AI Visibility in Ecommerce: Which Sources Shape AI Product Recommendations?

AI assistants offer another way for consumers to discover products, compare prices and decide where to buy. When a shopper asks ChatGPT “What’s the best robot vacuum under €200?”, the brands appearing in that answer can enter the consideration set. But which sources form the information base behind these recommendations? 

Peak Ace’s LLM Visibility Benchmark analysed 1,000 commercially relevant prompts across 10 industries to measure which brands appear in AI-generated answers, which sources are retrieved and how brands are spoken about. Based on a fixed German-language prompt set tested across ChatGPT, Gemini and Google AI Overview in August 2026, our snapshot shows how the source mix changes with the question being asked. 

Here’s what the data reveals for ecommerce and retail—and what that may imply for content and Digital PR. 

Contents 

• How we conducted our LLM Visibility Benchmark 
• Which sources do AI systems use for ecommerce questions in our benchmark? 
• How does the ecommerce source mix compare to other industries? 
• Which ecommerce brands are most visible in the benchmark snapshot? 
• How can ecommerce brands approach AI visibility? 
• The full picture is in the LLM Visibility Benchmark 
• FAQ: AI Visibility in Ecommerce 

How we conducted our LLM Visibility Benchmark 

The benchmark is built on a standardized methodology: 

• 1,000 prompts across 10 industries, each with 100 commercially relevant, bottom-of-funnel prompts. 
• Prompts were split across 10 topic clusters per industry to cover different decision-making situations. 
• Four intent types were tested: best/top/test winner, comparisons, pricing/where to buy, and criteria/guidance, with 25 prompts per intent in each industry. 
• The analysis focused on the German market and German-language answers from 1–31 August 2026. 
• Each prompt ran 15 times in total: five times each across ChatGPT, Gemini and Google AI Overview. 
• Data was sourced from Peec AI exports. Fifty brands per industry were monitored, with brand reporting focused on the Top 20 by visibility within the tracked set. 

Alongside source analysis, the benchmark reports visibility, share of voice (SOV) and sentiment. Visibility measures how often a tracked entity appears in answers for the prompt set; sentiment describes the tone of answers mentioning it, on a scale of 0–100. These measures should be read within the study’s scope, not as market share or absolute brand-quality ratings. 

For the full methodology, definitions and cross-industry breakdowns, the complete LLM Visibility Benchmark is available for download. 

Which sources do AI systems use for ecommerce questions in our benchmark? 

The benchmark analysed which types of content the tested AI systems retrieve – access as an information base – when processing ecommerce prompts. 

Important: retrieval is not the same as citation. The source data shows which pages were accessed, not necessarily which were explicitly named or linked in the final answer. The following table therefore describes the observed retrieval mix, not citation shares. 

The source mix varies by intent: 

A few things here stand out: 

• Listicles and comparison pages lead “best of” and “test winner” questions in this ecommerce snapshot. Listicles also appear among the leading formats for all four intent types, making shortlist and selection content a useful area to investigate. 
• Category pages play a strong role across multiple intent types, particularly pricing and “where to buy”. They lead that intent in the ecommerce scorecard. This may indicate that category-level choice and purchasing context are relevant to the questions tested. 
• How-to guides lead criteria and guidance questions. This provides a practical reason to examine decision-making content alongside transactional pages—not simply focus on recommendation lists. 
• Articles appear among the leading formats for comparison and guidance questions. This suggests an explanatory layer in those parts of the tested decision journey, rather than an identical source mix across every intent. 

For ecommerce specifically, the combination of selection content, comparisons, category pages and practical guidance is the main takeaway. In our benchmark runs, different information needs are associated with different source formats. That does not prove that a particular format caused a recommendation, but it helps identify where to investigate content gaps. 

How does the ecommerce source mix compare to other industries? 

Across all industries in the benchmark, comparison pages are the largest individual source format by retrieval volume. However, the ecommerce scorecard shows why the industry lens matters. 

For best/top/test-winner questions, comparison pages account for 28.31% of the cross-industry retrieval mix, followed by listicles at 14.99%. In ecommerce, listicles appear ahead of comparisons in the scorecard. 

For pricing and “where to buy”, comparison pages lead the cross-industry mix at 22.64%, followed by product pages at 14.57% and category pages at 12.35%. In ecommerce, category pages lead this intent instead. 

For criteria and guidance, both the cross-industry mix and the ecommerce scorecard begin with how-to guides. The cross-industry share for that format is 22.33%. 

The practical implication: is not that ecommerce performs better or worse than another industry. It is that a general AI content strategy may miss the source patterns observed in shopping questions. Ecommerce marketers should examine both the wider pattern and the specific information needs of their own categories.

Which ecommerce brands are most visible in the benchmark snapshot? 

As additional context, the three tracked entities with the highest visibility in the ecommerce scorecard are: 

• Bosch – 13% visibility 
• Media Markt – 12% visibility 
• Miele – 9% visibility 

What’s striking here is the mix of manufacturers and retailers. Bosch and Miele are product manufacturers, whilst Media Markt is a consumer electronics retailer. This may reflect the product-selection and purchasing contexts represented in the prompt set. It does not establish that AI systems universally favour either type of business or that their visibility results from a particular content strategy. 

The scorecard reports an average visibility of 5.45% and a sentiment score of 57.4. Its Top 3 share is 37%, calculated from SOV shares normalized within the Top 20 tracked entities. These are benchmark-snapshot measures, not ecommerce market share or evidence of sales impact. 

The brand findings add context, but the more actionable focus remains the source ecosystem: where relevant information is available, which formats are retrieved and how that changes by intent. 

How can ecommerce brands approach AI visibility? 

The data raises several questions that ecommerce marketers should consider: 

• Are you represented on relevant comparison and listicle pages? A strong online shop is only one part of the information ecosystem. Investigate which independent sources appear for your own product categories and whether they describe your brand and products accurately. The benchmark does not prove that absence from one source means absence from the final answer. 
• Does your content strategy cover all four intent types? Brands that only optimize for “best product” questions address just one quarter of this benchmark’s prompt set. Comparisons, pricing and guidance together account for the other 75%—a deliberate study design, not an estimate of consumer demand. 
• Do your category pages provide useful purchasing context? Their position in the ecommerce pricing mix makes them a relevant audit priority. Consider whether they help shoppers understand the available options and relevant differences, rather than serving only as navigation. 
• Are you monitoring how your brand is described, as well as whether it appears? Sentiment can provide within-set context, but it does not demonstrate that a positive reputation causes recommendations. Track brand mentions, source retrievals and explicit citations separately. 
• Have you considered an ecommerce GEO strategy? Generative Engine Optimisation (GEO) brings SEO, content and Digital PR together in a measurable approach to AI visibility. Use the benchmark to set priorities, then validate them against prompts relevant to your own business. 

Expert tip: Think beyond more content on your own site

The full picture is in the LLM Visibility Benchmark 

This blog post covers the ecommerce highlights. The full LLM Visibility Benchmark goes considerably deeper: 

• How the source mix differs across all 10 industries within the standardized snapshot. 
• How source formats change when users ask for recommendations, comparisons, pricing or guidance. 
• The industry scorecards, including brand visibility, SOV, sentiment and concentration measures. 
• The methodology and interpretation limits needed to read those findings correctly. 
• What the observed patterns may imply for content, Digital PR and further AI visibility research. 

Download the full LLM Visibility Benchmark. 

Had a look at our benchmark and looking to develop your ecommerce AI visibility strategy? Get in touch with our LLMO and GEO team. 

FAQ: AI Visibility in Ecommerce 

What types of content do AI systems use to answer ecommerce questions? 

In our fixed German-market prompt set for August 2026, listicles lead recommendations, comparison pages lead comparison questions, how-to guides lead criteria/guidance, and category pages lead pricing/where to buy. These are retrieved source types, not necessarily explicit citations. 

How visible are ecommerce brands in ChatGPT and other AI systems? 

The ecommerce scorecard reports an average visibility of 5.45% for this benchmark’s tracked set and study period. That is not a general visibility rate for every ecommerce brand or a measure of market share. 

Which ecommerce brands are most visible in the benchmark? 

Bosch appears at 13% visibility, Media Markt at 12% and Miele at 9%. This ranking applies only to the benchmark’s tracked entities, fixed prompts and August 2026 snapshot. 

Does AI visibility matter for ecommerce brands? 

Appearing in an AI shopping answer can make a brand part of a shopper’s consideration set. The benchmark helps investigate where and how that happens, but it does not measure a sales uplift, conversion advantage or guaranteed opportunity for challenger brands. 

What is a GEO strategy for ecommerce brands? 

A GEO strategy combines SEO, useful on-site content and relevant third-party coverage to work on visibility in AI-generated answers. For ecommerce, the observed source mix provides a reason to examine category pages, comparison and list formats, practical guidance and Digital PR together, then test the priorities over time.