Being mentioned by AI isn't enough. How AI describes you shapes customer perception, influences purchase decisions, and determines whether you win or lose against competitors. A brand described as "the affordable option" attracts different customers than one described as "the premium leader"—and these narratives are being written by machines.
🎯 The Core Insight
Narrative & Attribute Intelligence analyzes not just whether AI mentions your brand, but what it says about you. This is the difference between visibility and meaning—and it's the third pillar of AI Acquisition Intelligence.
1. Why Narrative Matters
Consider these two AI responses to the same query:
"Brand A is widely recognized as the industry leader in sustainable footwear, with innovative materials and award-winning customer service. Their products consistently receive top ratings from independent reviewers."
"Brand B offers affordable shoes made from recycled materials. While not as premium as some competitors, they provide good value for budget-conscious consumers."
Both brands are mentioned. Both are visible. But the narrative gap is enormous. Brand A is positioned for premium customers willing to pay more. Brand B is positioned for price-sensitive shoppers—and will capture very different revenue as a result.
Narrative Intelligence gives you the tools to measure and shape what AI says about you.
2. The Three Dimensions of Narrative Intelligence
Dimension 1: Sentiment Polarity
Sentiment Polarity measures whether AI describes you positively, neutrally, or negatively on a scale from -1.0 to +1.0.
Examples:
- +0.8 to +1.0: "Industry leader," "award-winning," "best-in-class," "exceptional quality"
- +0.3 to +0.7: "Good option," "reliable choice," "solid performance"
- -0.3 to +0.2: "Average," "adequate," "comparable to others" (neutral to mildly positive)
- -0.7 to -0.3: "Some issues," "mixed reviews," "not recommended for"
- -1.0 to -0.7: "Poor quality," "customer complaints," "avoid"
📊 Sentiment Polarity Benchmarks
Industry average: +0.42
Top performers: +0.70+
Needs improvement: < +0.30
Dimension 2: Attribute Association
Attribute Association identifies the specific qualities and characteristics AI connects to your brand.
Common Attribute Categories:
Example: Rail Operator Attributes
Dimension 3: Positioning
Positioning describes how AI frames your brand relative to competitors—the role you play in the market narrative.
Common Positioning Frames:
- The Premium Leader: "The most luxurious option," "high-end choice for discerning customers"
- The Affordable Option: "Budget-friendly alternative," "great value for money"
- The Innovator: "Cutting-edge technology," "revolutionary approach"
- The Established Authority: "Trusted for over 50 years," "industry standard"
- The Specialist: "Specializes in [niche]," "expert in [specific area]"
- The All-Rounder: "Good balance of price and quality," "versatile option"
Your positioning determines which customers you attract and how you compete. A brand positioned as "premium" can command higher prices; a brand positioned as "affordable" competes on price.
3. Platform-Specific Narrative Differences
Different AI platforms describe brands differently. The University of Toronto research (Chen et al., 2025) found systematic variations in how platforms frame brands.
| Platform | Narrative Style | Attribute Focus | Example Description |
|---|---|---|---|
| ChatGPT | Conversational, detailed | Customer experience, storytelling | "LNER's Azuma trains offer a comfortable journey with excellent onboard service..." |
| Gemini | Factual, precise | Specifications, data, comparisons | "LNER operates Azuma trains on the East Coast Main Line with journey times of 4h 20m..." |
| Claude | Balanced, ethical | Sustainability, responsibility, fairness | "LNER has implemented several sustainability initiatives and offers accessible travel options..." |
| Copilot | Action-oriented | Booking, pricing, commercial | "You can book LNER tickets from £42 for the London to Edinburgh route..." |
| Perplexity | Citation-heavy | Sources, reviews, third-party | "According to Trustpilot reviews, LNER has a 4.2/5 rating from 15,000+ customers..." |
📚 Research Foundation
The Toronto research (Chen et al., 2025) found that earned media (third-party reviews) is preferred 3.2x over brand-owned content. This directly impacts narrative—your customers' words carry more weight than your own claims in shaping AI descriptions.
4. Measuring Narrative Intelligence
Narrative Scorecard
Brand Narrative Analysis Template
| Overall Sentiment: | +0.68 |
| Top Positive Attributes: | innovative (78%), reliable (72%), customer-focused (65%) |
| Top Neutral Attributes: | established (45%), popular (38%) |
| Top Negative Attributes: | expensive (12%), complicated (8%) |
| Primary Positioning: | Premium Leader (62% of mentions) |
| Secondary Positioning: | Innovator (28% of mentions) |
| Narrative Consistency: | 87% (highly consistent) |
Key Metrics
📊 Sentiment Polarity Score
Average sentiment across all AI mentions. Tracked overall and by platform.
Formula: Σ (Mention Sentiment) / Total Mentions
Target: > +0.50 overall, > +0.60 on decision-stage queries
📊 Attribute Strength Index
Percentage of mentions that include your desired attributes.
Example: If "innovative" appears in 78% of mentions, Attribute Strength = 78%
Target: > 60% for priority attributes
📊 Positioning Share
Percentage of mentions that frame you in each positioning category.
Target: Primary positioning > 50%, secondary positioning > 20%
📊 Narrative Consistency Index
How consistently your narrative appears across platforms and over time.
Formula: 100 - (Standard Deviation of Sentiment × 50)
Target: > 80 (high consistency)
5. Case Study: Rail Operator Narrative Analysis
LNER (Hypothetical Narrative Analysis)
Baseline Narrative (Pre-Optimization):
| Overall Sentiment: | +0.38 |
| Top Attributes: | busy (45%), expensive (32%), reliable (28%) |
| Primary Positioning: | The Busy Option (40%) |
| Secondary Positioning: | The Expensive Option (32%) |
Issues Identified:
- Negative attribute "expensive" appearing too frequently
- Positive attributes like "comfortable" and "modern" rarely mentioned
- Positioning focused on price rather than value or experience
- Platform inconsistencies: ChatGPT described experience well, but Gemini focused only on price
Optimization Actions:
- Created content highlighting the Azuma train experience (comfort, speed, amenities)
- Amplified customer reviews mentioning positive experiences
- Added schema markup for awards and recognition
- Developed comparison content showing value rather than just price
- Platform-specific narrative reinforcement
Results After 6 Months:
| Overall Sentiment: | +0.67 (↑ 0.29) |
| Top Attributes: | comfortable (62%), modern (58%), reliable (52%), value (45%) |
| Primary Positioning: | The Premium Experience (58%) |
| Secondary Positioning: | The Reliable Choice (32%) |
| "Expensive" Mentions: | ↓ from 32% to 12% |
Business Impact: 18% increase in first-class bookings, 12% increase in average ticket value.
6. Controlling Your AI Narrative
You can't directly control what AI says about you—but you can influence it through strategic actions:
Strategy 1: Amplify Positive Third-Party Voices
AI trusts earned media 3.2x more than brand claims. Leverage this by:
- Prominently featuring positive reviews with schema markup
- Highlighting awards and recognition from authoritative sources
- Encouraging customer reviews on platforms AI trusts (Trustpilot, Google Reviews)
- Quoting media mentions and expert opinions
Strategy 2: Create Narrative-Rich Content
Structure content to reinforce your desired narrative:
- Use your desired attributes consistently in headings and key phrases
- Tell stories that embody your brand values (AI extracts these for ChatGPT)
- Include specific examples of customer experiences
- Use comparison tables that highlight your strengths
Strategy 3: Address Negative Narratives Directly
If AI associates negative attributes with your brand:
- Acknowledge the issue transparently (builds trust)
- Provide counter-evidence and context
- Highlight improvements and resolutions
- Amplify positive experiences that contradict negative narratives
Strategy 4: Platform-Specific Narrative Optimization
- For ChatGPT: Create rich, story-driven content about customer experiences
- For Gemini: Use structured data to reinforce factual attributes
- For Claude: Emphasize ethical practices and balanced perspectives
- For Copilot: Highlight value propositions and decision factors
- For Perplexity: Build citation density with positive third-party sources
7. Integrating with the Five Pillars
Narrative & Attribute Intelligence is Pillar 3 of the Five Pillars framework. It integrates with:
- Pillar 1 (Cross-Model Visibility): Compare narratives across platforms to identify inconsistencies
- Pillar 2 (Intent-Weighted Influence): Focus narrative optimization on decision-stage queries first
- Pillar 4 (Stability): Ensure your narrative is consistent over time, not volatile
- Pillar 5 (Prescriptive Optimization): Use narrative insights to guide content strategy
🎯 Key Takeaway
Visibility gets you mentioned. Narrative gets you chosen. Narrative & Attribute Intelligence ensures that when AI describes your brand, it describes you the way you want to be seen.
Ready to understand your AI narrative?
Get a free Narrative Intelligence analysis showing how AI describes your brand across ChatGPT, Gemini, Claude, and more.
Get Free AI Profile →References
- Chen, M., Wang, X., Chen, K., & Koudas, N. (2025). "Generative Engine Optimization: How to Dominate AI Search." arXiv preprint arXiv:2509.08919. arXiv:2509.08919
- Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., & Deshpande, A. (2024). "GEO: Generative Engine Optimization." Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. arXiv:2311.09735
- Halavachova, Y. (2026). "Narrative & Attribute Intelligence in AI Acquisition." UltraScout AI Research.
- Trustpilot. (2026). "The Impact of Reviews on AI Citations." Trustpilot Data Science.