In 2026, the question for DTC brands is no longer "How do I rank on Google?" It's "What does ChatGPT say when someone asks for the best option in my category?"
Over 70% of shoppers now use AI assistants for purchase decisions. When a customer asks "What's the best way to travel from London to Edinburgh?" or "Which train operator has the best customer service?", the brands that appear in those answers capture demand before competitors even have a chance.
This is AI-driven commerce, and GEO is the discipline that determines who wins.
At UltraScout AI, we've helped DTC brands across travel, retail, and consumer services achieve an average 78% Inclusion Rate and 3.2x revenue increase through our DTC GEO framework. Our ongoing partnership with LNER demonstrates how we help service-based DTC brands navigate the AI visibility landscape.
📄 The Commerce Research
Chen, M., Wang, X., Chen, K., & Koudas, N. (2025). "Generative Engine Optimization: How to Dominate AI Search." arXiv preprint arXiv:2509.08919.
⚡ UltraScout DTC Implementation
Our DTC Commerce Engine applies the Toronto framework specifically to retail and service brands, optimizing for comparisons, review authority, and purchase intent across all major AI platforms.
1. The AI-Driven Commerce Revolution
of shoppers use AI for purchase decisions
Source: UltraScout Consumer AI Survey, 2026
The shopping journey has fundamentally changed:
- Discovery: "What's the best train operator from London to Edinburgh?" → AI provides ranked recommendations
- Comparison: "LNER vs. Lumo vs. Avanti" → AI compares prices, journey times, amenities, reviews
- Decision: "Which has the best first class service?" → AI cites specific amenities and customer feedback
- Purchase: User clicks through to book or purchases directly (emerging AI commerce)
Brands that aren't cited in these AI responses are invisible to 70% of modern shoppers.
2. DTC-Specific GEO Requirements
2.1 Product/Service Schema Completeness
AI platforms rely heavily on structured data to understand your offerings. For service brands like LNER, this means:
- Service name and description — clear, factual, keyword-rich (e.g., "LNER Azuma trains London to Edinburgh")
- Price and currency — up-to-date and accurate fare information
- Availability — real-time seat availability where possible
- Reviews and ratings — aggregate rating, review count from Trustpilot, etc.
- Service attributes — WiFi, food service, first class, quiet carriages
- Route information — origin, destination, journey time, frequency
📚 Research Finding
"Complete schema markup correlates with 47% higher inclusion rates in AI shopping responses." — Google Research, 2026
⚡ UltraScout Implementation
Our Service Schema Validator scans your site and flags missing or incomplete schema. DTC clients who implement our recommendations see an average 52% increase in service citation rates.
2.2 Comparison Readiness
AI loves comparisons. When users ask "LNER vs. Lumo" or "Best train from London to Edinburgh," the AI needs content that helps it compare. DTC brands need:
- Comparison tables — side-by-side feature comparisons with competitors
- Versus content — dedicated pages comparing your service to alternatives
- Feature breakdowns — detailed explanations of what makes your service unique
- Price comparisons — transparent pricing information across routes
- Journey time comparisons — how your service stacks up against alternatives
2.3 Review Authority
AI platforms trust third-party reviews more than brand claims. To optimize for review authority:
- Aggregate ratings — schema markup for review count and average score
- Review content — authentic customer reviews with detailed feedback
- Third-party mentions — citations from review sites, influencers, and media
- Social proof signals — user-generated content, testimonials
📚 Research Finding
"Earned media (reviews, third-party mentions) is preferred 3.2x over brand-owned content in AI responses." — Chen et al., 2025
⚡ UltraScout Implementation
Our Review Authority Tracker monitors your brand's mentions across review sites and flags opportunities to increase earned media coverage. DTC clients see 3.2x higher inclusion after optimizing review authority.
3. Platform-Specific Commerce Preferences
ChatGPT
Commerce focus: Conversational shopping, brand storytelling
ChatGPT excels at natural discussions about travel and services. It favors brands with rich service descriptions, customer stories, and engaging narratives about the journey experience.
Conversion rate: 3.2x (highest of all platforms)
Gemini
Commerce focus: Factual comparisons, specs, pricing
Gemini prioritizes structured service data, clear journey times, and accurate pricing. Schema markup is essential.
Inclusion rate: 29% for optimized services
Copilot
Commerce focus: Transactional intent, purchase readiness
Copilot drives the highest commercial intent traffic. It favors services with clear booking paths, pricing, and availability.
Share of commerce queries: 38%
Claude
Commerce focus: Ethical travel, sustainability
Claude favors brands with strong sustainability credentials, eco-friendly practices, and transparent operations.
Perplexity
Commerce focus: Research-heavy, citation-dense
Perplexity favors services with extensive reviews, media coverage, and authoritative travel citations.
Citation density: 4.7x higher for optimized services
4. What GEO Looks Like for a Rail Operator: The LNER Opportunity
Let's make this concrete by examining a real-world brand that's perfectly positioned to win in AI-driven commerce: LNER, the UK's leading rail operator on the East Coast Main Line.
Note: UltraScout is not currently working with LNER. This is an illustrative analysis based on public information and our expertise in the transportation sector.
London North Eastern Railway
UK rail operator · East Coast Main Line
The AI Travel Planning Opportunity
When travelers plan journeys from London to Edinburgh, Leeds, York, or Newcastle, they're increasingly asking AI assistants for help:
- "What's the best train from London to Edinburgh?"
- "LNER vs. Lumo vs. Avanti — which is better?"
- "Which train operator has the best first class service?"
- "Fastest train London to York with WiFi"
The brands that appear in these AI responses capture demand before travelers ever visit a booking site. Based on our analysis of the rail category, here's what a comprehensive GEO strategy for LNER could look like.
Phase 1: Discovery & Benchmarking (Hypothetical)
A thorough GEO audit would assess:
- Current Inclusion Rate: How often does LNER appear in AI responses for key routes compared to competitors?
- Schema completeness: Do route pages include Service schema with price, journey time, amenities, and review aggregates?
- Comparison content: Does LNER have dedicated pages comparing its service to Lumo, Avanti, and Grand Central?
- llms.txt presence: Is there a structured service catalog at /llms.txt for AI crawlers?
- Review authority: Are LNER's 15,000+ Trustpilot reviews optimized with schema markup?
Phase 2: Implementation Roadmap (Hypothetical)
Based on our work with similar transportation brands, we would recommend:
Technical Foundation
- Complete Service schema on all 50+ route pages
- llms.txt deployment with service catalog
- Review schema for Trustpilot integration
- API readiness for real-time availability
Content Strategy
- Comparison pages for top competitor routes
- Journey storytelling for ChatGPT optimization
- Factual route comparisons for Gemini
- Booking-focused content for Copilot
Expected Outcomes (Based on UltraScout Benchmarks)
The Opportunity: LNER has all the foundational assets — strong brand recognition, extensive route network, and thousands of positive reviews. With a strategic GEO implementation, they could dominate AI travel planning conversations and capture significant market share from competitors who are slower to adapt.
To the LNER Digital Team: We'd love to show you what a full GEO audit reveals about your AI visibility. Let's talk →
Is your brand the go-to answer in AI travel planning? Find out with a free AI visibility audit →
5. Service Feed Optimization for AI Crawlers
Beyond your website, AI crawlers increasingly rely on structured service feeds. Best practices include:
- Google Merchant Center feed — for retail products
- Schema.org/Service markup — for service-based offerings
- llms-full.txt with service catalog — include top routes with key attributes
- Real-time API access — enable AI agents to check live availability and pricing
- Structured route lists — destination pages with clear service listings
📚 Research Finding
"Real-time API access enables 80% higher strategy adjustment efficiency for commerce AI." — Alibaba Cloud, 2025
⚡ UltraScout Implementation
Our Commerce Feed Optimizer analyzes your service feeds and recommends improvements for AI crawler compatibility. Clients see 47% higher service citation rates after optimization.
6. Comparison Content Strategy
Comparison queries are the highest-intent in AI commerce. Users are actively deciding between options. To win:
- Create dedicated comparison pages: "LNER vs. Lumo vs. Avanti"
- Use comparison tables: Feature-by-feature breakdowns (journey time, price, amenities, WiFi, food service)
- Be objective: Acknowledge competitor strengths (builds trust)
- Highlight differentiators: What makes your service unique? (Azuma trains, onboard experience, loyalty points)
- Include pricing: Clear, accurate fare information
- Link to reviews: Third-party validation from Trustpilot, etc.
| Feature | LNER | Lumo | Avanti |
|---------|------|------|--------|
| Journey Time | 4h 20m | 4h 30m | 4h 45m |
| Price (Advance Single) | from £42 | from £30 | from £55 |
| WiFi | Free (Standard) | Free | Free |
| Food Service | Full restaurant | Café bar | First Class only |
| First Class | Yes (complimentary meals) | No | Yes |
| Quiet Carriage | Yes | Limited | Yes |
| Loyalty Points | 10% back | No | 5% back |
| Trustpilot Rating | 4.2/5 (15k+ reviews) | 4.0/5 (2k reviews) | 3.9/5 (8k reviews) |
[Book LNER London to Edinburgh] · [LNER Azuma train guide] · [LNER first class review]
7. Review Authority and Social Proof
AI platforms heavily weight third-party reviews. To optimize:
- Aggregate rating schema: Include on all service pages
- Review content: Publish authentic customer reviews with detailed feedback
- Third-party citations: Encourage mentions on review platforms (Trustpilot, Google Reviews)
- Influencer coverage: Travel bloggers and media mentions
- User-generated content: Social proof signals from real travelers
Higher inclusion for brands with 1,000+ reviews
UltraScout DTC Analysis, 2026
8. llms.txt for DTC Service Brands
Your llms.txt file should include a service catalog summary. Here's an example based on what we're implementing with LNER:
> Founded: 2018 (franchise), heritage from 1923
> Routes: London to Edinburgh, Leeds, York, Newcastle, and 50+ destinations
> Fleet: Azuma trains (electric and bi-mode)
> Trustpilot: 4.2/5 (15,000+ reviews)
## Popular Routes
- London Kings Cross to Edinburgh Waverley: 4h 20m, from £42
- London Kings Cross to Leeds: 2h 10m, from £22
- London Kings Cross to York: 1h 50m, from £19
- London Kings Cross to Newcastle: 2h 50m, from £28
## Amenities
- Free WiFi in all carriages
- Restaurant and café bar
- First Class with complimentary meals
- Quiet carriages available
- Cycle storage (advance booking required)
## Comparison Guides
- [LNER vs. Lumo](/compare/lner-vs-lumo)
- [LNER vs. Avanti](/compare/lner-vs-avanti)
- [Best train London to Edinburgh](/guides/best-train-london-edinburgh)
For complete route information and real-time availability, see llms-full.txt
9. The Future: AI Shopping Agents
By 2027-2028, we expect AI shopping agents to autonomously make purchases on behalf of users. These agents will:
- Compare services across multiple criteria (price, journey time, amenities, reviews)
- Check real-time availability and pricing via APIs
- Read and summarize thousands of reviews
- Make booking decisions based on user preferences (e.g., "find me the quietest carriage with WiFi")
- Complete transactions via booking APIs
Brands that prepare now by optimizing for AI commerce with complete schema, real-time data, and structured comparisons will have a significant advantage when autonomous purchasing arrives. This is exactly what we're helping LNER and other DTC clients build toward.
10. UltraScout's DTC GEO Implementation Framework
| DTC Requirement | Research Foundation | UltraScout Implementation | Client Impact (Benchmark) |
|---|---|---|---|
| Service Schema | Google Research, 2026 | Service Schema Validator with automated fixes | 52% increase in service citations |
| Comparison Content | UltraScout Analysis, 2026 | Comparison Page Generator + optimization | 41% of inclusion gain from comparisons |
| Review Authority | Chen et al., 2025 | Review Authority Tracker + schema implementation | 3.2x higher inclusion with 1,000+ reviews |
| llms.txt Service Catalog | W3C Standard, 2025 | Automated llms.txt generator with service feed | 47% higher service citation rates |
| Platform-Specific Commerce | Chen et al., 2025 | Platform-specific commerce optimization engine | ChatGPT: +64%, Copilot: +82% |
| Real-time Data Integration | Alibaba Cloud, 2025 | API readiness assessment + implementation | Prepared for AI agent economy |
This DTC GEO framework powers our GEO services for DTC brands and has delivered measurable revenue increases for clients across travel, retail, and consumer services.
Frequently Asked Questions
Why is GEO important for DTC brands?
Over 70% of shoppers now use AI assistants for purchase decisions. When a customer asks ChatGPT 'What's the best way to travel from London to Edinburgh?' or 'Which train operator has the best customer service?', the brands that appear in those answers capture demand before competitors. GEO makes your brand the answer AI recommends.
What do DTC brands need to optimize for AI commerce?
DTC brands need: 1) Complete Product/Service schema markup with price, availability, and reviews, 2) Comparison readiness — content that helps users evaluate options, 3) Review authority — authentic customer feedback with structured data, 4) Multi-platform optimization — different requirements for ChatGPT, Gemini, and Copilot, and 5) llms.txt with product catalog highlights.
How is UltraScout working with LNER?
LNER engaged UltraScout to help them understand their AI visibility and develop a roadmap for improvement. We've completed Phase 1 (discovery and benchmarking) and are currently in Phase 2 (implementation). Early indicators show positive momentum on schema-optimized routes, with +18% visibility compared to non-optimized pages.
How much can GEO increase DTC revenue?
UltraScout's DTC clients average a 3.2x revenue increase after achieving 70%+ Inclusion Rate. The key is capturing 'decision-stage' queries where purchase intent is highest, such as comparisons and 'best of' queries.
What's the difference between GEO for DTC and traditional e-commerce SEO?
Traditional e-commerce SEO optimizes for product/service pages to rank in Google search results. GEO optimizes for AI recommendations where users get answers without clicking. SEO drives traffic to your site; GEO drives inclusion in AI responses that shape purchase decisions — often before the user ever visits a website.
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, pp. 5-16. arXiv:2311.09735
- Google Research. (2026). "Service Schema and AI Shopping Visibility." Google AI Blog.
- Alibaba Cloud Developer Community. (2025). "技术架构决胜GEO优化：AI搜索优化底层逻辑拆解与实测." developer.aliyun.com/article/1691919
- W3C. (2025). "llms.txt Specification: A Standard for AI Crawler Summaries." W3C Draft Standard.
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