on citation tracking in LLMs. Otterly has an entry point from $29 per month and tracks mentions on ChatGPT and Perplexity. Peec AI (from €89 per month) covers six AI engines and classifies citations by domain type and page type, which makes concrete opportunities easier to spot. Similarweb, traditionally used for web traffic analysis, also publishes data on traffic generated by AI platforms.

Do not forget that the LLMs themselves are free, powerful audit tools. Manually querying ChatGPT, Perplexity, or Gemini with your target queries lets you check in real time who is cited and in what context. To centralize results, combine these specialized tools with structured spreadsheets and cross GEO data with your classic SEO tools.

Porter’s 5 forces applied to GEO

Porter’s 5 forces, originally designed to assess the attractiveness of a sector, adapt well to GEO and help you anticipate shifts in the competitive landscape. In the AI-answer ecosystem, those forces take new forms.

The power of LLMs as intermediaries replaces the traditional power of distributors: these models decide which sources to cite and how to present your brand, which gives them considerable influence over your visibility. The threat of new entrants in AI answers is especially strong, because barriers to entry are lower than in classic SEO. An expert creator or a specialist publication can gain authority with LLMs quickly, without a website that has been established for years.

Rivalry among cited sources is the heart of GEO competition: you are no longer fighting for a SERP slot, but to be mentioned among three to five sources in a synthetic answer. The power of expert content creators has grown, because LLMs favor demonstrated expertise and high-value content. Finally, the threat of alternative formats such as video, podcasts, or audio that AIs reuse is a growing substitute for traditional text.

Use this grid to identify where your marketing strategy needs to change: which content types to prioritize, which alliances to build with expert creators, and which formats to explore to keep a competitive edge in AI answers.

Going further with your GEO strategy

Competitive analysis is a foundational pillar of any GEO strategy that is meant to last. By identifying who captures visibility in AI-generated answers, you lay the groundwork for lasting differentiation and a stronger brand image over time.

That analysis is still only one part of a broader approach. To build a complete GEO strategy, you also need to assess your own presence in LLMs, track performance over time, and understand precisely how AI models cite and mention sources.

Three complementary steps follow naturally after this competitive analysis. First, run a complete GEO audit to map your current visibility and identify your priority improvement areas. Next, master the difference between a citation and a mention in LLMs so you understand how AIs assign authority and credibility to sources. Finally, put rigorous tracking in place for the essential GEO KPIs to follow so you can measure the real impact of your actions and adjust the strategy as you go.

Combining these four dimensions (competitive analysis, audit, citation mechanics, and performance tracking) is how you build a GEO strategy that delivers real added value over the long term.

FAQ on competitive analysis in GEO

How do you run an effective competitive analysis for GEO?

To run a GEO competitive analysis well, start by defining the scope: pick the strategic queries for your business and the LLMs to watch (ChatGPT, Perplexity, Gemini). Then identify competitors systematically by putting those queries to the generative engines and noting which brands are cited, recommended, or compared. Collect the AI data by documenting appearance frequency, mention type, and the sentiment attached to each competitor. Finally, analyze the results to spot each player’s strengths and weaknesses, then turn those insights into a concrete action plan: find uncovered query opportunities, prioritize your content work, and set a production calendar so you can gain visibility in generative answers.

What are the 4 conditions of perfect competition applied to GEO?

The four classic conditions of perfect competition take on a particular shape in the GEO ecosystem. Atomicity shows up as the very large number of potential sources LLMs can cite. No single player can monopolize AI answers in a given sector. Homogeneity means the models treat all sources against similar authority and relevance criteria, regardless of company size. Information transparency exists because LLMs can synthesize and compare available content instantly, giving users a clear view when they decide to buy. Finally, free entry on the GEO market lets any new player get cited by AIs by producing quality expert content, without an insurmountable technical barrier, which keeps a constant competitive dynamic in generative answers.

How often should you refresh your GEO competitive analysis?

A monthly minimum update of your GEO competitive analysis is the baseline, plus weekly checkpoints on your priority queries. That cadence comes from a simple fact: AI-generated answers change much faster than classic SERPs. LLMs regularly ingest new data, change preferred sources, and adjust citation criteria as their models update. A competitor can gain or lose AI visibility in a few weeks. To keep a clear view of your market and anticipate competitive moves, watch the queries tied to your core offer every week, and run a full monthly analysis covering your whole competitive scope, your direct and indirect competitors, and new players emerging in AI answers.

What is the difference between SEO competitive monitoring and GEO competitive analysis?

SEO competitive monitoring follows your competitors’ positions on classic search engine results pages (Google, Bing SERPs), measuring their ranking for targeted keywords and analyzing their link-building and on-page strategy. GEO competitive analysis, by contrast, watches the citations, mentions, and recommendations of your competitors in answers generated by AIs such as ChatGPT, Perplexity, or Gemini. The metrics are different: in SEO you track positions and organic traffic, while in GEO you measure citation frequency, mention context, and the sentiment attached to each brand in automated summaries. The tools diverge too. SEO monitoring relies on rank-tracking platforms. GEO analysis needs specialized LLM-monitoring tools, or regular manual queries of generative engines, to understand what market picture is actually forming in the AI-answer ecosystem.