Solution · E-Commerce
Shoppers ask AI for products. Is yours in the answer?
When buyers ask AI “What’s the best running shoe under €100?”, is your brand in the answer? 78% of consumers follow AI product recommendations. But 84% of e-commerce brands have no idea if they’re being cited.
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19.3%
Average e-commerce AI citation rate (vs. 34% B2B)
3,600+
Weekly tracking prompts needed for a mid-size retailer
78%
of buyers follow AI product recommendations
97%
Statistical confidence with category sampling methodology
Trusted by 130+ brands
The challenge
Why e-commerce AI visibility is different.
It is not more of the same problem. It is a structurally different measurement challenge, and generic GEO tools built for B2B cannot handle the scale.
- 01
Zero-click purchases
AI assistants like Perplexity now offer “Buy Now” buttons directly in answers. The sale happens without ever visiting your website.
- 02
Thousands of SKUs
A fashion retailer with 50,000 SKUs needs 3,600+ tracking prompts per week. Generic tools offer 20 to 50.
- 03
Multi-platform complexity
ChatGPT, Perplexity, Google AI Mode, Microsoft Copilot: each platform has different product citation behavior.
- 04
Seasonal volatility
Product recommendations shift 3 to 4x during peak seasons. Static monitoring misses critical revenue windows.
What the research says
In e-commerce, an AI recommendation is almost a purchase.
78%
78% of consumers who receive a product recommendation from an AI assistant actually purchase the recommended product.
What Alexandrya.AI tracks for e-commerce
Six tracking dimensions built for product-level AI visibility.
Product category citation monitoring
Track whether your products appear in “best of” lists across all major AI platforms, at the SKU and category level.
Recommendation gap analysis
See which of your products are underrepresented in AI answers compared to competitors. And where rivals dominate the recommendations you should own.
Competitor displacement analysis
Know exactly which competitors are replacing you in AI recommendations and which content changes drive displacement.
Seasonal & trend tracking
Tracking cadence increases automatically during peak seasons like Black Friday, the holidays and back-to-school, when AI recommendations shift fastest.
Multi-platform tracking
Automatic daily monitoring on ChatGPT, Gemini, Google AI Mode, Perplexity, Claude and Microsoft Copilot. All in one dashboard.
Launch velocity monitoring
Track how quickly new products get picked up by AI systems after launch, and optimize for faster AI adoption.
Retail categories
AI recommends products differently in every category.
From fashion to electronics to marketplace listings: we track the queries and buying signals specific to your category. Especially for complex products, where specs and accuracy decide the sale.
Fashion & apparel
Track how AI ranks your styles across “best” and occasion-based queries, where trends shift weekly.
Electronics & tech
Monitor spec-driven comparisons and “best under €X” recommendations where accuracy decides the sale.
Beauty & personal care
See whether AI recommends your products for skin-type, ingredient and routine-based questions.
Home & living
Track visibility across room, material and budget queries in a highly visual, comparison-heavy category.
Marketplace sellers
Understand whether AI surfaces your listings. Or only the marketplace and its top competitors.
DTC & multi-brand
Measure share of voice across your entire portfolio and against direct-to-consumer challengers.
Why now matters
Claim the AI shelf before competitors wake up.
AI product recommendations are a land grab. The brands that become the cited answer now will be extremely hard to displace later. This window closes a little more every season.
01
The AI shelf is being set now
Most retailers still don’t measure whether AI recommends their products. The brands that optimize today shape which products AI names as “the best” tomorrow.
02
AI favors established product authority
Once a model consistently recommends a product in a category, that position compounds through repeated citations. Late entrants have to dislodge an incumbent.
03
Every season widens the gap
Each peak season of structured product data and reviews builds signals competitors can’t replicate overnight. Starting early is a durable advantage.
Why Alexandrya.AI for e-commerce
Built for SKU-level tracking. Not just brand mentions.
- Category sampling methodology: 97% confidence with 73% fewer prompts
- Seasonal peak auto-scaling, no manual cadence adjustments
- Competitor displacement analysis across product categories
- Recommendation gap analysis: see where competitors outperform you in AI answers
- 11 years of international e-commerce SEO consulting experience
11 years of international e-commerce SEO consulting.
We understand large catalogs, seasonal patterns and the data volumes that come with them. That is why Alexandrya.AI doesn’t just measure. It shows you which gap to close first.
+3Built by a team that has made B2B brands visible
since 2015. Based in Munich.
Certifications and memberships
BVDW
member
GDPR
compliant
Deep dive
E-commerce AI visibility: our research.
FAQ
AI tracking in e-commerce, answered.
Why should e-commerce brands track AI visibility?
Because more and more shoppers ask AI assistants like ChatGPT, Perplexity and Google AI Overviews for product recommendations instead of scrolling through search results or category pages. If AI doesn’t know or recommend your products, you lose sales before the shopper ever reaches your store. Classic analytics never show it. AI visibility tracking makes that hidden channel measurable.
How is this different from traditional SEO or product feed optimization?
Traditional SEO and shopping feeds optimize for rankings and paid placements. AI visibility is about whether large language models cite and recommend your specific products in their generated answers: at the SKU and category level, with the right price and specs, and against which competitors. It’s a different measurement problem that classic rank trackers and feed tools simply don’t cover.
We have tens of thousands of SKUs. Can you track at that scale?
Yes. That’s exactly why we built a category sampling methodology: instead of one prompt per SKU, we sample statistically across categories to reach about 97% confidence with far fewer prompts. This makes product-level AI visibility measurable even for large catalogs with tens of thousands of SKUs.
Which AI platforms do you track, and how often?
We monitor the assistants your customers actually use: ChatGPT, Gemini, Google AI Mode and AI Overviews, Perplexity, Claude and Microsoft Copilot. Tracking runs automatically every day, and the cadence increases around peak seasons like Black Friday and the holidays, when AI product recommendations shift fastest.
How do I find out where competitors outperform us in AI recommendations?
Our recommendation gap analysis compares your AI citation share against competitors at the product and category level. You see exactly which queries your rivals dominate, which of your products are underrepresented, and where you have the biggest opportunity to close the gap with targeted content and data improvements.
How fast do we see results, and why start now?
You get a visibility baseline within days and can act on concrete gaps immediately. And starting now matters: most competitors don’t track AI visibility yet, so the barrier to becoming the recommended product is still low. Brands that build authority now are far harder to displace once the category catches on.
Free trial
Find out in 7 days which of your products AI recommends.
Add your brand, enter your competitors, define your prompts. You will see the first results from ChatGPT, Gemini, Perplexity, AI Overviews and Copilot within minutes.
- 7 days free
- No credit card required
- All features included
- Cancel anytime