AI Reputation and Competition: Measuring Sentiment and Share of Voice

In classic search results, it is relatively clear who ranks ahead of you. In AI answers, things are more complicated. A competitor may be mentioned far more often in ChatGPT, Perplexity or Google AI OverviewsAI Overviews are AI-generated answer boxes from Google that appear above the classic search results. They summarize information from the web and can change click distribution.Open glossary entry →, even though your website ranks solidly in Google. Or your brand may be mentioned, but with a critical toneSentiment describes the tone in which a brand is portrayed in AI answers, for example positive, neutral, mixed, negative or uncertain. It measures the quality of a mention, not just its presence.Open glossary entry →, outdated information or incorrect reservations.

This is exactly where the RankScan insights “Competitor Dominates AI Answers” and “Negative AI Sentiment” come in:

• “Competitor Dominates AI Answers”: A competitor is mentioned more often, earlier or in greater detail than your brand for relevant prompts.
• “Negative AI Sentiment”: Your brand appears in AI answers with a negative, critical, uncertain tone or recurring reservations.

This article goes one step beyond the basic question “Is my brand mentioned?”. It focuses on the quality of the mention:

• How high is your share of voiceShare of Voice in AI answers measures the proportion of mentions a brand receives within a prompt set compared to competitors. It indicates the competitive position in AI answers.Open glossary entry → in artificial intelligence (AI) answers?
• Which competitors dominate specific topics?
• How is your brand described?
• Which sources shape AI sentiment?
• Which measures improve AI reputationAI Reputation describes how a brand is perceived in AI answers, that is how often, how prominently, in what tone and based on which sources it appears. It results from the brand's own website, third-party sources and model behavior.Open glossary entry → and competitive position?

The right framing is important:

AI reputation is not directly controllable. It is the result of your own website, third-party sources, user reviews, search results, crawlerCrawling is the process by which a search engine bot fetches a URL and downloads its content. It is the first step before a page can be indexed.Open glossary entry → access, prompt context and model behavior.

Good AI monitoring makes these signals visible and actionable.

Role in the RankScan cluster: This article does not focus only on whether your brand is mentioned or linked. It evaluates competitor dominance, tone, share of voice and the sources that shape the AI image of your brand.

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What Does “Competitor Dominates AI Answers” Mean? #

The RankScan insight “Competitor Dominates AI Answers” means that a competitor is mentioned more often, earlier or more strongly than your brand for relevant prompts.

Example:

Prompt:
Which tools help monitor AI visibilityAI Visibility describes whether and how visible a brand, website or person is in AI-generated answers. It measures mention, position, description and source linking across multiple platforms and prompts.Open glossary entry →?

Answer:
1. Competitor A
2. Competitor B
3. Competitor C
...
Your brand is not mentioned.

Or:

Prompt:
Which providers are suitable for marketing automation with Mautic?

Answer:
Competitor A is explained in detail.
Your brand is only mentioned in passing.

This is a share-of-voice problem. Your brand is less present than other providers at an important AI-driven decision moment.

Possible causes:

• competitors have stronger third-party sources,
• competitors are mentioned in more comparisons,
• competitors have more reviews,
• your positioning is unclear,
• your content is less citable,
• important crawlers are blocked,
• your brand is not strongly anchored in the topic,
• your website has few structured entityAn entity is a uniquely identifiable thing or concept, such as an organisation, person, brand, product, place or technical term. Search engines and AI systems process information as entities and their relationships.Open glossary entry → signals.

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What Does “Negative AI Sentiment” Mean? #

The RankScan insight “Negative AI Sentiment” means that your brand is presented critically or negatively in AI answers.

Examples:

“Provider X is well known, but is often criticized for high prices.”

“Users report slow support and unclear contract terms.”

“The solution appears less suitable for smaller companies.”

Negative sentiment can be accurate, outdated, exaggerated or wrong. That is why the source matters as much as the sentiment itself:

• Which information supports the statement?
• Is it current?
• Does it come from your website, a review, Reddit, a ratings platform or an old article?
• Does the statement repeat across several platforms?
• Does it point to a real product or service issue?

AI sentiment is not just reputation cosmetics. If a critical statement appears in an AI answer, it can influence buying decisions before a user ever visits your website.

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Why AI Monitoring Is Necessary #

AI answers are variable. They can differ depending on platform, prompt, language, market, time, model version, web access and available sources.

The original paper on Generative Engine Optimization (GEOGenerative Engine Optimization (GEO) covers measures that improve the chances of a brand or website being found, understood, extracted, classified and used as a source by generative AI answer systems. GEO extends classic SEO rather than replacing it.Open glossary entry →) describes generative search systems as systems that synthesize information from multiple sources and give website operators less direct control over when and how content is displayed. That is exactly why new metrics beyond classic rankings are needed.
Source: arXiv – GEO: Generative Engine Optimization

A single manual test is therefore too weak.

Professional AI brand monitoring needs:

• fixed prompt sets,
• repeated measurement,
• platform comparison,
• competitor comparison,
• source analysis,
• sentiment evaluation,
• development over time,
• documentation of changes.

A current research paper on AI visibility measurement argues that AI visibility should not be understood as one fixed value, but as a measurement with uncertainty. Repeated queries and confidence considerations are more meaningful than individual prompts.
Source: arXiv – Quantifying Uncertainty in AI Visibility

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The Most Important KPIs in AI Monitoring #

1. AI Share of Voice #

Share of voice in AI answers measures what share of mentions your brand receives compared with competitors.

Example:

Prompt set: 30 prompts
Total brand mentions: 120

Competitor A: 48 mentions = 40%
Competitor B: 30 mentions = 25%
Your brand: 18 mentions = 15%
Others: 24 mentions = 20%

Frequency alone is not enough. Prominence also matters.

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2. Answer Position #

Answer position shows how early your brand is mentioned.

Position	Meaning
First recommendation	very strong prominence
Top 3	strong visibility
Middle of the answer	present, but not leading
Side note	low prominence
Not mentioned	no AI visibility for this prompt

An early mention is more valuable than a late side note.

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3. Sentiment #

Sentiment describes the tone of the mention.

Sentiment	Example
Positive	“particularly suitable for Swiss small and medium-sized enterprises (SMEs)”
Neutral	“is a provider of marketing automation”
Mixed	“strong feature set, but complex to implement”
Negative	“is often criticized for poor support”
Uncertain	“information about the solution is limited”

For RankScan, recurring patterns are especially important. Individual negative phrases matter less than stable negative statements across many prompts.

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4. Citation Rate #

Citation rateCitation Rate measures the share of relevant AI answers in which a website is linked or named as a source. It is a monitoring metric, not an official ranking factor.Open glossary entry → measures how often your website is linked as a source when your brand or topic appears in AI answers.

Perplexity is particularly helpful for source analysis because the product strongly connects answers with visible source links. Perplexity documents its own crawlers, including PerplexityBot and Perplexity-User.
Source: Perplexity Docs – Perplexity Crawlers

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5. Source Dominance #

The brand is not the only thing that matters. The domains used by the AI as sources are also decisive.

Questions:

• Is your own website cited?
• Are competitor websites cited?
• Are third-party sites cited?
• Does negative sentiment come from review platforms?
• Do statements come from old articles?
• Are forums or Reddit threads used?
• Are the sources current and correct?

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Building a Prompt Set for AI Reputation #

Good AI monitoring starts with a fixed prompt set.

Prompt Types #

Prompt type	Example	Purpose
Category prompt	“Which tools help with AI visibility?”	Measure share of voice
Comparison prompt	“RankScan vs. competitor X”	Check differentiation
Reputation prompt	“What are people’s experiences with brand X?”	Check sentiment
Purchase-related prompt	“Which solution is suitable for Swiss SMEs?”	Check decision-stage visibility
Problem prompt	“How can I measure AI visibility?”	Check topic authority
Competitor prompt	“Which alternatives to competitor X are there?”	Check conquest potential
Local prompt	“Which providers in Switzerland help with SEOSearch Engine Optimization (SEO) covers all measures that help a website become more visible in unpaid (organic) search results.Open glossary entry → monitoring?”	Check market relevance

Minimum Setup #

For a start, the following is enough:

• 15 to 30 prompts,
• 3 to 5 direct competitors,
• 2 to 3 platforms,
• monthly repetition,
• fixed language and region,
• identical prompt wording,
• documented answers.

For critical brand or reputation situations, weekly monitoring can make sense.

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View Platforms Separately #

ChatGPT, Perplexity, Google AI Overviews, Gemini and Claude work differently. Results should therefore not be lumped together.

ChatGPT #

Depending on mode, ChatGPT can answer with or without web access. Source links are not always available. OpenAI documents different crawlers and user agents such as GPTBot, OAI-SearchBot and ChatGPT-User, each with different purposes.
Source: OpenAI Platform – Overview of OpenAI Crawlers

Perplexity #

Perplexity is strongly source-oriented and therefore useful for source analysis, citation rate and sentiment causes.

Google AI Overviews #

Google AI Overviews are embedded in Google Search. Google describes AI Overviews and AI Mode as search features in which content from the web may appear.
Google does not name a special GEO technique that guarantees inclusion; the normal Search fundamentals remain central.
Source: Google Search Central – AI features and your website

Important: A brand may be clearly visible in Perplexity and missing in ChatGPT — or the other way around. That is why platform segments are needed.

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Why a Competitor Dominates AI Answers #

1. Stronger Topic Authority #

The competitor has more and better content for a topic clusterA topic cluster is a group of thematically related pages in which a central pillar page and supporting detail pages link to each other. This creates a clear topical structure.Open glossary entry →.

Example:

• pillar pageA pillar page is the central overview page of a topic cluster. It covers a subject broadly and links to in-depth detail pages, which in turn link back to it.Open glossary entry →,
• detailed articles,
• comparison pages,
• glossary,
• FAQ (Frequently Asked Questions),
• cases,
• studies,
• product documentation,
• external mentions.

2. More Third-Party Sources #

AI systems do not rely only on a brand’s own website. Third-party sources can be decisive.

Typical sources:

• review platforms,
• industry directories,
• trade media,
• comparison articles,
• podcasts,
• YouTube,
• Reddit,
• LinkedIn,
• partner pages,
• customer cases,
• marketplaces,
• app stores.

3. Clearer Positioning #

Competitors are often mentioned when their category is unambiguous.

Example:

“tool for AI visibility monitoring”
“CRM for Swiss SMEs”
“e-commerce platform for B2B shops”

Vague self-descriptions such as “innovative digital solution” are less helpful for AI systems.

4. More Citable Content #

A research paper on AI citations found that influential sources are often more strongly structured, semantically better matched and richer in extractable information such as definitions, numbers, comparisons and process steps.
Source: arXiv – From Citation Selection to Citation Absorption

5. More Current or More Specific Information #

Another current study on competitive GEO found in a controlled test setup that topical relevance and position in context were the strongest drivers of first citations; explicit pricing information and current timestamps also helped consistently.
Source: arXiv – What Gets Cited: Competitive GEO in AI Answer Engines

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What to Do When a Competitor Dominates #

Step 1: Locate Dominance Precisely #

Do not ask generally: “Why does competitor X dominate?”

Better:

• for which prompt types?
• on which platform?
• in which market?
• for which topics?
• with which sources?
• in which answer position?
• with which sentiment?

Step 2: Analyze Sources #

Check:

• Which sources support the competitor?
• Are they the competitor’s own pages or third-party pages?
• Are they comparison articles?
• Are they review platforms?
• Are they trade media?
• Are specific data points repeated?

Step 3: Sharpen Differentiation #

Define clearly where you are better or different.

Examples:

for Swiss SMEs
for B2B SaaS
for agencies
for Mautic setups
for technical SEO audits
for AI visibility monitoring

Step 4: Close Content Gaps #

Build pages that answer specific user questions:

• comparison pages,
• use-case pages,
• industry pages,
• FAQ,
• methodology pages,
• customer cases,
• data/benchmark pages,
• integration pages,
• alternatives pages.

Step 5: Strengthen Off-Site Presence #

Build serious third-party sources:

• trade articles,
• industry profiles,
• customer reviews,
• partner pages,
• podcast guest appearances,
• guest posts,
• case studies with customers,
• comparison platforms.

Important: no fake mentions, no manipulated reviews and no forum spam.

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Why Negative AI Sentiment Emerges #

Negative sentiment rarely appears by chance.

Typical causes:

1. Real Product or Service Problems #

If many users mention the same criticism, AI systems may pick it up.

Examples:

• poor support,
• high prices,
• missing features,
• long contract periods,
• poor documentation,
• data protection concerns,
• poor app reviews.

2. Outdated Information #

An old review can still shape perception, even if the product has since improved.

3. Inconsistent Communication #

When different sources provide different information, uncertainty emerges.

Examples:

• different prices,
• different feature descriptions,
• contradictory target groups,
• old product names,
• multiple domains with inconsistent statements.

4. Third-Party Sources Dominate Perception #

If your own website does not provide clear counterinformation, review platforms or forums can shape the overall picture.

5. Missing Trust Signals #

Without sources, authors, contact details, case studies, reviews and transparent company data, a brand appears less trustworthy.

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What to Do About Negative AI Sentiment #

Step 1: Isolate the Statement #

Record exactly:

Which negative statement appears?
For which prompt?
On which platform?
How often does it repeat?
Which source is cited?

Step 2: Check the Source #

Ask:

• Is the source credible?
• Is the criticism current?
• Is the criticism justified?
• Are there several sources?
• Is outdated information responsible?
• Is the statement wrong, or just unfavorably worded?

Step 3: Fix the Cause #

If the criticism is justified, content alone is not enough.

Examples:

• improve support processes,
• make pricing more transparent,
• add data protection documentation,
• update product documentation,
• correct old help-center articles,
• actively collect customer feedback.

Step 4: Create Fact-Based Counterevidence #

Create clear, crawlable content:

• FAQ on the critical topic,
• transparencyAccountability means it is clear which person or organisation is responsible for a piece of content and a website. It is a core trust signal.Open glossary entry → page,
• methodology page,
• security or data protection page,
• product comparison,
• updated documentation,
• customer cases,
• review overview,
• changelog.

Step 5: Correct External Signals #

Check and update:

• review profiles,
• industry directories,
• partner pages,
• old guest articles,
• app store descriptions,
• comparison platforms,
• social profiles,
• knowledge panels,
• public relations (PR) articles.

Step 6: Measure Development #

Sentiment does not change immediately. Measure it over several weeks or months.

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Avoid Manipulation #

AI reputation cannot be improved sustainably with fake signals.

Risky tactics include:

• purchased fake reviews,
• forum spam,
• artificial Reddit comments,
• keywordA keyword is the search term users enter into a search engine to find information, products or providers. Keywords are the basis against which rankings and visibility are measured.Open glossary entry → stuffing,
• hidden text,
• invented awards,
• fake authors,
• manipulated Wikipedia entries,
• mass-generated PR copy with no substance.

Google describes several manipulative practices in its spam policies that are intended to deceive search systems. Even though AI systems follow their own logic, the same basic rule applies: manipulative signals are risky in the long term.
Source: Google Search Central – Spam Policies for Google Web Search

Better:

• real reviews,
• real cases,
• real data,
• transparent sources,
• clear corrections,
• expert contributions,
• consistent profiles,
• visible improvements.

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What a Good AI Reputation Check Looks At #

A good AI reputation check should not only count mentions.

A good check evaluates:

• share of voice per prompt set,
• share of voice per platform,
• answer position,
• competitors before/after the brand,
• sentiment per mention,
• recurring criticism,
• source links,
• linked domains,
• linked URLs,
• source qualitySource quality shows whether important statements are backed by reliable evidence. It does not mean adding links indiscriminately, but providing robust support for verifiable claims.Open glossary entry →,
• freshnessFreshness describes how current a piece of content is relative to the search intent. It is not equally important for every query.Open glossary entry → of sources,
• own site vs. third-party site,
• incorrect or outdated statements,
• brand context,
• prompt type,
• language and market,
• change over time,
• correlation with website changes,
• crawler access,
• external reputation sources.

This turns “Competitor Dominates AI Answers” and “Negative AI Sentiment” into concrete action areas.

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Example: Competitor Dominates ChatGPT #

Situation #

A Swiss B2B SaaS provider notices that prompts such as:

Which tools are suitable for AI visibility monitoring?
Which providers help with AI visibility?
Which alternatives are there to classic SEO reporting tools?

almost always mention an international competitor. The company’s own brand is missing or appears late.

RankScan reports:

“Competitor Dominates AI Answers”

Analysis #

AI monitoring shows:

• the competitor is mentioned in 68% of answers,
• the own brand is mentioned in 12%,
• the competitor usually appears in the top 3,
• the own brand usually appears late,
• sources come from comparison articles, product directories and trade blogs,
• the own website has no clear category page,
• external mentions are missing.

Solution #

1. Create a category pillar page.
2. Build comparison and use-case pages.
3. Sharpen product positioning.
4. Add Organization SchemaOrganization schema describes a company or organisation as an entity, with details such as name, URL, logo, address, contact and official profiles.Open glossary entry → and sameAssameAs is a Schema.org property that links an entity to reference pages confirming its identity, such as official profiles, Wikipedia or Wikidata. It serves disambiguation.Open glossary entry → profiles.
5. Place trade articles and customer cases on third-party sites.
6. Build reviews on relevant platforms.
7. Continue measuring the prompt set monthly.

Result #

The brand is first mentioned more often in long-tail prompts. After that, share of voice in broader prompts may gradually improve. This is not guaranteed, but the relevant signals are strengthened systematically.

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Example: Negative Sentiment Due to Outdated Sources #

Situation #

A software brand is repeatedly described in AI answers as “expensive and complex”.

RankScan reports:

“Negative AI Sentiment”

Analysis #

Source analysis shows:

• an old review from 2023 is picked up repeatedly,
• pricing has since changed,
• onboarding has been simplified,
• the company’s own website does not explain the changes,
• current customer reviews are scarce.

Solution #

1. Update the pricing page and explain it transparently.
2. Publish a changelog or product update page.
3. Add FAQ content on onboarding and costs.
4. Collect current customer testimonials.
5. Update review platforms.
6. Correct old external profiles where possible.
7. Continue monitoring for 3 months.

Result #

Negative sentiment can weaken when more current, consistent and trustworthy information becomes available.

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Common Mistakes in AI Reputation Management #

Mistake 1: Testing Only Your Own Brand #

Without competitor comparison, there is no share of voice.

Mistake 2: Overvaluing Individual Answers #

A single negative result is not yet a pattern.

Mistake 3: Ignoring Sources #

Without source analysis, it remains unclear where the AI gets its assessment from.

Mistake 4: Fighting Negative Sentiment With PR Phrases #

AI systems are more likely to adopt concrete, substantiated information than advertising language.

Mistake 5: Neglecting Third-Party Sites #

Many AI answers rely on external sources. Your own website alone is often not enough.

Mistake 6: Not Checking Technical Blocks #

If AI crawlersAI crawlers are automated bots that fetch web pages for AI providers in order to find content, make it available for search functions, retrieve it for answers, or, depending on the provider, use it for model training.Open glossary entry → or search crawlers are blocked, current information may be missing.

Mistake 7: Treating Reputation Problems Only as Search Engine Optimization (SEO) #

Real criticism must be addressed in product, service or support.

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Prioritization: What to Do First? #

Problem	Priority	Why
Competitor dominates purchase-related prompts	High	direct decision relevance
negative statement repeats across several platforms	High	reputation risk
incorrect or outdated information is cited	High	correctable and business-critical
own website is never used as a source	High	weak source authority
brand is mentioned late, but neutrally	Medium	improve prominence
competitor dominates only very broad prompts	Medium	check long-tail prompts first
one negative result without repetition	Low to medium	monitor
neutral mention without link	Medium	citation-rate issue

The most important rule:

Measure first, then understand sources, then fix real causes and build consistent signals.

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Checklist: Check AI Reputation and Share of Voice #

Use this checklist:

• Is there a fixed prompt set?
• Are direct competitors measured as well?
• Are platforms evaluated separately?
• How high is your share of voice?
• Which competitors appear more often?
• Who is mentioned first?
• What is your brand sentiment?
• Do negative statements repeat?
• Which sources are linked?
• Are these sources current?
• Is your own website cited?
• Is there incorrect or outdated information?
• Are important AI crawlers allowed?
• Is your positioning clear?
• Are there external reputation signalsAuthority signals are indicators of the reputation and recognised competence of a person or organisation. They correspond to the A (Authoritativeness) in E-E-A-T.Open glossary entry →?
• Are there real customer reviews?
• Is development measured over time?

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In addition, AI Readiness Score, Entity SEO, E-E-A-T and Visibility Index help narrow down the cause and prioritize the next SEO measures.

Conclusion: AI Reputation Emerges From Visibility, Context and Trust #

AI reputation is more than the question of whether a brand is mentioned. What matters is how often it appears compared with competitors, how early it is mentioned, how it is described and which sources support that representation.

The RankScan insights “Competitor Dominates AI Answers” and “Negative AI Sentiment” show two advanced AI visibility problems:

• The competition has more share of voice.
• Your brand is assessed critically or incorrectly.

The best approach is:

1. define a fixed prompt set,
2. measure competitors as well,
3. evaluate platforms separately,
4. record share of voice, position, sentiment and sources,
5. analyze causes instead of symptoms,
6. strengthen content, entities and third-party sources,
7. fix real reputation problems,
8. monitor development over time.

This turns AI monitoring into a strategic early-warning system for brand perception, competitive pressure and modern search visibilitySearch visibility describes how present and findable a website is in the organic search results. It is not the same as traffic, but reflects theoretical findability.Open glossary entry →.

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Sources and Further Reading #

• Google Search Central – AI features and your website
• OpenAI Platform – Overview of OpenAI Crawlers
• Perplexity Docs – Perplexity Crawlers
• Google Search Central – Spam Policies for Google Web Search
• arXiv – GEO: Generative Engine Optimization
• arXiv – Quantifying Uncertainty in AI Visibility
• arXiv – From Citation Selection to Citation Absorption
• arXiv – What Gets Cited: Competitive GEO in AI Answer Engines