Data Collected: Analyzed AI Overview presence patterns year-over-year to understand:

• Overall AI Overview expansion by industry
• Intent-based presence patterns in eCommerce
• Search volume correlation with AI Overview appearance
• Category-level shifts in AI treatment
• Google's strategic decisions on research vs. purchase queries

Key Finding: Google expanded AI Overview presence from 34% to 46% overall (+12pp), but eCommerce sits at just 16% while all other industries average 58%. Within shopping queries, intent determines everything: "best [product]" queries exploded from 5% to 83% AI presence, while transactional and pure product queries remained flat at 13-14%. Google learned that AI helps shoppers research but gets in the way when they're ready to buy.

The Year-Over-Year Shift

The Headline Numbers

November 2024: 34% of all keywords had AI Overviews

November 2025: 46% of all keywords have AI Overviews

But that +12pp growth is not evenly distributed. Google got highly selective.

The Industry Divide

eCommerce: 16% AI Overview presence

All Other Industries: 58% AI Overview presence

Google expanded AI aggressively everywhere — except shopping. That's not an accident.

Where Google Expanded AI Overviews YoY

The Biggest Gainers

The categories and query types that saw dramatic AI Overview expansion:

• "Best [product]" queries: 5% → 83% (+78pp)
• Grocery/Food: 5% → 49% (+44pp)
• Entertainment: 4% → 47% (+43pp)
• Travel: 32% → 57% (+25pp)
• Electronics: 9% → 24% (+15pp)
• TV & Home Theater: 13% → 25% (+12pp)
• Small Kitchen Appliances: 13% → 24% (+10pp)

Google leaned heavily into research and consideration content.

Where Google Held Back

Meanwhile, transactional shopping categories barely moved:

• Apparel: 8% → 11% (+2pp)
• Home: 10% → 9% (-1pp)
• Furniture: 2% → 2% (flat)
• Pure product queries: Still under 15%

High-volume purchase terms like "65 inch tv" or "mens sneakers" remain AI Overview-free zones.

The Intent Line Google Drew

Query Intent Determines AI Presence

Forget categories. The real pattern within eCommerce is intent:

• Informational Queries ("best air fryer"): 83% have AI Overviews
• Transactional Queries ("buy air fryer"): 13% have AI Overviews
• Pure Product Names ("air fryer"): 14% have AI Overviews

The "Best" Query Explosion

The single biggest YoY shift in our entire dataset:

• 2024: 5% of "best [product]" queries had AI Overviews
• 2025: 83% of "best [product]" queries have AI Overviews

That's a +78pp swing in one year. If you're ranking for "best" content, the SERP you optimized for last holiday season no longer exists.

What This Tells Us

Google spent 2024 figuring out where AI helps versus where it gets in the way. Their conclusion:

• Research Phase: AI Overviews add value
• Purchase Phase: Traditional results close the sale

The data proves it. Informational queries saw massive AI expansion. Transactional queries stayed deliberately protected.

What the Volume Data Reveals

The Unexpected Pattern

Across all industries, we found an inverse relationship between search volume and AI Overview presence:

• Under 1K volume: 45% → 60% (+15pp)
• 1K - 5K volume: 46% → 61% (+15pp)
• 5K - 10K volume: 34% → 46% (+12pp)
• 10K - 50K volume: 26% → 36% (+11pp)
• 50K - 100K volume: 21% → 32% (+12pp)
• 100K+ volume: 20% → 28% (+8pp)

Lower volume keywords consistently have higher AI Overview rates.

But eCommerce Is Different

In eCommerce specifically, AI Overview presence is flat across all volume tiers: 14-19% regardless of search volume.

Translation: Google isn't using search volume to decide which shopping queries get AI treatment. They're using intent signals. A 500K volume product query stays protected while a 5K "best" query gets an AI Overview.

Category Deep Dive: eCommerce

AI Overview Presence by Category (2025)

• Grocery: 5% → 49% (+44pp)
• Electronics: 9% → 24% (+15pp)
• TV & Home Theater: 13% → 25% (+12pp)
• Small Kitchen Appliances: 13% → 24% (+10pp)
• Apparel: 8% → 11% (+2pp)
• Home: 10% → 9% (-1pp)
• Furniture: 2% → 2% (flat)

Why Grocery Stands Out

Grocery saw the largest expansion because food queries blend informational intent with shopping. Queries like "cottage cheese," "prosciutto," or "vegetables" have educational components (nutrition, recipes, preparation) that AI Overviews can address — unlike pure product queries.

Why Furniture Stayed Flat

At just 2% AI Overview presence in both years, furniture represents a category Google has deliberately kept AI-free. High-consideration, high-price purchases appear to warrant traditional search results where users can compare retailers, prices, and options directly.

Holiday Shopping Categories: The Black Friday View

Traditional Black Friday/Cyber Monday Categories

• Computers/Laptops: 6% → 21% (+15pp)
• Small Kitchen Appliances: 13% → 24% (+10pp)
• TVs: 9% → 18% (+9pp)
• Streaming Devices: 35% → 42% (+8pp)
• Apparel: 8% → 11% (+2pp)
• Furniture: 2% → 2% (flat)

The Mattress Reversal

One notable outlier: Mattresses dropped from 44% to 17% AI Overview presence (-27pp). Google appears to have pulled back AIOs for this high-consideration category — possibly recognizing that mattress shoppers need to compare retailers and deals rather than get AI-summarized answers.

The Strategic Implications

Two Metrics for 2025

Last year, your "best [product]" pages competed for Position 1. This year, they compete for AI citations while Position 1 sits below the fold.

This means tracking two different metrics:

• For Research Queries: Are you being CITED in AI Overviews?
• For Purchase Queries: Are you RANKING in organic results?

Same content calendar. Different success metrics depending on intent.

The Dual-Channel Reality

This isn't about AI OR organic anymore. It's both:

• Your "best [product]" content needs to be citation-worthy for AI
• Your product pages need traditional SEO excellence
• Same brand, two battlefronts

What Google Learned (And What You Should Too)

Google tested aggressively in 2024. They kept what worked, pulled back what didn't.

Their conclusion: Let AI guide consideration. Let results close conversion.

The brands who adapt aren't choosing between AI optimization and traditional SEO — they're matching their strategy to Google's intent-based approach.

Strategic Implementation Framework

For Research Content ("Best [Product]" Queries)

Your Reality: 83% of these queries now show AI Overviews

Your Goal: Get cited, not just ranked

Action Items:

• Create comprehensive comparison content that AI can summarize
• Include clear, factual statements that work as citation sources
• Structure content with distinct sections AI can reference
• Update existing "best" content for citation-worthiness

For Product Pages (Transactional Queries)

Your Reality: Only 13-14% show AI Overviews

Your Goal: Win the traditional ranking game

Action Items:

• Traditional on-page SEO still dominates
• Focus on shopping feed optimization
• Product schema markup matters
• Page speed and Core Web Vitals remain critical

For Category Pages

Your Reality: Intent varies by category

Action Items:

• Grocery/Food: Prepare for high AI presence, optimize for citations
• Apparel/Furniture: Traditional SEO focus, minimal AI disruption
• Electronics: Mixed approach — research content needs citation optimization, product pages need ranking focus

Action Items for Holiday 2025

Immediate Actions

1. Audit Your "Best" Content: The SERP changed dramatically. Review your top research-phase content for citation-worthiness.
2. Segment by Intent: Map your pages to informational vs. transactional buckets. Apply different success metrics to each.
3. Check Your Exposure: What percentage of your traffic comes from "best" queries? That's your AI Overview exposure rate.

Content Priorities

High Priority (AI Citation Focus):

• "Best [product]" guides
• Comparison content
• Buyer's guides
• "How to choose" content

Standard Priority (Traditional SEO):

• Product pages
• Category pages
• Brand pages
• Transactional landing pages

Measurement Framework

For Research Queries, Track:

• AI Overview citation presence
• Citation position within AI Overview
• Brand mention frequency

For Purchase Queries, Track:

• Organic rank position
• Click-through rate
• Shopping carousel presence

Technical Methodology

Data Source: BrightEdge AI Catalyst

Analysis Approach:

• Same keyword set pulled at identical time points (Black Friday week 2024 vs. 2025)
• AI Overview presence tracked as binary (yes/no) per keyword
• Intent classification based on query patterns and modifiers
• Volume segmentation using monthly search volume data
• Category taxonomy applied consistently across both years

Measurement Periods: November 2024, November 2025

Key Takeaways

• The 12pp Growth: Overall AI Overview presence grew from 34% to 46%, but distribution is highly uneven
• The 16% vs 58% Divide: eCommerce has dramatically lower AI presence than all other industries
• The 78pp Explosion: "Best [product]" queries saw the largest single shift in AI Overview presence
• The Intent Line: Google drew a clear boundary — AI for research, traditional results for purchase
• The Volume Insight: Search volume doesn't determine AI presence in eCommerce; intent does

The Strategic Reality: Brands need to track citations AND rankings — different metrics for different query types

Data Collected: Analyzed brand mention patterns across three AI search engines to understand:

• Brand density variations by query intent

• Platform-specific recommendation strategies

• Journey stage impact on competitive landscapes

• Citation patterns across informational, consideration, and transactional queries

• Category-level variance in brand mentions

Key Finding: All three AI engines adapt brand recommendations based on query intent, with consideration queries showing 26% more brand competition than transactional queries. Google AI Mode peaks at 8.3 brands for consideration, while Google AIO mentions only 1.4 brands for informational queries despite appearing 30.3% of the time.

The Intent Framework

Query Classification

• Informational: Educational queries where users learn about products ("What is OLED?")

• Consideration: Research queries where users evaluate options ("best coffee maker")

• Transactional: Purchase-ready queries with commercial intent ("Samsung TV Walmart")

Brand Mention Patterns by Intent

The Universal Pattern

Google AI Mode - Comprehensive Throughout

• Informational: 6.6 brands per query

• Consideration: 8.3 brands per query

• Transactional: 6.6 brands per query

ChatGPT - Journey-Adaptive

• Informational: 5.1 brands per query

• Consideration: 6.5 brands per query

• Transactional: 4.7 brands per query

Google AI Overviews - Selective Presence

• Informational: 1.4 brands per query

• Consideration: 3.9 brands per query

• Transactional: 3.9 brands per query

• Overall presence: 18.4% of queries

The Competition Landscape

Highest Competition: Consideration Stage

The 8.3 Brand Reality

• AI Mode averages 8.3 brands for consideration queries

• ChatGPT follows with 6.5 brands

• Even Google AIO jumps to 3.9 brands

• 26% more competition than transactional stage

Hidden Opportunity: Informational on Google AIO

The 1.4 Brand Advantage

• Google AIO appears for 30.3% of informational queries

• Mentions only 1.4 brands on average

• Lowest competition across all engines and intents

• Prime opportunity for educational content

The Transactional Divide

Platform-Specific Strategies

• ChatGPT drops to 4.7 brands (28% reduction from consideration)

• Google AIO appears only 14.3% of the time

• Google relies on shopping carousels for purchase intent

• AI Mode maintains 6.6 brands consistently

Citation Patterns Tell Another Story

Authority Signals by Engine

• Google AIO: 9-12 citations per query (highest)

• AI Mode: 5-8 citations per query

• ChatGPT: 4-6 citations per query

Google values source authority more heavily, particularly for informational content.

Google's Strategic Division of Labor

Where AI Overviews Appear

• Informational: 30.3% presence (highest)

• Transactional: 14.3% presence

• Consideration: 13.8% presence

Why the Pattern Makes Sense

Google uses AI Overviews to enhance education and research, while relying on established commerce features (shopping carousels, product grids, merchant listings) for transactional queries. This explains both the low presence and low brand counts for purchase-intent searches.

Category-Level Insights

High Variance Categories

Categories like Furniture show massive swings:

• AI Mode: 11.5 brands

• ChatGPT: 5.8 brands

• AI Overviews: 0.1 brands

Consistent Categories

Small Kitchen Appliances remain steady:

• AI Mode: 6.5 brands

• ChatGPT: 5.7 brands

• AI Overviews: 5.2 brands (when present)

Strategic Implementation Framework

For Consideration Content

Your Biggest Battleground

• Create comprehensive comparison guides

• Build detailed feature tables

• Develop use-case scenarios

• Expect to compete with 8+ brands

For Informational Content

Your Best Opportunity

• Target "how does X work" queries

• Create "what is" educational guides

• Build "difference between" content

• Capitalize on low competition in Google AIO

For Transactional Content

The Reality Check

• Google AIO rarely appears (14.3%)

• Traditional SEO still dominates

• Focus on shopping feed optimization

• Product pages matter more than AI optimization

Platform-Specific Strategies

Google AI Mode Strategy

• Consistently high brand mentions require differentiation

• Focus on comprehensive content that stands out

• 36% of queries show 10+ brands

ChatGPT Strategy

• Biggest journey adaptation (28% drop at transaction)

• Optimize for middle-funnel content

• Balance between information and recommendation

Google AIO Strategy

• Appears selectively but cites heavily

• Win with authoritative educational content

• Remember: complements shopping results, doesn't replace them

Action Items for Implementation

Immediate Actions

1. Audit by Intent: Map your content to informational, consideration, and transactional buckets

2. Gap Analysis: Where are you missing content for each intent stage?

3. Competitive Assessment: Count how many competitors appear for your key queries

Content Priorities

1. Consideration Content: Comparison guides, "best of" lists, evaluation criteria

2. Informational Content: How-to guides, educational resources, feature explanations

3. Transactional Optimization: Traditional SEO, shopping feeds, product pages

Technical Methodology

Data Source: BrightEdge AI Catalyst

• Tens of thousands shopping-related queries analyzed

• Brand mention counts per query

• Citation analysis per result

• Intent classification based on query patterns

Measurement Period: November 2024

Key Takeaways

• The 26% Rule: Consideration queries see 26% more brand competition than transactional

• The 8.3 Peak: AI Mode's consideration queries represent maximum brand density

• The 1.4 Opportunity: Google AIO's informational queries offer minimal competition

• The 18.4% Reality: Most queries still rely on traditional search results

• The Journey Principle: All engines adapt to user intent, just differently

Data Collected: Analyzed AI Overview citation patterns across major ecommerce categories to understand:

• Domain citation frequency patterns
• Category-specific citation preferences
• Multi-source citation overlap patterns
• Search volume correlation with citation sources
• Query intent patterns by domain

Key Finding: YouTube dominates third-party citations with nearly 7% coverage of all AI Overviews, appearing alongside 70% of the other top 5 cited sources. The real opportunity isn't beating competitors—it's leveraging the validation ecosystem AI Overviews already trust.

The Citation Hierarchy

Coverage Distribution

• YouTube: 6.9% of tracked keywords
• Amazon: 2.9% of tracked keywords
• Reddit: 2.4% of tracked keywords
• Wikipedia: 1.9% of tracked keywords
• Facebook: 1.2% of tracked keywords

The Overlap Pattern

Every AI Overview cites multiple sources (typically 3-5+), but the overlap patterns reveal YouTube's central role:

When Amazon gets cited:

• 72% also include YouTube
• 24% also include Reddit
• 9% also include Wikipedia

When Reddit gets cited:

• 72% also include YouTube
• 29% also include Amazon
• 8% also include Wikipedia

The Strategic Insight: YouTube acts as the validation hub—when Google cites other sources for credibility, YouTube provides the visual proof.

Category Domination Patterns

Electronics & TVs: YouTube's Kingdom

• YouTube: 78% of category citations
• Reddit: 17% of category citations
• Others: 5% combined

Example queries: "best 65 inch tv", "samsung vs lg oled", "4k tv under 1000"

Kitchen Appliances: Demonstration Drives Citations

• YouTube: 62% of category citations
• Reddit: 16% of category citations
• Amazon: 12% of category citations

Example queries: "best blender for smoothies", "vitamix vs ninja", "air fryer reviews"

Apparel & Fashion: The Authenticity Split

• YouTube: 41% of category citations
• Reddit: 39% of category citations
• Amazon: 17% of category citations

Example queries: "best running shoes", "winter coats that work", "jeans for men"

Grocery & Food: The Information Divide

• Wikipedia: 55% of category citations
• Facebook: 46% of category citations
• YouTube: 15% of category citations

Example queries: "what is quinoa", "apple varieties", "beef cuts"

The Query Intent Patterns

What YouTube Wins

• 31% contain "best" - comparison and evaluation queries
• 9% contain numbers - specific product specifications
• Average 3.2 words - detailed, specific searches

What Reddit Captures

• 28% contain "best" - authentic recommendations
• 39% are apparel - fit and quality discussions
• Average 3.4 words - long-tail specific queries

What Wikipedia Owns

• 0% contain "best" - purely informational
• 52% high-volume (>50K monthly searches)
• Average 1.8 words - broad topic queries

Search Volume Insights

High-Volume Keywords (>50K monthly)

• Wikipedia: 52% of its citations
• Facebook: 29% of its citations
• YouTube: 17% of its citations

Strategic Insight: Educational content dominates high-volume informational queries

Mid-Volume Keywords (5K-50K monthly)

• Reddit: 49% of its citations
• YouTube: 44% of its citations
• Amazon: 37% of its citations

Strategic Insight: The sweet spot for product research and evaluation

Low-Volume Keywords (<5K monthly)

• Amazon: 49% of its citations
• Reddit: 44% of its citations
• YouTube: 40% of its citations

Strategic Insight: Long-tail, specific product queries

The Multi-Source Reality

Citation Frequency

• Single source only: 50% of queries
• Two sources: 37% of queries
• Three sources: 12% of queries
• Four+ sources: 1% of queries

Unique vs. Shared Citations

• YouTube: 40% unique citations (highest standalone value)
• Wikipedia: 38% unique citations
• Facebook: 27% unique citations
• Reddit: 14% unique citations
• Amazon: 20% unique citations

Holiday Shopping Implications

November Research Phase

Based on patterns, expect:

• Increased "best" and comparison queries
• Higher YouTube citation frequency
• Multi-source validation for gift guides

December Purchase Phase

Historical patterns suggest:

• Decreased AI Overview presence
• More transactional queries
• Traditional search dominance

Strategic Implementation Framework

For Retailers

Immediate Actions:

1. Audit existing YouTube presence for your products
2. Identify YouTubers already reviewing your inventory
3. Create comparison content for Black Friday
4. Build resource pages featuring trusted reviews

Content Priorities:

• "Best [product] for [use case]" content
• Category comparison guides
• Gift guides with video embeds
• Product roundups with multiple sources

For OEMs

Immediate Actions:

1. Ensure every holiday SKU has video content
2. Partner with reviewers before Black Friday
3. Create embeddable specification widgets
4. Monitor Reddit for product feedback (intelligence only)

Content Priorities:

• Official product demonstration videos
• Feature explanation content
• Comparison within product lines
• Technical specification resources

The Code Freeze Advantage

Many brands face development freezes during the holidays, but these citation patterns reveal off-domain opportunities:

No Dev Resources Required:

• Amplify existing YouTube reviews
• Share creator content on social channels
• Build partnerships with micro-influencers
• Create curated review roundups
• Engage with comparison content

Universal Patterns Across Verticals

Content That Wins Citations

• Evaluation content ("best [product] for [use case]")
• Comparison content ("X vs Y")
• Educational content ("how to use")
• Visual demonstrations
• Authentic user discussions

Content That Doesn't Get Cited

• Pure product pages
• Promotional content
• Price-focused pages
• Single-brand content
• Traditional ad copy

Technical Methodology

Data Source: AI Catalyst by BrightEdge

Analysis Period: October-November 2024

Coverage: Tens of thousands of ecommerce queries across all major categories

Tracking: Daily citation pattern monitoring and category-specific analysis

Key Takeaways

1. The 7% Rule: YouTube appears in nearly 7% of all tracked ecommerce queries, 3x more than any other non-brand domain
2. The 70% Overlap: When other sources get cited, YouTube appears alongside them 70% of the time
3. The Category Split: Electronics (78% YouTube) vs Grocery (55% Wikipedia) shows clear domain preferences
4. The Intent Filter: "Best" queries favor YouTube/Reddit, informational queries favor Wikipedia

The Multi-Source Standard: 50% of queries cite multiple sources—optimize for complementary citations

Data Collected: Analyzed AI Overview presence patterns across major ecommerce categories to understand:

• Daily percentage changes in AI Overview coverage
• Category-specific retention patterns
• Search volume correlation with AI Overview presence
• Year-over-year pattern changes
• Keyword intent and funnel stage analysis

Key Finding: Google retained only 30% of

AI Overviews at peak, with dramatic differences by category. Grocery maintained 56% retention while Furniture dropped to 3%—revealing a deliberate strategy to deploy AI where it adds value without disrupting commerce.

The September Spike Pattern

Coverage Timeline

• Sept 1-10: 9% baseline coverage
• Sept 11-18: Surged to 26% (peak on Sept 18)
• Sept 19-30: Rapid pullback to 11%
• Oct 1-15: Stabilized at 9%

The Magnitude Shift

• 2024: Gentle 5.6% reduction (321 → 303 keywords)
• 2025: Massive 56.8% reduction (741 → 320 keywords)
• 10x larger pullback year-over-year

The Category Hierarchy

Winners: High Retention Categories

Grocery & Food - 56% Retention

• Recipe queries maintained strong presence
• Ingredient information valued by AI
• Food preparation guidance prioritized

TV & Home Theater - 43% Retention

• Comparison content survived cuts
• "Best TV for [use case]" queries retained
• Technical specification explanations kept

Small Kitchen Appliances - 37% Retention

• "How to use" content preserved
• Product comparison queries maintained
• Feature explanation content retained

Losers: Low Retention Categories

Furniture - 3% Retention (97% removed)

• Visual shopping experience prioritized
• Traditional galleries preferred
• Limited informational value for AI

Home - 7% Retention (93% removed)

• Decorating queries removed
• Shopping-focused vertical
• Visual browsing emphasized

Apparel - 23% Retention (77% removed)

• Fashion requires visual discovery
• Size/fit better served by reviews
• Brand shopping preserved for traditional search

The Search Volume Revelation

2025's Complete Strategy Reversal

Unlike 2024, Google now retains higher-volume keywords:

• Retained keywords: 13,675 median search volume
• Removed keywords: 12,817 median search volume
• Ratio: 1.07x higher volume for retained

Volume Distribution

Removal rates were surprisingly uniform across volume quartiles:

• Q1 (Lowest): 71.5% removed
• Q2 (Med-Low): 73.1% removed
• Q3 (Med-High): 75.0% removed
• Q4 (Highest): 66.5% removed

The Intent Pattern

What Google Kept

Middle-Funnel Dominance

• 26.3% of retained keywords are evaluation/comparison queries
• "Best [product]" queries show 25% retention
• "X vs Y" comparisons maintained strong presence

Research & Learning

• "How to" queries retained where applicable
• Educational content about products preserved
• Comparison and evaluation prioritized

What Google Removed

Bottom-Funnel Purge

• Transactional keywords heavily removed
• Price-related queries eliminated
• Specific product names dropped
• "Buy" and "deals" queries removed

The Strategic Logic: AI Overviews help during research, step back during purchase

Holiday Predictions Based on Patterns

Expected November Behavior

If 2025 follows 2024's seasonal pattern:

• Current: 9% coverage
• November projection: 10-11% coverage
• Rationale: Research phase intensifies

Expected December Behavior

• December projection: 8-9% coverage
• Rationale: Purchase intent dominates
• Pattern: AI steps back for shopping season

The Opportunity Window

• November: Citation opportunities during research phase
• December: Traditional search dominates purchases
• Critical timing: Content must be ready NOW

Strategic Implementation Framework

For High-Retention Categories (>40%)

• Double down on comparison content
• Create comprehensive buying guides
• Build "best of" content for every segment
• Focus on educational material

For Medium-Retention Categories (20-40%)

• Test both AI and traditional optimization
• Monitor weekly for pattern changes
• Create topic clusters for stability
• Balance informational and transactional

For Low-Retention Categories (<20%)

• Prioritize traditional SEO tactics
• Focus on shopping feed optimization
• Invest in visual content
• Maintain product grid prominence

Universal Patterns Across Verticals

Despite category differences, certain patterns hold:

Content Types That Win

• Evaluation content ("best [product for use case]")
• Comparison content ("X vs Y")
• Educational content ("how to use")
• Higher search volume queries

Content Types That Lose

• Transactional queries
• Specific product searches
• Price-focused content
• Brand-specific queries

The 82% Reshuffling

Year-over-year keyword overlap: Only 18%

This massive reshuffling indicates:

• Google is actively experimenting
• Strategies must be flexible
• Historical performance doesn't guarantee future presence
• Monitoring is more critical than ever

Actionable Insights for SEO Professionals

Immediate Actions

1. Audit your content: Identify evaluation vs transactional pages
2. November priority: Get comparison content indexed NOW
3. Category check: Assess your vertical's retention rate
4. Volume analysis: Focus on 13K+ search volume keywords

Long-Term Strategy

1. Dual approach: Win AI for research, traditional for transactions
2. Content clusters: Build comprehensive topic coverage
3. Monitor volatility: Weekly tracking through holidays
4. Prepare for change: Only 18% YoY consistency

Technical Methodology

Data Sources:

• Daily AI Overview tracking Sept 1 - Oct 15
• Keyword-level analysis across ecommerce categories
• Search volume correlation analysis
• Category-specific retention calculations

Measurement Period: September 1 - October 15, 2024 and 2025

Analysis Tools: Manual tracking and proprietary monitoring systems

Key Takeaways

1. The 57% Rule: Google pulled back 57% from peak, 10x more aggressive than 2024
2. The Category Split: 56% retention (Grocery) vs 3% (Furniture) shows clear priorities
3. The Volume Flip: Higher volume now retained (opposite of 2024)
4. The Intent Filter: Research queries win, transactional queries lose

The 82% Shuffle: Only 18% keyword overlap year-over-year

Data Collected: Analyzed citation and mention volatility patterns across four major AI platforms to understand:

• Week-over-week percentage changes in citation/mention share
• Correlation between citation frequency and volatility
• Domain type volatility patterns
• Engine-specific stability characteristics
• Market share impact on citation consistency

Key Finding: While each AI engine shows different baseline volatility (Perplexity: 72.8%, Google AIO: 41.8%), all engines stabilize around high-authority domains. Domains cited frequently experience 0.7% weekly volatility, while those cited sporadically swing 50%+—a 70x difference that holds across every platform.

The Volatility Hierarchy

Citation Volatility Rankings by Engine

1. AI Mode - Most volatile

2. ChatGPT - Moderate-high

3. Perplexity - Moderate baseline with extreme spikes

4. Google AIO - Most stable

Mention Volatility Rankings

1. Perplexity - Extremely volatile

2. AI Mode - High volatility

3. Google AIO - Moderate

4. ChatGPT - Most stable

The Frequency-Stability Law

Critical Threshold: The 50-citation mark represents the stability inflection point where volatility drops from 50% to 8%.

Market Share Impact on Stability

Universal Pattern Across Engines

Google AIO Citations:

• Dominant (>5% share): 2.8% volatility
• Major (1-5%): 6.9% volatility
• Medium (0.5-1%): 12.2% volatility
• Small (0.1-0.5%): 19.9% volatility
• Tiny (<0.1%): 42.9% volatility

ChatGPT Citations:

• Dominant (>5%): 1.4% volatility
• Major (1-5%): 4.9% volatility
• Medium (0.5-1%): 5.9% volatility
• Small (0.1-0.5%): 12.2% volatility
• Tiny (<0.1%): 50.7% volatility

The Perplexity Anomaly

Perplexity shows inverse patterns with extreme volatility for major players:

• Dominant (>5%): 1,945.7% volatility
• Major (1-5%): 1,471.6% volatility
• Tiny (<0.1%): 32.2% volatility

This suggests active algorithm experimentation affecting established domains most.

Domain Type Volatility Patterns

Most Volatile Categories

• Forums/Q&A (Reddit, Quora): 50-3,600% volatility depending on engine
• News Media: 20-1,900% volatility (highly engine-dependent)
• Blog Platforms: 25-76% average volatility

Most Stable Categories

• Government (.gov): 35-54% volatility across engines
• Educational (.edu): 45-60% volatility
• Reference Sites (Wikipedia): 18-204% volatility

Platform-Specific Stability

• YouTube: Maintains <1% volatility on Google properties
• Wikipedia: Consistent low volatility across all engines
• Mayo Clinic-type sites: Industry authority sites show universal stability

Strategic Implementation Framework

For High-Frequency Domains (0.7% volatility)

• Update content quarterly to maintain relevance
• Expand into adjacent query spaces
• Monitor for algorithm shifts that could impact position
• Leverage stability to test new content formats

For Moderate-Frequency Domains (8% volatility)

• Deepen content in existing categories
• Build comprehensive guides to cross stability threshold
• Focus on earning citations in 100+ additional queries
• Create topic clusters rather than standalone pages

For Low-Frequency Domains (50% volatility)

• Accept volatility as normal during growth phase
• Create pillar content in core expertise areas
• Focus on one topic cluster until gaining traction
• Prioritize quality over quantity to build authority

Universal Winners Across All Engines

Despite engine differences, certain content maintains stability everywhere:

1. Government Resources: 35-55% more stable than average
2. Educational Content: Consistent performance across platforms
3. Reference-Quality Resources: The "Mayo Clinic" of each vertical
4. Major Platforms: YouTube, Wikipedia maintain dominant positions
5. Review Sites: 3.6-5.3% citation share universally

The "Optimize Once, Win Everywhere" Reality

The data validates a unified optimization approach:

• Authority signals transcend engine preferences
• Quality content achieves stability across all platforms
• The same fundamentals drive success everywhere
• Engine "personalities" affect initial citation, not long-term stability

Why This Works

Even Perplexity—with extreme baseline volatility—stabilizes around frequently-cited sources. AI Mode shows 62.4% average volatility, yet high-authority domains maintain sub-10% volatility across ALL engines.

Actionable Insights for SEO Professionals

Immediate Actions

1. Monitor Weekly Volatility: Track citation patterns to understand current stability level
2. Identify Industry Authorities: Find and partner with your vertical's "Mayo Clinics"
3. Leverage Trust Signals: Create YouTube videos and educational content
4. Build Topic Clusters: Comprehensive coverage reduces volatility faster

Long-Term Strategy

1. Prioritize Frequency Over Recency: Being cited often matters more than being cited recently
2. Accept Initial Volatility: New domains should expect 30-50% weekly swings
3. Focus on Threshold Crossing: Aim for 50+ citations as minimum viable stability
4. Diversify Content Types: Mix formats that perform well across engines

Technical Methodology

Data Sources:

• Hundreds of Thousands of citation records across all engines
• Hundreds of Thousands of mention records across all engines
• Week-over-week percentage changes in citation/mention share
• Correlation analysis between frequency, market share, and volatility

Measurement Period: Ongoing tracking with weekly volatility calculations

Analysis Tools: BrightEdge AI Catalyst for comprehensive citation tracking

Key Takeaways

1. The 70x Rule: High-frequency citations are 70x more stable than low-frequency
2. The 50-Citation Threshold: Minimum target for achieving stability
3. Perplexity's Paradox: Major players face MORE volatility than smaller ones
4. Google's Stability Premium: Most predictable citation patterns
5. Universal Authority: Quality content stabilizes across all engines

Data Collected: Monitored brand mention and citation patterns across four major AI platforms using BrightEdge AI Catalyst to analyze:

• Brand mention rates by engine and industry
• Citation source preferences and domain diversity
• Prompt patterns that trigger brand visibility
• Industry-specific behavioral differences
• Holiday and seasonal query performance

Key Finding: Each AI engine has a distinct "personality" that determines brand visibility. ChatGPT mentions brands in 99.3% of eCommerce responses while Google AI Overview includes them in just 6.2%. These aren't random variations—they're fundamental design differences that shape how customers discover products.

The Big Numbers

• 99.3% of ChatGPT eCommerce responses include brands
• 6.2% of Google AI Overview responses mention brands
• 41.3% of ChatGPT citations go to retail/marketplace domains
• 62.4% of Google AI Overview citations go to YouTube
• 8,027 unique domains cited by Perplexity (most diverse)
• 2,127 unique domains cited by ChatGPT (most concentrated)

Brand Mention Patterns by Engine

The Brand Maximizer: ChatGPT

• 5.84 average brands per eCommerce response
• 99.3% of responses include brand mentions
• 24 maximum brands in a single response
• Heavily favors established retail giants
• Amazon appears in 61.3% of citations

The Minimalist: Google AI Overview

• 0.29 average brands per eCommerce response
• 6.2% of responses include any brands
• 19 maximum brands in a single response
• Appears alongside organic results that handle commercial intent
• Prioritizes educational content over brand listings

The Balanced Performer: Google AI Mode

• 5.44 average brands per eCommerce response
• 81.7% of responses include brands
• 30 maximum brands in a single response
• Strong preference for brand/OEM sites (15.2% of citations)
• Balances commercial and informational content

The Citation Champion: Perplexity

• 4.37 average brands per eCommerce response
• 85.7% of responses include brands
• 45 maximum brands in a single response
• 8.79 average citations per response (highest)
• Most diverse source pool with 8,027 unique domains

Domain Citation Patterns

Where Each Engine Gets Its Information

Retailer/Marketplace Dominance:

• ChatGPT: 41.3% (Amazon, Target, Walmart lead)
• Perplexity: 21.2% (more balanced distribution)
• Google AI Mode: 14.5%
• Google AI Overview: 4.7%

Social/Community Reliance:

• Google AI Overview: 12.3% (YouTube 62.4%, Reddit 25.4%)
• Google AI Mode: 4.6%
• Perplexity: 4.6%
• ChatGPT: 0.4% (almost never cites social)

Brand/OEM Sites:

• Google AI Mode: 15.2% (highest brand site preference)
• ChatGPT: 5.5%
• Perplexity: 4.1%
• Google AI Overview: 2.6%

The "Other" Category: 48-77% of citations come from specialized sites beyond major platforms—industry publications, niche experts, and topic-specific resources. This long tail represents significant opportunity for specialized content creators.

Winning Prompt Patterns

Keywords That Trigger Maximum Brand Mentions

1. "Budget/Affordable/Cheap" → 6.3-8.8 brands per response
2. "Best/Top" rankings → 4.7-6.2 brands per response
3. "Deals/Sales/Discount" → 6.2-8.3 brands per response
4. "Buy/Shop/Purchase" → 5.8-7.8 brands per response
5. "Compare/vs/versus" → 4.5-5.8 brands per response

Holiday Performance Boost

• Holiday-specific prompts generate 12% more brand mentions
• Gift queries average 6.5 brands vs 5.8 for general queries
• Perplexity delivers 9.4 citations for holiday searches
• Deal/discount queries see the highest brand density

Industry Variations

While our analysis focused on eCommerce, the behavioral patterns persist across industries:

• B2B Tech: High brand mentions for software comparisons
• Healthcare: Conservative brand mentions, higher citation requirements
• Finance: Institution names and product brands prominent
• Restaurants: Chain brands dominate mentions
• Travel: Hotels, airlines, booking platforms as primary brands

Strategic Takeaways

Understanding Engine Behaviors

ChatGPT treats most commercial queries as requiring comprehensive brand options. It prioritizes being helpful through extensive listings rather than selective recommendations.

Google AI Overview intentionally minimizes commercial content, relying on organic results for transactions while using AI for educational guidance.

Perplexity balances brand mentions with extensive source citations, appealing to research-oriented users who value transparency.

Google AI Mode strikes a middle ground, providing substantial brand information while maintaining source credibility.

The Market Context

While Google maintains 90% of search market share, these emerging AI engines influence high-value customers during research phases. ChatGPT and Perplexity users often represent early adopters and decision-makers who turn to AI for comprehensive analysis rather than simple searches.

Optimization Priorities

1. Don't pick favorites - Optimize for all engines with a unified strategy
2. Ensure retail presence - Major marketplaces are crucial for ChatGPT visibility
3. Build authority content - Perplexity rewards comprehensive, citable resources
4. Leverage video - YouTube dominates Google AI Overview citations
5. Target trigger keywords - "Budget," "best," and comparison terms guarantee mentions
6. Monitor the long tail - 48-77% of citations come from specialized sites

The 2025 Opportunity

The data reveals clear patterns: certain keywords trigger predictable brand mention rates, specific content types earn more citations, and each engine has distinct preferences. Brands that understand these patterns can optimize once but win everywhere—ensuring visibility regardless of which AI engine customers choose.

The surprise finding? Review sites perform equally across all engines (3.6-5.3% of citations), suggesting this content type transcends individual engine preferences. Meanwhile, most brands haven't yet adapted to these behavioral differences, creating significant first-mover advantage for those who act now.