Data Collected

We analyzed keywords at the same point heading into Black Friday and Cyber Monday across both years to understand:

• Year-over-year changes in AI Overview deployment rates
• Pixel height changes (how much screen real estate AIOs consume)
• Category-level expansion patterns within eCommerce
• Before/during/after timing patterns around each holiday
• Search volume distribution across keywords with AIOs

Key Finding

Google expanded AI Overviews 3x more aggressively for Black Friday than Cyber Monday — and made them 65% larger year-over-year. The pattern reveals an intent-based strategy: AIOs dominate research moments (Black Friday) while traditional results still own purchase moments (Cyber Monday). Google isn't stepping aside for shopping anymore. They're leaning in — strategically.

The Year-Over-Year Expansion

Black Friday: +106% More Keywords with AIOs

Black Friday saw the most dramatic expansion. The number of keywords triggering AI Overviews more than doubled from 2024 to 2025.

This aligns with Black Friday's role as a research-heavy shopping moment. Consumers are hunting for deals, comparing options, and evaluating purchases. Google's AI Overviews are built for exactly this use case.

Cyber Monday: +37% More Keywords with AIOs

Cyber Monday saw meaningful expansion too — but at roughly one-third the rate of Black Friday.

Cyber Monday has evolved into a more transactional holiday. Shoppers have done their research; now they're ready to buy. Google's more conservative AIO expansion here suggests they recognize that traditional shopping results (carousels, PLAs, retail organic) better serve purchase-ready users.

The Intent Interpretation

The 3x expansion gap isn't random. It reflects Google's learning over the past year:

Research intent → Heavy AIO treatment (Black Friday mindset)

Purchase intent → Traditional results dominate (Cyber Monday mindset)

Google is matching SERP treatment to user intent, not just query volume or category.

The Size Shift: 65% More Screen Real Estate

Pixel Height Comparison

Black Friday AIOs averaged 826px in 2024 and grew to 1,368px in 2025 — a 65.6% increase.

Cyber Monday AIOs averaged 831px in 2024 and grew to 1,370px in 2025 — a 64.9% increase.

AI Overviews aren't just appearing on more keywords — they're consuming significantly more screen real estate when they do appear.

What This Means for Organic Visibility

A Position 1 organic ranking in 2025 sits roughly 540 pixels lower than the same ranking in 2024 — even if your actual position didn't change.

For context: 540 pixels is approximately half a standard desktop viewport. Content that was "above the fold" last holiday season may now require scrolling to reach.

The Q4 Trend

This wasn't a sudden holiday spike. Q4 2025 averaged 1,272px compared to Q4 2024's 823px — a 54.6% increase across the entire quarter. Google committed to larger AIOs heading into the shopping season and maintained that size throughout.

The Ramp-Up Pattern: Google's Pre-Holiday Expansion

2024: Flat Throughout November

In 2024, AI Overview pixel height remained remarkably consistent throughout the holiday shopping window. November 20 saw 821px, the day before Black Friday hit 832px, Black Friday itself was 826px, and Cyber Monday was 831px.

Variation of only ~10px across the entire period. Google appeared to be in testing mode — maintaining a steady presence without significant expansion.

2025: Deliberate Ramp-Up

2025 told a different story. Google progressively expanded AIO presence in the weeks leading up to Black Friday.

Four weeks before Black Friday, pixel height averaged 1,204px. Three weeks before, it grew to 1,298px. Two weeks before, it reached 1,307px. One week before, it hit 1,340px. During holiday week, it peaked at 1,371px.

That's a 14% increase in just four weeks — a clear signal that Google intentionally expanded AIO coverage heading into peak shopping.

The Strategic Implication

For 2026 planning: the optimization window may need to shift earlier. Content that earns AIO citations 3-4 weeks before peak shopping could establish presence before the real estate gets crowded. Waiting until November may be too late.

Category Expansion: Where Google Leaned In

eCommerce Share of AIOs Grew Significantly

On Black Friday, eCommerce's share of keywords with AIOs grew from 6.8% in 2024 to 10.1% in 2025 — a 35% increase. On Cyber Monday, it grew from 7.7% to 10.2% — a 32% increase.

Google didn't just expand AIOs overall — they specifically expanded coverage of shopping-related queries. eCommerce's share of the AIO pie grew by roughly one-third.

High-Volume Shopping Keywords

Keywords with 50,000+ monthly search volume that triggered AIOs grew from approximately 1.7% in 2024 to 2.7% in 2025 — a 59% increase in Google's willingness to serve AIOs on high-stakes, high-volume shopping queries.

The Categories That Saw the Biggest Expansion

TV & Home Theater saw the most dramatic growth at +242% for Black Friday and +57% for Cyber Monday. Electronics followed with +75% for Black Friday and +44% for Cyber Monday. Small Kitchen Appliances grew +43% for Black Friday and +42% for Cyber Monday.

These are core gift categories — and exactly where shoppers do the most research before purchasing. Queries like "best 65 inch tv," "ninja vs vitamix," and "top chromebooks" now live in AIO territory.

What Stayed Flat

Apparel saw slight contraction (-4% to -9%) and Home Furnishings declined (-29% to -30%). Google appears more aggressive with AIOs in categories where research and comparison shopping dominate, less aggressive where visual browsing and personal taste drive decisions.

The "No Pullback" Finding

Did Google Step Aside During Peak Shopping?

One theory heading into 2025 was that Google might reduce AIO presence during actual shopping days — letting transactional results take over when users are ready to buy.

The data says otherwise.

Black Friday 2025 Timing Distribution

Keywords with AIOs were distributed almost perfectly across the window: 33.3% the day before, 33.3% on Black Friday itself, and 33.4% the day after. Google maintained consistent AIO presence throughout the shopping window — no pullback during the peak.

Cyber Monday 2025 Timing Distribution

Same pattern: 33.5% the day before, 33.4% on Cyber Monday, and 33.2% the day after. Google isn't temporarily stepping aside for shopping moments anymore. AIOs are a persistent feature of the shopping SERP.

What Google Learned (And What It Means)

The 2024 Testing Phase

Last year's flat pixel heights and moderate expansion suggested Google was still experimenting — testing where AIOs helped users and where they got in the way.

The 2025 Commitment

This year's data shows Google has reached conclusions:

AIOs help shoppers research. The massive Black Friday expansion (research mode) vs. moderate Cyber Monday expansion (purchase mode) shows Google matching AIO presence to intent.

Bigger AIOs don't hurt engagement. The 65% size increase suggests Google's internal metrics support larger AI-generated content blocks for shopping queries.

Category matters. Electronics, appliances, and TV/home theater saw aggressive expansion. Apparel and furnishings didn't. Google is selective about where AIOs add value.

Timing matters less than expected. No significant pullback during peak shopping days means AIOs are now a permanent fixture, not a situational feature.

Strategic Implications for Brands

Two Metrics Now Matter

The old model: track your rank position, measure traffic.

The new model: track two different metrics depending on query intent.

For research queries (Black Friday mindset): Track citation share in AI Overviews and visibility within AIO content. These queries saw the heaviest AIO expansion.

For purchase queries (Cyber Monday mindset): Track traditional rank position, Shopping carousel presence, and PLA performance. These queries saw more conservative AIO growth.

Same content calendar. Different success metrics depending on intent.

The Optimization Window Shifted

2024: Optimize in November, compete during peak shopping.

2025+: Optimize in October (or earlier), establish AIO presence before the ramp-up begins.

Google's 14% pre-holiday expansion suggests that waiting until the shopping season to optimize for AIOs puts you behind brands who started earlier.

Category-Specific Planning

If you're in TV/Electronics/Small Appliances: AIOs are now a major part of your competitive landscape. These categories saw the biggest expansion — TV & Home Theater grew +242%, Electronics +75%, and Small Kitchen Appliances +43%. Plan for AIOs as a persistent SERP feature.

If you're in Apparel/Home Furnishings: AIOs expanded less aggressively — Apparel contracted slightly and Home Furnishings declined significantly. Traditional SEO may still deliver more impact than AIO-specific strategies in these categories.

Technical Methodology

Data Source: BrightEdge AI Catalyst

Analysis Approach:

• Keywords analyzed at equivalent points around Black Friday and Cyber Monday in 2024 and 2025
• AI Overview presence tracked by date, industry, and category
• Pixel height data tracked daily throughout Q4 2024 and Q4 2025
• Search volume distribution analyzed across keywords with AIOs
• Category-level breakdowns by L0/L1 taxonomy

Measurement Periods:

• Black Friday 2024: November 28-29, 2024
• Cyber Monday 2024: December 1-3, 2024
• Black Friday 2025: November 27-29, 2025
• Cyber Monday 2025: November 30 - December 2, 2025

Pixel Height Tracking: Daily averages across tracked keyword set

Key Takeaways

The Expansion Gap: Black Friday saw +106% more keywords with AIOs YoY; Cyber Monday saw +37%. Google expanded 3x more aggressively for research moments than purchase moments.

The Size Shift: AI Overviews grew 65% larger YoY (~826px → ~1,368px). Same rankings now mean less visibility above the fold.

The Ramp-Up Pattern: 2025 showed a 14% increase in AIO pixel height in the 4 weeks before Black Friday. 2024 was flat. Google now deliberately expands heading into peak shopping.

The Category Story: TV & Home Theater (+242%), Electronics (+75%), and Small Kitchen Appliances (+43%) saw the biggest AIO expansion. Core gift categories are now AIO territory.

The No-Pullback Reality: Google maintained consistent AIO presence before, during, and after both holidays. No stepping aside for peak shopping.

The Intent Strategy: Google matches AIO presence to user intent — heavy for research, lighter for purchase. Your metrics should do the same.

Data Collected

Analyzed citation sources across both AI platforms to understand:

• Which domains each AI cites for shopping queries
• Retailer vs. third-party citation distribution
• How the same queries produce different source selections
• The underlying philosophy driving each platform's approach

Key Finding

Google AI Overviews cite retailers only ~4% of the time, leaning heavily on YouTube, Reddit, and editorial sources. ChatGPT cites retailers ~36% of the time — a 9x difference. The reason? Google's AIOs sit above Shopping carousels that handle transactions, so they can focus on research. ChatGPT has to serve both needs in one response.

The Citation Divide

Google AI Overviews: "The Crowd"

Top cited sources for shopping queries: 

1. YouTube
2. Reddit
3. Quora
4. Amazon
5. Facebook

Retailer share of citations: ~4%

Google's AI Overviews lean heavily on user-generated content and editorial sources. YouTube product reviews, Reddit discussions, and Quora Q&A threads dominate the citation landscape for eCommerce queries.

ChatGPT: "The Store"

Top cited sources for shopping queries:

1. Amazon
2. Target
3. Walmart
4. Home Depot
5. Best Buy

Retailer share of citations: ~36%

ChatGPT's citations skew heavily toward retailers and product pages. Major marketplaces and retail sites appear at the top, with editorial and UGC sources playing a smaller role.

Same Query, Different Sources

Example: "Costco TV Sale"

Google AI Overview cites:

• Reddit (most frequent)
• YouTube
• Welcome to Costco Wholesale (third)

ChatGPT cites:

• Welcome to Costco Wholesale (most frequent)
• Scattered others

When users search for a specific retailer's deals, Google still routes them through community discussion first. ChatGPT goes directly to the source.

Example: "Best Immersion Blender"

Google AI Overview cites:

• Serious Eats
• Food & Wine
• The Spruce Eats
• CNET
• YouTube

ChatGPT cites:

• Amazon
• Consumer Reports

For research-phase queries, Google cites editorial reviews and recipe sites. ChatGPT cites the marketplace where you can buy immediately.

Why the Difference? Context Matters

Google's Advantage: The Full SERP

Google AI Overviews don't exist in isolation. They sit atop a full search results page that includes:

• Shopping carousels with product listings
• Product listing ads (PLAs)
• Organic retailer results
• Price comparisons

Because the transactional elements already exist below the AI Overview, Google can afford to make the AIO purely informational. The citation strategy reflects this: cite the crowd for research, let Shopping results handle the purchase.

ChatGPT's Reality: One Response

ChatGPT doesn't have a Shopping carousel underneath. The AI response IS the entire experience. This forces ChatGPT to serve both needs simultaneously:

• Provide helpful product information
• Give users a path to purchase

The higher retailer citation rate reflects this necessity — ChatGPT has to be both the research assistant AND the shopping guide.

What Each AI Prioritizes

Google AI Overviews Prioritize:

User-Generated Content

• YouTube product reviews and unboxings
• Reddit community discussions (r/BuyItForLife, r/Appliances, brand-specific subreddits)
• Quora Q&A threads

Editorial Reviews

• Serious Eats, Food & Wine, The Spruce Eats (kitchen products)
• Rtings, CNET, Consumer Reports (electronics)
• Wirecutter, TechRadar, Tom's Guide (tech products)

Why: Google assumes users in the AI Overview are still researching. The shopping infrastructure below handles conversion.

ChatGPT Prioritizes:

Major Retailers

• Amazon (dominant across categories)
• Target, Walmart (general merchandise)
• Home Depot, Lowe's (home improvement)
• Best Buy (electronics)

Brand Sites

• Manufacturer product pages
• Brand-specific information

Editorial (Secondary)

• Consumer Reports
• Category-specific review sites

Why: ChatGPT needs to provide a complete answer, including where to buy. Retailer citations serve that need directly.

The Philosophy Behind the Citations

Google's Approach: "What Do Real People Say?"

Google's citation pattern reveals a philosophy of objectivity through community consensus. By citing YouTube reviewers, Reddit discussions, and editorial reviews, AI Overviews position themselves as aggregators of authentic opinion.

This makes strategic sense: Google doesn't need to push users toward purchase — the Shopping results, PLAs, and retail organic listings already do that. The AIO can focus purely on being helpful for research.

ChatGPT's Approach: "Where Can You Get This?"

ChatGPT's citation pattern reveals a philosophy of utility through directness. By citing retailers and product pages, ChatGPT aims to shorten the path from question to purchase.

This also makes strategic sense: without a commercial infrastructure below the response, ChatGPT serves users best by including purchase pathways directly in the answer.

Strategic Implications for Brands

Understanding Visibility Across Platforms

The same content can surface differently depending on which AI a user asks. Brands should monitor visibility across both platforms:

On Google AI Overviews, look for:

• YouTube content citations
• Reddit/community mentions
• Editorial review coverage
• UGC and social proof signals

On ChatGPT, look for:

• Retail listing citations
• Product page references
• Marketplace presence
• Brand site mentions

Content That Works Across Both

Quality content surfaces on both platforms — but in different contexts:

• Product reviews on YouTube → Cited by Google AIOs
• Strong Amazon listings → Cited by ChatGPT
• Editorial coverage → Cited by both (Google more frequently)
• Brand product pages → Cited by ChatGPT primarily

The Monitoring Framework

Rather than building separate strategies, brands should track where their content appears:

YouTube reviews

• Google AIO Visibility: High
• ChatGPT Visibility: Low

Reddit presence

• Google AIO Visibility: High
• ChatGPT Visibility: Low

Editorial coverage

• Google AIO Visibility: High
• ChatGPT Visibility: Medium

Retail listings

• Google AIO Visibility: Low
• ChatGPT Visibility: High

Product pages

• Google AIO Visibility: Low
• ChatGPT Visibility: HighRetryClaude can make mistakes. Please double-check responses.

Your content surfaces differently on each platform. Use AI search tracking tools to monitor:

• Which of your pages get cited on Google AIOs
• Which get cited on ChatGPT
• How citation patterns shift over time

Understand What Drives Citations

For Google AIO visibility:

• Invest in YouTube product content
• Engage authentically in Reddit communities
• Pursue editorial review coverage
• Create content that answers "which should I buy?" questions

For ChatGPT visibility:

• Optimize retail listings and product pages
• Ensure strong marketplace presence
• Keep product information current and comprehensive
• Maintain accurate brand site content

Let Each Platform Do Its Job

Google cites the crowd because Shopping results handle transactions. ChatGPT cites retailers because it has to do both. Your content strategy doesn't need to force either platform to behave differently — optimize your content, and each AI will use it where it fits.

Coming Next

This analysis focused on citation patterns — which sources each AI trusts for shopping queries.

Up next: Our full Black Friday and Cyber Monday post-mortem, examining how Google's AI Overview strategy evolved from 2024 to 2025 and what it signals for holiday shopping in 2026.

Technical Methodology

Data Source: BrightEdge AI Catalyst

Analysis Approach:

• eCommerce prompts analyzed across Google AI Overviews and ChatGPT
• Citation sources extracted and categorized by domain
• Domains classified by type (retailer, UGC/social, editorial, brand)
• Same prompts compared across platforms where possible
• Week-over-week citation changes tracked

Measurement Periods:

Holiday Shopping Season 2025

Key Takeaways

• The 4% vs. 36% Divide: Google AIOs cite retailers ~4% of the time; ChatGPT cites retailers ~36% — a 9x difference
• The Source Hierarchy: Google leads with YouTube/Reddit/Quora; ChatGPT leads with Amazon/Target/Walmart
• The Context Explanation: Google can focus on research because Shopping results handle purchase; ChatGPT has to do both
• The Same-Query Difference: Identical searches produce different source selections based on each platform's philosophy
• The Strategic Reality: Brands should monitor visibility on both platforms and understand where their content surfaces

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