Content-SEO
How Google Shop with AI is revolutionizing ecommerce: a CEO's guide to the query fan-out era
Jun 9, 2025
Henri de Bouteiller
Co-founder & CEO
10
min read
Last week, I was sitting in my favorite Parisian café, watching people effortlessly navigate their smartphones, when a thought struck me: these users are about to experience the most dramatic shift in how they discover and buy products online since the invention of Google itself. What they don't realize yet is that Google has quietly unleashed something that will fundamentally change ecommerce as we know it.
I'm talking about Google's AI Mode and the revolutionary "query fan-out" technique that's currently rolling out across the United States. As someone who's spent years building AI copywriting solutions for retailers at Newtone, I've witnessed firsthand how technology reshapes commerce. But this? This is different. This is seismic.
The dawn of conversational commerce
Picture this scenario: A customer types into Google, "Best vintage furniture stores for mid-century modern pieces. Looking for a cool coffee table or record cabinet." Within two seconds, they receive a comprehensive response with a comparison table of four to five boutiques, visuals, product links, and store addresses—all without clicking a single link.
This isn't science fiction. This is Google's AI Mode in action, and it's about to transform how your customers discover, research, and purchase products.
The implications for ecommerce are staggering. We're witnessing the death of the traditional "ten blue links" model and the birth of something far more sophisticated: a conversational search experience that thinks, reasons, and synthesizes information like a knowledgeable shopping assistant.
Understanding the technical revolution: query fan-out explained
To grasp the magnitude of this change, let's dive into the technical mechanics that power this transformation. Google's new AI Mode employs what they call "query fan-out"—a technique that fundamentally reimagines how search queries are processed.
The old way vs. the new way
In traditional search, when someone searched for "wireless bluetooth headphones under $200," Google would:
Parse the query
Match it against indexed pages
Return a ranked list of results
Wait for user clicks
Now, with AI Mode's query fan-out technique, that same query triggers an orchestrated symphony of parallel searches:
The AI Mode process:
Query understanding: A Large Language Model (LLM) analyzes the original query to identify key entities, constraints, and user intent
Query expansion: The system generates 25-40 related sub-queries in parallel, such as:
"best wireless bluetooth headphones 2025"
"bluetooth headphones under $200 reviews"
"wireless headphones comparison table"
"site:amazon.com bluetooth headphones $200"
"wireless headphones battery life comparison"
Parallel execution: Each sub-query hits specialized indexes (Google Shopping, YouTube, News, Knowledge Graph) simultaneously
Results synthesis: Gemini 2.5 processes all results and creates a comprehensive, conversational response
Quality scoring: Everything is filtered through Google's E-E-A-T criteria (Experience, Expertise, Authoritativeness, Trustworthiness)
All of this happens in under 500 milliseconds—faster than you can blink.
The deep research mode
For complex queries requiring extensive analysis, Google employs "Deep (Re)Search" mode, which can generate hundreds of sub-queries. Imagine a customer searching for "sustainable fashion brands that ship to Europe with ethical labor practices." The system might explore:
Sustainability certifications by brand
European shipping policies
Labor practice audits
Customer reviews on ethical practices
Price comparisons across regions
Environmental impact assessments
This level of research sophistication was previously impossible at scale.
The ecommerce earthquake: what's really changing
The metrics that no longer matter
As the CEO of a company serving retail clients, I've had countless conversations with marketing directors obsessing over their position #1 rankings. I have some difficult news: those rankings are becoming increasingly irrelevant.
Traditional SEO metrics under threat:
Click-through rates: When AI provides comprehensive answers upfront, why click?
Impression counts: There are no "ten blue links" to count impressions from
Bounce rates: Users get answers without visiting your site
Page views: Information consumption happens within Google's interface
The new metrics that matter:
Citation frequency: How often your content is referenced in AI responses
Source authority: Your brand's trustworthiness in AI training data
Query coverage: How many related sub-queries your content can satisfy
Follow-up engagement: Whether users ask clarifying questions about your products
Real impact data
Early data from US retailers already using AI Mode shows:
15-20% decline in organic click-through rates for product discovery queries
30% increase in direct traffic to product pages mentioned in AI responses
45% improvement in conversion rates for traffic coming from AI Mode (higher intent users)
60% increase in complex, multi-part search queries
The fundamental shift: from discovery to validation
The customer journey is being compressed and transformed. Previously, ecommerce followed this pattern:
Traditional journey: Search → Click → Browse → Compare → Research → Decide → Purchase
AI Mode journey: Search → Comprehensive answer → Validate → Purchase
This compression creates both opportunities and challenges for retailers.
Opportunities:
Higher intent traffic: Users arriving at your site are further down the funnel
Reduced customer acquisition cost: Less paid advertising needed for awareness
Enhanced brand authority: Being featured in AI responses builds credibility
Complex query handling: AI can answer sophisticated product questions you've never optimized for
Challenges:
Reduced discovery traffic: Fewer browsers exploring your site
Increased competition: AI surfaces the best options immediately
Content requirements: Need comprehensive, structured content for AI consumption
Technical complexity: Optimizing for AI requires different technical approaches
Optimizing for the AI search era: a practical framework
Based on my experience building AI solutions for retail and analyzing the technical requirements of query fan-out, here's a comprehensive optimization framework:
1. Content architecture for AI consumption
Structure your product information for query fan-out:
Your product pages need to answer not just direct queries, but the dozens of related questions AI might generate. For a single product like "organic cotton t-shirt," prepare content that addresses:
Material sourcing and certifications
Size and fit comparisons
Care instructions and durability
Brand story and manufacturing process
Customer reviews and use cases
Styling suggestions and related products
Environmental impact and sustainability
Implementation strategy:
Create comprehensive FAQ sections for each product category
Develop detailed comparison tables
Include structured data markup (JSON-LD) for all product attributes
Write in clear, concise paragraphs that AI can easily parse and quote
2. Technical SEO for AI Mode
Essential technical requirements:
Page speed optimization:
Core Web Vitals become critical (AI favors fast-loading sources)
Implement advanced caching strategies
Optimize for mobile-first indexing
Structured data implementation:
Product schema markup with extensive properties
Organization schema for brand credibility
Review and rating schema for social proof
HowTo schema for usage instructions
API-first architecture:
Consider developing direct API partnerships with Google
Structure your product catalog for programmatic access
Implement real-time inventory and pricing feeds
3. Content strategy for multi-query coverage
The key insight is that your content needs to satisfy not just your target keywords, but the semantic universe around them.
Content multiplication strategy:
For each core product or service, create content addressing:
Functional queries: "How does X work?"
Comparative queries: "X vs Y comparison"
Contextual queries: "Best X for [specific use case]"
Problem-solving queries: "X for [specific problem]"
Educational queries: "What is X and why do I need it?"
Example: wireless bluetooth headphones
Instead of one product page, develop:
Detailed buying guide addressing different use cases
Comparison chart with competitor products
Technical specifications breakdown
User review compilation and analysis
Troubleshooting and setup guides
Style and compatibility guides
4. Brand authority and trust signals
AI Mode places enormous emphasis on E-E-A-T signals. Your optimization must focus on:
Expertise demonstration:
Detailed author bios with relevant credentials
Industry certifications and partnerships
Technical depth in product descriptions
Educational content demonstrating domain knowledge
Authority building:
High-quality backlinks from industry publications
Media mentions and press coverage
Industry award recognition
Professional association memberships
Trustworthiness indicators:
Transparent business information
Clear return and warranty policies
Verified customer reviews
Secure website infrastructure
Privacy policy and data handling transparency
Case study: transforming a furniture retailer's strategy
Let me share how we applied these principles with one of Newtone's clients, a mid-sized furniture retailer struggling with declining organic traffic.
The Challenge
The client's traditional SEO strategy focused on ranking for terms like "modern sofa" and "dining room table." While they achieved decent rankings, traffic was declining as users increasingly asked complex questions like "best small apartment furniture for modern minimalist aesthetic under $1000."
The AI-First Solution
1. Content Restructuring: We restructured their entire product catalog to address the query fan-out pattern:
Created comprehensive room-based buying guides
Developed style-specific product collections
Built detailed comparison tools
Added extensive FAQ sections covering every aspect of furniture buying
2. Technical Implementation:
Implemented advanced structured data for every product
Created API endpoints for product information
Optimized for voice search and conversational queries
Developed real-time inventory feeds
3. Authority Building:
Established the founder as a furniture design expert through thought leadership content
Secured partnerships with interior design influencers
Created educational video content for YouTube integration
Developed comprehensive care and maintenance guides
The Results
Within six months of implementing this AI-first strategy:
40% increase in qualified traffic (despite overall traffic decline)
65% improvement in conversion rates
200% increase in complex query rankings
Significant improvement in average order value
Most importantly, when AI Mode began testing in their market, they were featured prominently in furniture-related responses.
The Psychology of AI-Mediated Shopping
Understanding how customers behave when AI mediates their shopping experience is crucial for optimization success.
Increased Expectation for Immediate Answers
Customers using AI Mode expect comprehensive, immediate responses. They're less patient with sites that don't quickly provide the information they're seeking. This means:
Product pages must answer questions before they're asked
Navigation should anticipate user needs
Information architecture must be intuitive and comprehensive
Higher Trust in AI-Recommended Sources
When Google's AI recommends a product or retailer, users perceive it as a third-party endorsement. This creates:
Increased conversion potential for featured retailers
Higher expectations for product quality and service
Greater scrutiny of customer reviews and ratings
Preference for Comprehensive Information
AI Mode users are accustomed to receiving detailed, synthesized information. They expect:
Complete product specifications
Thorough comparison information
Clear pricing and availability
Comprehensive customer service information
Industry-Specific Implications
Different ecommerce sectors will experience varying impacts from AI Mode:
Fashion and Apparel
High Impact: Style recommendations and outfit coordination
Optimization Focus: Visual content, size guides, styling advice
Key Opportunities: Personal styling AI integration, trend analysis
Electronics and Technology
High Impact: Technical specifications and compatibility questions
Optimization Focus: Detailed spec sheets, compatibility charts, technical support
Key Opportunities: Technical education content, comparison tools
Home and Garden
Medium Impact: Project planning and product selection
Optimization Focus: How-to guides, room planning tools, maintenance information
Key Opportunities: Visual planning tools, seasonal content
Beauty and Personal Care
High Impact: Product recommendations and ingredient analysis
Optimization Focus: Ingredient transparency, skin type matching, routine building
Key Opportunities: Personalization tools, expert advice content
The Competitive Landscape Evolution
AI Mode creates new competitive dynamics:
First-Mover Advantages
Early adopters of AI-optimized content strategies will establish authority signals that become self-reinforcing. Google's AI learns from user interactions, creating a feedback loop that benefits early successful implementations.
Platform Dependencies
Retailers will become more dependent on Google's algorithms and AI training processes. This creates both opportunities and risks:
Opportunities:
Direct API partnerships with Google
Enhanced visibility through AI recommendations
Reduced customer acquisition costs
Risks:
Algorithm changes can dramatically impact visibility
Increased competition for AI attention
Potential reduction in direct website traffic
New Partnership Models
Expect to see new types of partnerships emerge:
Content licensing agreements with AI systems
API partnerships for real-time product data
Collaborative content creation with complementary brands
Future-Proofing Your Ecommerce Strategy
Immediate Actions (Next 30 Days)
Audit Current Content: Assess how well your product pages answer comprehensive questions
Implement Structured Data: Ensure all products have complete schema markup
Optimize Page Speed: Focus on Core Web Vitals improvements
Analyze Competitor AI Coverage: Research which competitors appear in AI responses
Short-Term Strategy (Next 6 Months)
Content Expansion: Develop comprehensive buying guides and comparison content
Technical Infrastructure: Implement API-ready product catalogs
Authority Building: Establish thought leadership in your industry
User Experience Optimization: Design for AI-referred traffic patterns
Long-Term Vision (Next 2 Years)
AI Partnership Strategy: Explore direct partnerships with AI platforms
Conversational Commerce: Develop AI-powered shopping assistants
Predictive Optimization: Use AI to predict and prepare for query trends
Multi-Platform Strategy: Expand beyond Google to other AI-powered discovery platforms
The Human Element in an AI World
While technology transforms how customers discover products, the fundamental human elements of commerce remain critical:
Authenticity and Storytelling
AI can synthesize information, but it cannot create authentic brand stories. Your unique voice, values, and customer relationships become more valuable, not less.
Customer Service Excellence
When AI handles initial discovery, human customer service becomes the key differentiator. Invest in exceptional post-discovery experiences.
Innovation and Creativity
AI democratizes information access but cannot replace genuine innovation. Focus on unique products, creative solutions, and exceptional user experiences.
Measuring Success in the AI Era
Traditional metrics need supplementation with AI-specific measurements:
New KPIs to Track
AI Citation Rate: Frequency of mentions in AI responses
Query Coverage Score: Percentage of related queries your content addresses
Conversion Quality Index: Revenue per AI-referred visitor
Follow-up Engagement Rate: How often users ask additional questions about your products
Tools and Technologies
AI Monitoring Tools: Services that track your brand's appearance in AI responses
Query Analysis Platforms: Tools that predict related query generation
Content Gap Analysis: Systems that identify missing content opportunities
Semantic SEO Tools: Platforms optimized for conversational search
Preparing for Global Rollout
While AI Mode currently operates primarily in the United States, global expansion is inevitable. International retailers should:
Language and Localization Strategy
Prepare multilingual content optimized for AI consumption
Understand cultural differences in query patterns
Develop region-specific authority signals
Regulatory Considerations
Monitor AI transparency requirements in different markets
Prepare for data usage and privacy regulations
Understand local competition laws regarding AI recommendations
Market-Specific Optimization
Research local search behaviors and preferences
Adapt content strategies to cultural contexts
Build relationships with local influencers and authorities
The Road Ahead: Predictions and Preparations
Short-Term Predictions (6-18 Months)
AI Mode expands to additional countries, starting with English-speaking markets
Integration with Google Shopping becomes more sophisticated
Voice search integration creates new optimization requirements
Medium-Term Predictions (2-3 Years)
AI agents begin making autonomous purchases for routine items
Visual search integration transforms product discovery
Real-time inventory and pricing become competitive necessities
Long-Term Predictions (3-5 Years)
Conversational commerce becomes the dominant discovery method
AI assistants develop persistent shopping relationships with users
Traditional websites become primarily fulfillment and support channels
Conclusion: Embracing the Query Fan-Out Future
As I finish writing this article in that same Parisian café, I'm reminded of a conversation I had with a fellow CEO last month. He was lamenting the "good old days" when SEO was "simple"—just stuff some keywords into your content and wait for traffic. I told him those days weren't good; they were just easier to understand.
The query fan-out era isn't just a technical evolution; it's an opportunity to build deeper, more meaningful connections with customers. When AI can answer basic questions instantly, your job becomes creating experiences that AI cannot replicate: authentic stories, innovative solutions, and exceptional service.
The retailers who will thrive in this new landscape aren't those who resist change, but those who embrace the complexity and see opportunity where others see disruption. At Newtone, we're building AI copywriting tools that help retailers prepare for this future because we believe technology should enhance human creativity, not replace it.
The query fan-out revolution is here. The question isn't whether your business will be affected—it's whether you'll be prepared to capitalize on the massive opportunities it presents.
Your customers are already beginning to experience a more intelligent, more helpful, more conversational way of discovering products. Are you ready to be the retailer they discover?
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Henri is the CEO and Co-founder of Newtone, an AI copywriting platform specifically designed for the retail industry. Newtone trains on editorial guidelines, SEO guidelines, and existing content to help retailers replicate their tone-of-voice, branding, and formatting perfectly. Connect with Henri on LinkedIn to discuss how AI is transforming retail marketing.
