Over 40 experienced ecommerce SEO professionals across 24 countries responded to the 2026 SEOFOMO Ecommerce SEO & AI Search Optimization Survey, answering key questions about this year’s challenges, trends, tools, and goals.
Thank you to everyone who participated, and congratulations to Elisa Ferrari, the winner of the SEOFOMO mug and hoodie giveaway.
Key Insights from the 2026 Ecommerce SEO & AI Search Optimization Survey
1. Technical SEO is still the backbone of ecommerce SEO.
Even with all the AI search buzz, the majority of ecommerce SEO practitioners continue to point to technical SEO as their core focus area.
Crawlability, indexability, site architecture, schema markup, performance remain fundamental for visibility across both traditional and AI-driven results. And here’s the thing: many respondents flagged that while everyone’s rushing toward AI optimization, a huge number of ecommerce sites still haven’t nailed the technical basics.
2. AI search optimization has gone mainstream.
Practically all respondents said they’re either already working AI search optimization into their ecommerce SEO processes or have concrete plans to start soon.
This is a meaningful industry shift: Optimizing for AI-generated answers, citations, and entity recognition is quickly becoming part of standard SEO workflows, not something reserved for “innovation teams.”
3. Agentic commerce optimization is on the radar.
It’s still early days, but ecommerce SEO professionals are starting to pay attention to agentic commerce frameworks like the Universal Commerce Protocol (UCP) and Agent Commerce Protocol (ACP).
The direction is clear: AI systems and agents that can directly interact with product catalogs and feeds, fundamentally changing how product discovery and purchasing work online.
4. The biggest barrier to SEO success? Getting things implemented.
Strategy isn’t the problem. Execution is. The most common reason ecommerce SEO projects fall short is the implementation gap — development backlogs, limited engineering bandwidth, complex site architectures, CMS constraints.
SEO teams know what needs to happen; they just can’t get it shipped fast enough.
5. AI-driven SERP changes are eating into organic performance.
AI Overviews, new search features, more ad placements: Respondents are seeing these reshape organic visibility and click-through rates in real time.
Even when rankings go up, traffic can still go down because of how much the search results layout has changed. This is forcing SEO teams to rethink both their success metrics and their visibility strategies.
6. Revenue is still the metric that matters most.
Yes, teams are starting to track AI visibility, citations, and share of voice in AI answers. But at the end of the day, ecommerce SEO success is still judged by revenue.
Conversion rate, transactions, average order value, and ecommerce revenue: Those are the numbers that determine whether leadership sees SEO as delivering or not.
7. Measuring AI search impact remains a work in progress.
Tracking AI search performance is still tricky. Teams are experimenting with AI citation tracking, share of voice in AI responses, and referral traffic from AI platforms, but there’s no standard measurement framework yet. Attribution models for AI search are still being figured out across the board.
8. The SEO tool stack hasn’t changed much, yet.
Despite all the new AI-focused platforms popping up, most teams still lean on the same established tools: Semrush, Ahrefs, Google Search Console, Screaming Frog. AI visibility tracking tools are starting to emerge, but the tooling landscape for AI search optimization is still pretty fragmented.
9. Product feeds and structured data are more important than ever.
With search moving toward AI-assisted discovery and shopping, well-structured product data is becoming essential. Optimized product feeds, schema markup, and structured ecommerce data are increasingly what helps AI systems understand, match, and surface products to the right queries.
10. Ecommerce SEO is in a hybrid era.
What the survey results really show is that the industry is in transition.
SEO teams need to keep strengthening their traditional SEO foundations while simultaneously preparing for a landscape where AI systems, agents, and entirely new search interfaces play a much bigger role in product discovery and commerce.
Let’s go through the insights below.
30 %
Freelance / Independent SEO
14 answers
28 %
In-House SEO
13 answers
38 %
Agency SEO
18 answers
23 %
+15 Years
11 answers
34 %
From 10-15 Years
16 answers
28 %
From 5-10 Years
13 answers
9 %
From 2-5 Years
4 answers
6 %
Less than 2 Years
3 answers
89 %
Technical SEO
42 answers
74 %
AI Search Optimization
35 answers
36 %
Agentic Commerce Optimization
17 answers
77 %
Content Optimization
36 answers
43 %
Link Building
20 answers
36 %
Digital PR
17 answers
45 %
SEO Testing
21 answers
68 %
SEO reporting
32 answers
85 %
SEO Strategy
40 answers
36 %
Coordination with PPC Campaigns
17 answers
9 %
Less than 20% of the time
4 answers
21 %
20 - 49% of the time
10 answers
34 %
50% - 69% of the time
16 answers
23 %
70% - 89% of the time
11 answers
13 %
More than 90% of the time
6 answers
1. Implementation Bottlenecks & Development Constraints (Most Common)
Estimated frequency: ~35–40% of responses.
This is the dominant pattern across responses.
Typical issues mentioned:
- Development backlogs
- Lack of developer capacity
- Slow or delayed implementation
- Complex CMS or site architecture
- Internal workflows slowing execution
- Technical implementations applied incorrectly
Examples from responses
- “Slow implementation”
- “Implementation issues or delays”
- “Development backlogs”
- “Lack of capacity from dev teams”
- “Complex sites… optimisations uncover further issues”
- “Poor technical implementations”
Interpretation
Even when strategy was clear, organizations lacked the engineering resources to execute SEO and AI search initiatives quickly enough.
2. SERP Changes & AI Search Disruption (Very Common)
Estimated frequency: ~25–30%.
Many respondents pointed to structural changes in search caused by AI and Google SERP evolution.
Key factors mentioned
- AI Overviews reducing CTR
- AI Mode and LLM search shifting traffic
- More SERP features and PPC placements
- Increased presence of UGC in results
- Google algorithm volatility
Examples
- “AI Overviews lowering CTR”
- “Shifting landscape such as AI overviews / AI mode”
- “Changes in what’s surfaced in SERPs”
- “Core updates and AI mainstreaming”
Interpretation
Even well-executed SEO saw reduced traffic potential because SERPs themselves changed.
3. Budget & Resource Limitations (Common)
Estimated frequency: ~15–20%.
Many teams simply did not have enough resources to execute.
Issues mentioned
- Low SEO budgets
- Limited teams
- Too many organizational layers
- Insufficient tools
- Time constraints
Examples
- “Low budget to execute effectively”
- “Lack of sufficient tools for tracking AI visibility”
- “Limited time while managing large product catalogs”
- “Resources”
Interpretation
Ecommerce SEO programs often lacked the operational investment needed to compete effectively.
4. Strategy & Planning Issues
Estimated frequency: ~10–15%.
Some projects missed targets due to strategic mistakes or unclear goals.
Typical issues
- Unclear KPIs for AI search
- Unrealistic growth expectations
- Goals based on wrong projections
- Overreliance on traditional SEO tactics
Examples
- “Goals are fuzzy”
- “Not clear goals from the client”
- “Plans too ambitious”
- “Focused too much on old-school SEO”
Interpretation
AI search optimization is still emerging, so measurement frameworks and strategy alignment are often unclear.
5. Market, Competition & Demand Factors
Estimated frequency: ~10%.
Some respondents cited external business factors rather than SEO issues.
Examples
- Declining search demand
- Stronger competitors with better brand reputation
- Weak product offering or inventory
- Marketplace competition
Examples
- “Declining search demand”
- “Competitors have better brand perception”
- “Marketplace offering better discount”
0 %
No, and don't plan to
0 answers
11 %
No, but plan to
5 answers
0 %
I'm not sure
0 answers
4 %
No, and don't plan to
2 answers
47 %
No, but plan to
22 answers
17 %
I'm not sure
8 answers
6 %
No, and don't plan to
3 answers
53 %
No, but plan to
25 answers
26 %
I'm not sure
12 answers
17 %
No, but plan to
8 answers
4 %
No, and don't plan to
2 answers
5 %
I'm not sure
2 answers
1. Google Analytics (GA4) — Most Popular
Many teams still rely on traditional analytics tools because AI visibility tracking is not yet standardized.
Mentions: ~15+
This was by far the most cited tool, often used to measure:
- AI referral traffic (ChatGPT, Bing, etc.)
- ecommerce revenue
- channel attribution
- blended dashboards
Common setups mentioned
- GA4 alone
- GA4 + Looker Studio dashboards
- GA4 custom explorations
- GA4 with AI referral tracking
2. SEMrush — Most Popular SEO Platform
SEMrush remains the dominant all-in-one SEO platform used alongside analytics tools.
Mentions: ~10+
Used for:
- Keyword rankings
- Competitive research
- AI search tracking
- General SEO monitoring
Example responses
- “Semrush”
- “Semrush AI search”
- “Mostly Semrush”
- “SEMrush and Google Analytics”
3. Ahrefs — Very Common
Mentions: ~5+
Mainly used for:
- Backlink analysis
- Organic search rankings
- Visibility checks
4. Google Search Console
Google Search Console
Mentions: ~4+
Typically used for:
- Organic performance
- Query data
- Indexing monitoring
Often paired with GA4 dashboards.
5. AI Search Visibility Tools (Emerging Category)
Several responses mentioned new AI visibility tracking tools, but adoption is still fragmented.
- Profound
Mentions: ~3
Used for: AI search citations, LLM visibility monitoring. - Peec AI
Mentions: ~2
Tracks brand mentions and visibility in AI systems. - RankScale
Mentions: ~2 - Others: LLM Pulse, LLM Watcher.
6. Internal / Custom Tools — Very Common Pattern
Because AI search analytics is still immature, many organizations are building their own tracking solutions. Many companies use custom dashboards or internal tools.
Examples:
- Log file analysis systems
- Custom prompt testers
- Custom dashboards
1. Traffic from AI Platforms (Most Popular KPI)
Estimated frequency: ~40–45%.
The most frequently mentioned metric is traffic coming from AI systems and LLMs.
Since AI platforms don’t yet provide native analytics dashboards, most teams track AI impact indirectly through referral traffic in analytics platforms.
Examples include:
- AI referral sessions
- LLM traffic
- Visits from AI platforms
- Referral traffic from ChatGPT or similar tools
2. Conversions & Revenue (Very Common)
Estimated frequency: ~30–35%.
Many respondents emphasized business outcomes rather than visibility metrics. For AI search, revenue attribution remains the ultimate KPI.
Typical metrics:
- Revenue
- Orders / purchases
- conversion rate
- ecommerce revenue
- average order value
- ROI
Examples:
- “Revenue and orders”
- “Purchases, engaged sessions, revenue”
- “Revenue from organic traffic”
- “Conversion rate and AOV”
One respondent summarized the sentiment well:
“At the end of the day, teams care less about organic metrics. The main metric is always revenue.”
3. Mentions, Citations & Brand Presence in AI Responses (Very Common)
Estimated frequency: ~25–30%.
Many respondents track whether a brand appears inside AI-generated answers. This reflects the new equivalent of rankings in AI search: whether your brand is cited in AI-generated responses.
Common metrics:
- Citations
- Brand mentions
- Links in AI responses
- Entity visibility
Examples:
- “Citations and brand mentions”
- “Mentions in LLM and links”
- “Visibility across queries”
- “Entity visibility score”
4. Share of Voice / Visibility Metrics
Estimated frequency: ~15–20%.
Several respondents track AI search share of voice across prompts or topics. This metric attempts to measure how often a brand appears compared with competitors in AI answers.
Typical metrics:
- AI share of voice
- prompt coverage
- keyword coverage
- visibility across queries
Examples:
- “AI share of voice”
- “Number of prompts featured”
- “Directional visibility within topics”
5. AIO Visibility in Google SERPs
Estimated frequency: ~10–15%.
Some respondents specifically track AI search features in Google results, including:
- AI Overviews
- shopping listings
- featured snippets
Examples:
- “Positions in AIO”
- “AI Overview visibility”
- “Enhanced featured snippets”
6. Engagement Metrics
Estimated frequency: ~10%.
A smaller set of responses mentioned engagement indicators, such as:
- engaged sessions
- click-through rate
- user interactions
- session quality
These are used to evaluate traffic quality from AI sources.
7. Attribution Modeling
Estimated frequency: ~5%.
A few responses referenced multi-touch attribution models for AI traffic, such as:
- first-touch AI
- any-touch AI
- no-touch AI
This attempts to determine how AI contributes across the customer journey.
2 %
Less than 5%
1 answers
4 %
Between 6% to 10%
2 answers
11 %
Between 11% to 25%
5 answers
31 %
Between 26% to 40%
15 answers
28 %
Between 41% and 60%
13 answers
11 %
Between 61% and 80%
5 answers
13 %
More than 81%
6 answers
62 %
Less than 5%
29 answers
23 %
Between 6% to 10%
11 answers
4 %
Between 11% to 25%
2 answers
4 %
Between 26% to 40%
2 answers
2 %
Between 41% and 60%
1 answers
2 %
Between 61% and 80%
1 answers
3 %
More than 81%
1 answers
1. SEMrush — Most Mentioned Tool
Mentions: ~12+
SEMrush is clearly the most widely used tool across respondents. It appears to function as the default all-in-one SEO platform in many ecommerce teams.
Typical use cases mentioned:
- keyword research
- competitive analysis
- rank tracking
- AI search visibility
- general SEO monitoring
Example responses:
- “BrightEdge and Semrush”
- “Semrush, Profound”
- “Mostly Semrush”
- “Semrush + Screaming Frog”
2. Ahrefs — Very Popular
Mentions: ~10+
Common uses:
- backlink analysis
- competitor research
- keyword discovery
- site audits
Example responses:
- “Ahrefs”
- “Ahrefs and Semrush”
- “Ahrefs, Majestic”
3. Screaming Frog — Most Used Technical SEO Tool
Mentions: ~7+
Typical use cases:
- site crawling
- technical audits
- identifying indexation issues
- analyzing ecommerce site structures
Example responses:
- “Screaming Frog / Sitebulb”
- “Screaming Frog + Semrush”
4. Google Search Console — Core Data Source
Mentions: ~6+
Used for:
- organic search performance
- impressions and clicks
- indexing monitoring
- query-level insights
Often paired with GA4 and crawling tools.
5. AI Search Optimization & LLM Visibility Tools
This is an emerging category where adoption is fragmented.
- Profound
Mentions: ~3.
Tracks brand visibility in LLM responses. - Peec AI
Mentioned for tracking citations and AI search presence. - Waikay
Mentioned as a single platform for LLM tracking. - LLM Pulse
6. Content Optimization Tools
- Surfer SEO
- Clearscope
- Frase
7. Other SEO Research Tools
- AlsoAsked: Used for identifying question clusters and understanding search intent.
- Majestic: Used primarily for backlink analysis.
- Moz: All in one SEO platform.
8. AI & Automation Tools
Several respondents referenced AI tooling and automation stacks rather than traditional SEO platforms.
- Claude: Used for analysis, SEO automation, prompt-based workflows.
- OpenAI API: Used for bulk content generation automation.
- n8n: Used to automate SEO workflows.
9. Internal / Custom Tools — Very Common Pattern
A notable number of respondents rely on internal solutions. Many teams are building their own AI SEO tooling, likely because the ecosystem is still immature.
Examples:
- In-house SEO tools
- Custom AI agents
- CMS-integrated SEO tools
- Custom dashboards
- Automation scripts
1. Technical SEO Foundations (Most Common Priority)
Estimated frequency: ~35–40%.
The most frequent theme is strengthening technical SEO fundamentals, particularly to support both search engines and AI systems. Many ecommerce sites still struggle with basic technical infrastructure, which limits both search and AI visibility.
Key activities mentioned:
- crawlability improvements
- indexability fixes
- Core Web Vitals
- site architecture improvements
- schema implementation
- entity optimization
Example responses:
- “Fix technical SEO first”
- “Crawlability, schema, entity optimization”
- “Improve CWV, indexability, crawlability, UX”
- “Ensure content is crawlable for bots and AI agents”
2. AI Search & Agentic Commerce Readiness (Very Common Emerging Priority)
Estimated frequency: ~25–30%.
A major emerging theme is preparing ecommerce sites for AI-driven shopping and agentic commerce. Many respondents see AI commerce infrastructure as the next major shift in ecommerce SEO.
Key concepts mentioned:
- agentic commerce
- Universal Commerce Protocol (UCP)
- Agent Commerce Protocol (ACP)
- AI visibility
- optimization for AI answers
Example responses:
- “Agentic readiness”
- “UCP and ACP implementation”
- “Optimizing for AI answers”
- “Increase AI visibility”
3. Product Feed & Ecommerce Data Optimization
Estimated frequency: ~15–20%.
Another recurring priority is optimizing product feeds and ecommerce data structures. Product feeds are becoming critical infrastructure for AI search and shopping results.
Activities mentioned:
- product feed optimization
- Merchant Center improvements
- local inventory feeds
- product schema
- product page enhancements
Example responses:
- “Product feed enhancements”
- “PDP optimization for LLMs and shopping feeds”
- “Local inventory feeds”
4. Content & Topical Authority Development
Estimated frequency: ~20%.
Many respondents highlighted content strategy improvements as a priority. Content strategies are shifting from keyword pages to topic ecosystems and entity coverage.
Typical activities:
- content updates and freshness
- building topical authority
- expanding content clusters
- optimizing content for prompts and AI queries
Examples:
- “Content updates / freshness”
- “Build topical authority”
- “Optimize content for prompts users search for”
5. Revenue, CRO & Conversion Optimization
Estimated frequency: ~15–20%.
Several responses focused on improving conversion performance rather than just traffic. Many teams are shifting SEO goals from traffic growth to revenue generation.
Typical priorities:
- conversion rate optimization (CRO)
- increasing ecommerce revenue
- optimizing product pages for conversions
Examples:
- “Revenue increase YoY”
- “Focus on conversions”
- “Improve CRO”
6. Digital PR, Authority & Brand Mentions
Estimated frequency: ~10–15%.
Another priority is strengthening brand authority and citations, especially for AI visibility. Authority signals are increasingly important because AI models rely on trusted sources for citations.
Activities mentioned:
- digital PR campaigns
- link building
- brand mentions
- reputation management
- presence in sources cited by AI systems
Examples:
- “Online PR”
- “Digital PR and linkbuilding”
- “Brand mentions in external sources”
7. Measurement & Reporting Improvements
Estimated frequency: ~10–15%.
Some respondents highlighted measurement frameworks as a priority. Measurement remains one of the biggest gaps in AI search optimization.
Typical initiatives:
- AI visibility tracking
- attribution models
- unified reporting dashboards
- revenue attribution from AI
Examples:
- “Create accurate analytics on AI revenue”
- “Track AI visibility and revenue”
- “Unify reporting”
8. Multi-Channel Search & Omnichannel Optimization
Estimated frequency: ~10%.
A few responses emphasized expanding SEO beyond traditional search.
Activities mentioned:
- multi-channel search strategies
- aligning SEO with paid teams
- visibility across platforms
Examples:
- “Multi-channel approach”
- “Align with paid performance teams”
- “Search everywhere optimization”
AUTHOR Aleyda Solís
Aleyda Solis is an SEO and AI Search consultant and the founder of Orainti, a boutique consultancy advising some of the world's leading brands on organic growth. One of the most followed organic search specialists and most sought-after SEO and AI Search Optimization speakers, she's the maker behind SEOFOMO, the weekly newsletter read across the SEO industry, and its sister publications AI Marketers and MarketingFOMO, plus the free Learning SEO and Learning AI Search roadmaps that have helped thousands break into the field. She also runs SEOFOMO News, the SEOFOMO Meetups, and the Crawling Mondays video series and is the co-founder of Finchling, a PR story opportunities platform.
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