| name | 42-brand-intelligence |
| description | Unified brand authority and sentiment analysis. Scans Reddit, YouTube, Wikipedia, LinkedIn, news, forums, and reviews for brand mentions. Scores mention volume, sentiment distribution, and composite Brand Authority Score. Combines what was previously two separate skills (brand-mentions + sentiment). Use when user says "brand mentions", "brand authority", "sentiment", "brand analysis", "what do people say about us", "online reputation".
|
| allowed-tools | Read, Write, Edit, Bash, Glob, Grep, WebFetch, WebSearch |
| version | 2.0.0 |
| tags | ["brand","mentions","sentiment","authority","geo","eeat"] |
Brand Intelligence Skill
Unified brand authority and sentiment analysis in a single crawl pass. This skill replaces two previously separate workflows (brand-mentions + sentiment) by scanning platforms once and producing both a Brand Authority Score and a Sentiment Breakdown in one report.
Core Insight
Brand mentions correlate approximately 3x more strongly with AI visibility than traditional backlinks. An Ahrefs study published in December 2025, analyzing 75,000 brands across AI search platforms, found that unlinked brand mentions -- references to a brand name without a hyperlink -- are a stronger predictor of whether AI systems cite and recommend a brand than Domain Rating or backlink count.
The critical finding: the platform where the mention appears matters enormously. Not all mentions are equal. A mention on YouTube or Reddit carries far more weight for AI citation than a mention on a low-authority blog, because AI training data and retrieval systems disproportionately index high-engagement platforms.
This inverts a core assumption of traditional SEO. In traditional SEO, a backlink from a high-DR site is the gold standard. In GEO, an unlinked mention on Reddit or a YouTube video description may be more valuable than a dofollow backlink from a DR 70 blog.
Understanding the sentiment of those mentions is equally important. A brand with high mention volume but predominantly negative sentiment faces a different challenge than one with low volume but glowing reviews. AI models weigh sentiment signals when deciding whether to recommend a brand, making sentiment analysis inseparable from mention tracking.
Platform Importance Ranking for AI Citations
Based on the Ahrefs December 2025 study and corroborating research from Profound (2025) and Terakeet (2025):
1. YouTube Mentions -- Correlation ~0.737 (STRONGEST)
Why YouTube matters most:
- YouTube is the second-largest search engine and the largest video platform globally (2.5B+ monthly users).
- AI training datasets heavily incorporate YouTube transcripts, descriptions, and metadata.
- Google's Gemini and AI Overviews directly reference YouTube content.
- Perplexity and ChatGPT both index and cite YouTube video content.
- YouTube transcripts are particularly valuable because they contain natural language mentions in conversational context, which aligns with how AI models process and generate text.
What to check:
- Brand YouTube channel: Does the brand have an active YouTube channel? How many subscribers? Video count? Upload frequency?
- Third-party video mentions: Are other YouTubers or channels mentioning the brand? In what context (reviews, tutorials, comparisons)?
- Video descriptions: Does the brand name appear in video descriptions of industry-relevant content?
- Video transcripts: Is the brand mentioned in spoken content of relevant videos? (AI models index transcripts)
- YouTube search presence: When searching "[brand name]" on YouTube, do results appear? Are they positive?
- Comment mentions: Is the brand mentioned in comments on relevant industry videos?
Scoring for YouTube (0-100):
| Score | Criteria |
|---|
| 90-100 | Active channel with 10K+ subscribers, regular uploads, brand mentioned in 20+ third-party videos, appears in YouTube search results for industry terms |
| 70-89 | Active channel with 1K+ subscribers, brand mentioned in 10-19 third-party videos, some YouTube search presence |
| 50-69 | Channel exists with some content, brand mentioned in 5-9 third-party videos, limited YouTube search presence |
| 30-49 | Channel exists but inactive, brand mentioned in 1-4 third-party videos |
| 10-29 | No channel or empty channel, brand mentioned in 1-2 videos only |
| 0-9 | No YouTube presence whatsoever |
2. Reddit Mentions -- High Correlation
Why Reddit matters:
- Reddit is one of the most heavily indexed platforms in AI training data (confirmed in Google's $60M/year Reddit licensing deal, 2024).
- AI systems heavily weight Reddit for product recommendations, comparisons, and user sentiment.
- "Reddit" is now appended to an estimated 10-15% of Google searches by users seeking authentic opinions.
- Perplexity frequently cites Reddit threads as sources.
- ChatGPT and Claude both reference Reddit discussions when answering product/service questions.
What to check:
- Subreddit presence: Is the brand discussed in relevant subreddits? Which ones?
- Mention volume: How many Reddit threads mention the brand? What is the trend (increasing/decreasing)?
- Sentiment: Are mentions mostly positive, negative, or neutral? What are common praise points and complaints?
- Official presence: Does the brand have an official Reddit account? Do they participate in discussions? Have they done AMAs?
- Recommendation threads: Does the brand appear in "What do you recommend for X?" threads? Is it the top recommendation or an also-ran?
- Subreddit community: Does the brand have its own subreddit? How active is it?
Scoring for Reddit (0-100):
| Score | Criteria |
|---|
| 90-100 | Frequently recommended in relevant subreddits, predominantly positive sentiment, active official presence, own subreddit with 5K+ members, appears in top recommendations for industry queries |
| 70-89 | Regularly mentioned in relevant subreddits, mostly positive sentiment, some official presence, appears in multiple recommendation threads |
| 50-69 | Mentioned in several relevant threads, mixed sentiment, brand name is recognized by community members |
| 30-49 | Occasional mentions, limited to 1-2 subreddits, no official presence |
| 10-29 | Rare mentions, brand largely unknown on Reddit |
| 0-9 | No Reddit presence |
3. Wikipedia Presence -- High Correlation
Why Wikipedia matters:
- Wikipedia is one of the highest-authority sources in AI training data. All major AI models have been trained on Wikipedia dumps.
- AI systems use Wikipedia as a primary source for entity recognition -- determining whether a brand is a "real" entity worth knowing about.
- Wikidata (Wikipedia's structured data sibling) provides machine-readable facts that AI models use for knowledge graph construction.
- Having a Wikipedia page is a strong signal of notability, which correlates with AI systems treating the brand as an authoritative entity.
What to check:
- Wikipedia page: Does the brand or company have its own Wikipedia article? Is it marked for deletion or quality issues?
- Founder page: Does the founder/CEO have a Wikipedia page? (Strong authority signal)
- Wikipedia citations: Is the brand's website cited as a reference in any Wikipedia articles?
- Wikidata entry: Does the brand have a Wikidata item (Q-number)? How complete is it?
- Wikipedia mentions: Is the brand mentioned in other Wikipedia articles (industry articles, competitor pages, category pages)?
- Article quality: If a Wikipedia page exists, is it a stub, start-class, or higher quality?
Scoring for Wikipedia (0-100):
| Score | Criteria |
|---|
| 90-100 | Detailed Wikipedia article (B-class or higher), Wikidata entry with complete properties, brand cited as reference in multiple articles, founder has Wikipedia page |
| 70-89 | Wikipedia article exists (start-class or higher), Wikidata entry exists, brand mentioned in 2+ other Wikipedia articles |
| 50-69 | Wikipedia article exists (stub or start), basic Wikidata entry, limited mentions in other articles |
| 30-49 | No Wikipedia article but brand is mentioned in other articles or cited as reference; Wikidata entry may exist |
| 10-29 | Brand mentioned in 1-2 Wikipedia articles as a passing reference only |
| 0-9 | No Wikipedia or Wikidata presence of any kind |
4. LinkedIn Presence -- Moderate Correlation
Why LinkedIn matters:
- LinkedIn content is increasingly indexed by AI systems for professional and B2B context.
- Company LinkedIn pages and employee thought leadership posts build brand entity signals.
- AI models reference LinkedIn for company information, team credentials, and professional authority.
- LinkedIn articles and posts are indexed by search engines and AI crawlers.
What to check:
- Company page: Does the brand have a LinkedIn company page? Follower count? Post frequency?
- Employee thought leadership: Are employees (especially leadership) posting thought leadership content that mentions the brand?
- Company mentions: Is the brand mentioned in LinkedIn posts by non-employees? Industry analysts? Customers?
- LinkedIn articles: Are there long-form LinkedIn articles about or mentioning the brand?
- Employee profiles: Do employees list the company with detailed descriptions? Do they have strong professional profiles?
- Engagement metrics: What is the typical engagement (likes, comments, shares) on company posts?
Scoring for LinkedIn (0-100):
| Score | Criteria |
|---|
| 90-100 | Active company page with 10K+ followers, leadership regularly posts thought leadership, brand frequently mentioned by industry professionals, strong employee profiles |
| 70-89 | Active company page with 5K+ followers, some employee thought leadership, occasional third-party mentions |
| 50-69 | Company page exists with 1K+ followers, irregular posting, limited third-party mentions |
| 30-49 | Company page exists but is sparse or inactive, few followers, no third-party mentions |
| 10-29 | Basic company page with minimal information |
| 0-9 | No LinkedIn company page |
5. News and Press -- Moderate Correlation
Why news matters:
- News mentions build entity authority and recency signals.
- AI models use news sources for up-to-date brand information and factual claims.
- Recent coverage (last 6 months) is far more valuable than older mentions.
What to check:
- Has the brand been covered by major news outlets or industry publications?
- How recently? What was the context (positive feature, controversy, product launch)?
- Is the brand mentioned in news aggregators and Google News?
Scoring for News (0-100):
| Score | Criteria |
|---|
| 90-100 | Regular coverage in major outlets, featured in industry reports, consistent press mentions in last 6 months |
| 70-89 | Multiple news mentions in last year, some industry publication features |
| 50-69 | Occasional news mentions, mostly press releases or minor coverage |
| 30-49 | Rare news mentions, limited to niche publications |
| 10-29 | One or two mentions total |
| 0-9 | No news coverage found |
6. Forums, Reviews, and Other Platforms -- Supplementary
These platforms have lower but still meaningful correlation with AI visibility:
Quora
- Relevance: Quora answers are frequently included in AI training data and cited by Perplexity.
- What to check: Is the brand mentioned in Quora answers to industry-relevant questions? Does the brand have an official Quora presence?
- Signal strength: Moderate for B2C, lower for B2B.
Stack Overflow / Stack Exchange
- Relevance: Critical for developer-facing brands (SaaS, dev tools, APIs).
- What to check: Is the brand's product discussed in Stack Overflow questions/answers? Does the brand have a tag? Do they have an official account answering questions?
- Signal strength: High for technical products, irrelevant for most B2C.
GitHub
- Relevance: Critical for open-source and developer-focused brands.
- What to check: Does the brand have a GitHub organization? Stars on repositories? Mentions in other repos' documentation or discussions?
- Signal strength: High for dev tools and open-source, low for non-technical brands.
Industry Forums and Communities
- Relevance: Niche authority signals that AI models pick up from domain-specific training data.
- What to check: Is the brand discussed in industry-specific forums (e.g., Hacker News for tech, ProductHunt for startups, industry-specific Slack communities)?
- Signal strength: Moderate, but valuable for establishing niche authority.
Review Sites (Trustpilot, G2, Capterra, etc.)
- Relevance: AI models reference review sites for product recommendations and brand reputation.
- What to check: Does the brand have profiles on relevant review sites? What are the ratings and review volumes? Are reviews recent?
- Signal strength: High for B2B SaaS (G2, Capterra) and consumer brands (Trustpilot, Amazon reviews).
Podcasts
- Relevance: Growing AI training data source. Transcripts are increasingly indexed.
- What to check: Has the brand or its leadership appeared on podcasts? Are podcast transcripts mentioning the brand indexed by search engines?
- Signal strength: Moderate and growing.
Composite Brand Authority Score
Scoring Formula
| Platform | Weight | Rationale |
|---|
| YouTube Presence | 25% | Strongest correlation with AI citation (0.737) |
| Reddit Presence | 25% | Second strongest correlation; critical for product recommendations |
| Wikipedia / Wikidata | 20% | Entity recognition foundation; AI training data cornerstone |
| LinkedIn Authority | 15% | Professional authority signals; B2B relevance |
| Other Platforms | 15% | Supplementary signals from news, Quora, GitHub, forums, reviews, podcasts |
Formula:
Brand_Authority_Score = (YouTube * 0.25) + (Reddit * 0.25) + (Wikipedia * 0.20) + (LinkedIn * 0.15) + (Other * 0.15)
Score Interpretation
| Score Range | Rating | Interpretation |
|---|
| 85-100 | Dominant | Brand is a well-recognized entity across AI platforms. Highly likely to be cited and recommended by AI systems. |
| 70-84 | Strong | Brand has solid cross-platform presence. AI systems likely recognize and cite it for relevant queries. |
| 50-69 | Moderate | Brand has presence on some platforms but gaps exist. AI citation is inconsistent. |
| 30-49 | Weak | Brand has limited platform presence. AI systems may not recognize it as a distinct entity. |
| 0-29 | Minimal | Brand has negligible platform presence. AI systems are unlikely to cite or recommend it. |
Sentiment Distribution Model
While the Brand Authority Score measures volume and presence, the Sentiment Distribution measures how people feel about the brand on each platform. Both are produced from the same crawl pass.
Sentiment Categories
| Sentiment | Indicators |
|---|
| Positive | Recommendations ("I love [brand]," "We switched to [brand] and...", "Highly recommend"), upvoted mentions, positive comparison against competitors |
| Neutral | Factual mentions ("We use [brand] for...", "[Brand] offers..."), questions about the brand, balanced comparisons |
| Negative | Complaints ("Avoid [brand]", "[Brand] has terrible support"), downvoted recommendations, negative comparisons |
Per-Source Sentiment Scoring
For each platform where content is found, calculate:
- Positive % -- percentage of mentions with positive sentiment
- Neutral % -- percentage of mentions with neutral sentiment
- Negative % -- percentage of mentions with negative sentiment
- All three must sum to exactly 100%
Overall Sentiment Calculation
Use weighted aggregation across sources:
- Equal base weight per source, with a confidence multiplier based on item count:
- High confidence (20+ items from source): multiplier 1.0
- Medium confidence (5-19 items): multiplier 0.7
- Low confidence (1-4 items): multiplier 0.4
- Round to whole percentages; adjust largest category if sum is not exactly 100%
Emotion Profile
Extract dominant emotions from the content:
- 5 core emotions (always present): trust, frustration, excitement, disappointment, concern
- 2-3 dynamic context-specific emotions (e.g., humor, outrage, hope, nostalgia)
- All percentages (core + dynamic) must sum to exactly 100%
Topic Extraction
Extract 8-15 specific, concrete topics from the source content:
- GOOD: "Battery degradation after 2 years", "Autopilot false braking incidents"
- BAD: "Product quality", "Customer service" (too generic)
- Group topics into 3-5 broader themes
- For each topic, produce sentiment percentages, explanation, dominant emotions, per-source sentiment lean, and 2-3 representative verbatim quotes with source URLs
Sarcasm Detection
When scoring sentiment (especially Reddit and forum content):
- Detect sarcasm and adjust sentiment scores to reflect intended meaning, not literal text
- Add a sarcasm note to a topic only when sarcasm is prevalent in that topic's discussion
Analysis Procedure
Step 1: Identify Brand Information
Gather the following from the user or from the website:
- Brand name (exact spelling, including any official variants)
- Founder/CEO name(s)
- Domain URL
- Industry/category
- Key products or services (top 3)
- Key competitors (for comparison context)
Step 2: Unified Platform Crawl (Single Pass)
Scan all platforms in one pass. For each platform, collect both mention data (for authority scoring) AND content/sentiment data (for sentiment analysis). This eliminates the redundant double-crawl that the separate skills previously required.
Crawl Priority Groups
Execute sources in three priority groups, reporting status between each group.
Group 1: Reddit + YouTube (highest priority, strongest AI correlation)
YouTube Check:
- WebSearch:
[brand name] site:youtube.com
- Check:
youtube.com/@[brand-name] or youtube.com/c/[brand-name] for official channel
- WebSearch:
"[brand name]" site:youtube.com (exact match for mentions in descriptions)
- Note: Channel subscriber count, video count, latest upload date, third-party mention count
- Capture sentiment signals from video titles, descriptions, and comments
Reddit Check:
- WebSearch:
[brand name] site:reddit.com
- WebSearch:
"[brand name]" site:reddit.com (exact match)
- Check:
reddit.com/r/[brand-name] for official subreddit
- Check:
reddit.com/user/[brand-name] for official account
- Note: Thread count, dominant subreddits, recommendation frequency
- Capture full comment content for sentiment analysis (Reddit is the richest sentiment source)
Group 2: Wikipedia + LinkedIn + News
Wikipedia Check (use BOTH methods to avoid false negatives):
Method 1 -- Python API check (most reliable, do this first):
python3 -c "
import requests, json
from urllib.parse import quote_plus
brand = '[Brand_Name]'
api_url = f'https://en.wikipedia.org/w/api.php?action=query&list=search&srsearch={quote_plus(brand)}&format=json'
r = requests.get(api_url, headers={'User-Agent': 'GEO-Audit/1.0'}, timeout=15)
data = r.json()
results = data.get('query', {}).get('search', [])
if results and brand.lower() in results[0].get('title', '').lower():
print(f'WIKIPEDIA PAGE EXISTS: {results[0][\"title\"]}')
print(f'URL: https://en.wikipedia.org/wiki/{results[0][\"title\"].replace(\" \", \"_\")}')
else:
print('No direct Wikipedia page found')
wd_url = f'https://www.wikidata.org/w/api.php?action=wbsearchentities&search={quote_plus(brand)}&language=en&format=json'
r2 = requests.get(wd_url, headers={'User-Agent': 'GEO-Audit/1.0'}, timeout=15)
wd = r2.json()
entities = wd.get('search', [])
if entities:
print(f'WIKIDATA ENTRY: {entities[0].get(\"id\", \"\")} -- {entities[0].get(\"description\", \"\")}')
"
Method 2 -- Direct URL check (backup verification):
- WebFetch:
https://en.wikipedia.org/wiki/[Brand_Name] -- check if the page loads (not a redirect to search)
- WebFetch:
https://en.wikipedia.org/wiki/[Founder_Name] for founder article
CRITICAL: Web search alone is NOT reliable for determining Wikipedia presence. ALWAYS run the Python API check first.
LinkedIn Check:
- WebSearch:
[brand name] site:linkedin.com
- Check:
linkedin.com/company/[brand-name] for company page
- Note: Follower count, post frequency, employee count listed, engagement levels
News Check:
- WebSearch:
"[brand name]" with time filter for last 6 months
- WebSearch:
[brand name] site:news.ycombinator.com (Hacker News)
- Capture headlines and snippets for sentiment analysis
Group 3: Forums + Reviews + Other Platforms
- WebSearch:
[brand name] site:quora.com
- WebSearch:
[brand name] site:stackoverflow.com (if technical brand)
- WebSearch:
[brand name] site:github.com (if technical brand)
- WebSearch:
[brand name] site:trustpilot.com
- WebSearch for topic-relevant review sites (G2, Capterra for SaaS; Yelp for local; Amazon for consumer products)
- Note: Presence/absence and quality of mentions on each platform
- Capture review content for sentiment analysis
Firecrawl Deep Crawl (Optional)
For deeper content extraction when Firecrawl is available, use it to scrape full page content from high-value sources. This is particularly useful for:
- Reddit thread full content (beyond search snippets)
- Review site detailed reviews
- Forum discussion threads
Check Firecrawl availability:
firecrawl --status
If available, use Firecrawl for scraping high-priority URLs discovered during the WebSearch phase. If not available, proceed with WebSearch/WebFetch content only.
You can also use ${CLAUDE_PLUGIN_ROOT}/scripts/brand_scanner.py if it exists in the project for automated scanning workflows.
Step 3: Sentiment Analysis (During Crawl)
As content is collected from each platform, perform sentiment analysis inline:
- Score sentiment per source -- Group items by source. For each source group, assess overall positive/negative/neutral percentages.
- Extract topics -- Identify 8-15 specific, concrete topics mentioned across all sources.
- Group into themes -- Organize topics into 3-5 broader themes.
- For each topic, produce:
- Sentiment percentages (positive/negative/neutral, must sum to 100%)
- Explanation (analytical, neutral tone, research-report style; 4-6 sentences for major topics, 2-3 for minor)
- Dominant emotions (top 1-2 emotions detected)
- Per-source sentiment lean: "positive", "negative", "neutral", or "mixed"
- Quotes: 2-3 representative verbatim quotes with source URL
- Sarcasm note (only when sarcasm is prevalent in this topic's discussion)
- Build emotion profile -- 5 core emotions + 2-3 dynamic context-specific, all summing to 100%.
- Calculate overall sentiment -- Weighted aggregation across all sources with confidence multipliers.
Step 4: Authority Score Calculation
- Score each platform (0-100) using the rubrics defined above.
- Apply weights to calculate the composite Brand Authority Score.
- Identify the strongest and weakest platforms.
Step 5: Competitive Comparison (Optional)
If competitors are identified, do a quick scan of their platform presence for context. This helps calibrate the score -- a brand with "moderate" Reddit presence in an industry where competitors have zero Reddit presence is relatively strong.
Step 6: Consistency Verification
Before generating the report, verify:
| Check | Rule |
|---|
| Overall sentiment | positive + negative + neutral = 100% |
| Per-source sentiment | Each source: positive + negative + neutral = 100% |
| Per-topic sentiment | Each topic: positive + negative + neutral = 100% |
| Emotion profile | All core + dynamic percentages = 100% |
| Topic count | 8-15 total across all themes |
| Theme count | 3-5 themes |
| Topics per theme | 2-5 per theme |
| Quote URLs | Every quote has a valid URL |
| Authority scores | Each platform 0-100, weighted total 0-100 |
If any check fails, fix it before proceeding.
Output Format
Generate a file called BRAND-INTELLIGENCE.md:
# Brand Intelligence Report: [Brand Name]
**Analysis Date:** [Date]
**Brand:** [Brand Name]
**Domain:** [URL]
**Industry:** [Industry]
---
## Executive Summary
[3-5 sentences summarizing the brand's AI visibility standing, overall sentiment direction with percentages, top topics, source count, and the single most impactful action to take.]
---
## Brand Authority Score: [X]/100 ([Rating])
### Platform Breakdown
| Platform | Score | Weight | Weighted | Status |
|---|---|---|---|---|
| YouTube | [X]/100 | 25% | [X] | [Active Channel / Mentioned / Absent] |
| Reddit | [X]/100 | 25% | [X] | [Active / Discussed / Absent] |
| Wikipedia | [X]/100 | 20% | [X] | [Article / Mentioned / Absent] |
| LinkedIn | [X]/100 | 15% | [X] | [Active / Basic / Absent] |
| Other Platforms | [X]/100 | 15% | [X] | [Summary] |
| **Total** | | | **[X]/100** | |
---
## Sentiment Overview
### Overall Sentiment: [X]% Positive / [X]% Neutral / [X]% Negative
### Per-Source Sentiment
| Source | Positive | Neutral | Negative | Items | Confidence |
|---|---|---|---|---|---|
| Reddit | [X]% | [X]% | [X]% | [N] | [High/Medium/Low] |
| YouTube | [X]% | [X]% | [X]% | [N] | [High/Medium/Low] |
| News | [X]% | [X]% | [X]% | [N] | [High/Medium/Low] |
| Reviews | [X]% | [X]% | [X]% | [N] | [High/Medium/Low] |
| Forums | [X]% | [X]% | [X]% | [N] | [High/Medium/Low] |
| LinkedIn | [X]% | [X]% | [X]% | [N] | [High/Medium/Low] |
### Dominant Emotions
[Top 3 emotions with percentages, e.g.: Trust (28%), Excitement (22%), Frustration (18%)]
### Emotion Profile
| Emotion | Percentage |
|---|---|
| Trust | [X]% |
| Frustration | [X]% |
| Excitement | [X]% |
| Disappointment | [X]% |
| Concern | [X]% |
| [Dynamic 1] | [X]% |
| [Dynamic 2] | [X]% |
| **Total** | **100%** |
---
## Themes and Topics
### Theme 1: [Theme Name]
#### Topic: [Specific Topic Name]
- **Sentiment:** [X]% positive / [X]% neutral / [X]% negative
- **Dominant emotions:** [emotion 1], [emotion 2]
- **Source lean:** Reddit: [positive/negative/neutral/mixed] | News: [lean] | ...
- **Analysis:** [4-6 sentence analytical explanation for major topics, 2-3 for minor]
- **Quotes:**
> "[Verbatim quote from source]" -- [source_label] ([URL])
> "[Verbatim quote from source]" -- [source_label] ([URL])
- **Sarcasm note:** [Only if applicable, otherwise omit]
[Repeat for each topic in theme]
### Theme 2: [Theme Name]
[Repeat structure]
---
## Platform Detail
### YouTube ([X]/100)
**Official Channel:** [Yes/No] | [URL if exists]
**Subscribers:** [Count or N/A]
**Videos:** [Count or N/A]
**Last Upload:** [Date or N/A]
**Third-Party Mentions:** [Estimated count]
**Sentiment:** [X]% positive / [X]% neutral / [X]% negative
**Key Findings:**
- [Finding 1]
- [Finding 2]
### Reddit ([X]/100)
**Official Account:** [Yes/No] | [URL if exists]
**Own Subreddit:** [Yes/No] | [URL and member count if exists]
**Mention Volume:** [Estimated thread count]
**Primary Subreddits:** [List of subreddits where brand is discussed]
**Sentiment:** [X]% positive / [X]% neutral / [X]% negative
**Key Findings:**
- [Finding 1]
- [Finding 2]
### Wikipedia ([X]/100)
**Company Article:** [Yes/No] | [URL if exists]
**Founder Article:** [Yes/No] | [URL if exists]
**Wikidata Entry:** [Yes/No] | [Q-number if exists]
**Cited in Other Articles:** [Yes/No] | [Which articles]
**Key Findings:**
- [Finding 1]
- [Finding 2]
### LinkedIn ([X]/100)
**Company Page:** [Yes/No] | [URL if exists]
**Followers:** [Count or N/A]
**Post Frequency:** [Weekly/Monthly/Rare/Never]
**Sentiment:** [X]% positive / [X]% neutral / [X]% negative
**Key Findings:**
- [Finding 1]
- [Finding 2]
### News and Press ([X]/100)
**Recent Coverage:** [Yes/No] | [Count of articles in last 6 months]
**Notable Outlets:** [List]
**Sentiment:** [X]% positive / [X]% neutral / [X]% negative
**Key Findings:**
- [Finding 1]
- [Finding 2]
### Other Platforms ([X]/100)
| Platform | Presence | Sentiment Lean | Notes |
|---|---|---|---|
| Quora | [Yes/No] | [Positive/Neutral/Negative/Mixed] | [Brief note] |
| Stack Overflow | [Yes/No] | [Lean] | [Brief note] |
| GitHub | [Yes/No] | [Lean] | [Brief note] |
| Hacker News | [Yes/No] | [Lean] | [Brief note] |
| Trustpilot | [Yes/No] | [Lean] | [Rating if available] |
| Podcasts | [Yes/No] | [Lean] | [Brief note] |
---
## Recommendations
### Immediate Actions (Week 1-2)
1. **[Platform]:** [Specific action to take with expected impact]
2. **[Platform]:** [Specific action]
### Short-Term Strategy (Month 1-3)
1. **[Platform]:** [Strategy with tactics]
2. **[Platform]:** [Strategy with tactics]
### Long-Term Authority Building (Month 3-12)
1. **[Platform]:** [Long-term strategy]
2. **[Platform]:** [Long-term strategy]
### Sentiment-Specific Actions
1. **[Negative topic]:** [How to address the negative sentiment]
2. **[Positive topic]:** [How to amplify the positive sentiment]
---
## Competitive Context
[If competitors were analyzed, show a brief comparison table]
| Brand | Authority Score | YouTube | Reddit | Wikipedia | LinkedIn | Other | Overall Sentiment |
|---|---|---|---|---|---|---|---|
| [Subject Brand] | **[X]** | [X] | [X] | [X] | [X] | [X] | [X]% pos / [X]% neg |
| [Competitor 1] | **[X]** | [X] | [X] | [X] | [X] | [X] | [X]% pos / [X]% neg |
| [Competitor 2] | **[X]** | [X] | [X] | [X] | [X] | [X] | [X]% pos / [X]% neg |
---
## Key Takeaway
[1-2 sentence summary of the brand's AI visibility standing, sentiment position, and the single most impactful action to take.]
Reference Data
Correlation Strengths (Ahrefs Dec 2025, 75K Brands)
| Signal | Correlation with AI Citation | Traditional SEO Value |
|---|
| YouTube mentions | ~0.737 | Low (not a ranking factor) |
| Reddit mentions | High (exact coefficient not published) | Low |
| Wikipedia presence | High | Moderate (trust signal) |
| LinkedIn presence | Moderate | Low |
| Domain Rating | ~0.266 | Very High |
| Backlink count | ~0.266 | Very High |
| Organic traffic | Moderate | Very High |
Key insight: The signals that matter most for AI visibility (YouTube, Reddit) are almost irrelevant in traditional SEO, and the signals that matter most for traditional SEO (backlinks, DR) are weak predictors of AI visibility. This requires a fundamentally different optimization strategy.
Platform-Specific Tips for Building Presence
YouTube Quick Wins:
- Create a channel and upload 3-5 explainer videos about your core topics.
- Ensure your brand name appears in video titles, descriptions, and spoken content.
- Pursue guest appearances on relevant industry YouTube channels.
- Create comparison or "alternatives" videos (these get cited by AI for comparison queries).
Reddit Quick Wins:
- Identify 3-5 subreddits where your target audience is active.
- Participate authentically (do not shill -- Reddit communities detect and punish this).
- Do an AMA if appropriate for your brand.
- Monitor and respond to mentions of your brand.
- Create genuinely helpful posts that naturally mention your brand's expertise.
Wikipedia Strategy:
- Hire a Wikipedia-knowledgeable consultant -- do NOT edit your own article (conflict of interest).
- Build notability through press coverage, academic citations, and industry recognition first.
- Ensure your Wikidata entry is complete even if you do not have a Wikipedia article.
- Contribute to industry-relevant articles where your brand can be naturally cited as a source.
LinkedIn Quick Wins:
- Optimize your company page with complete information and regular posting.
- Encourage leadership to post thought leadership content weekly.
- Publish LinkedIn articles on topics where your brand has unique expertise.
- Engage with industry discussions to increase brand visibility in professional contexts.
Sentiment Improvement Tactics:
- Monitor negative topics and create response strategies for each.
- Amplify positive stories by resharing and engaging with positive mentions.
- Address recurring complaints with visible product/service improvements and communicate changes publicly.
- Encourage satisfied customers to share their experiences on Reddit and review sites.
- For sarcasm-heavy platforms (Reddit), engage with humor and authenticity rather than corporate tone.
