LLM Industry Benchmark 2026: Which Sources Shape AI E-commerce Recommendations?

When potential customers ask ChatGPT, Gemini or Google AI Overviews about products, retailers or prices, they often get an answer before they visit a shop. The sources behind that answer are therefore a relevant question for e-commerce brands. Peak Ace analysed 1,000 standardised prompts across ten industries to investigate this. This article presents the e-commerce findings and places them in the cross-industry context. 

Note: This article is an excerpt from the LLM Industry Benchmark. The complete analyses, including the source mix for every industry, are available in the benchmark itself. 

In short: 

• In our benchmark, listicles lead recommendation questions in e-commerce, while comparison pages lead across all industries. 
• Category pages lead the pricing and where-to-buy mix, which makes the category level a key point for review. 
• How-to guides lead criteria and advice questions, in line with the cross-industry pattern. 
• All findings refer to this prompt set, this period (August 2026), and this setup. 

Methodology and data corpus

The benchmark is based on a clearly defined data corpus. The following sections describe its structure and the key figures derived from it. 

Prompt set and structure: The prompt set comprises 1,000 standardised bottom-of-funnel prompts. There are 100 commercially relevant prompts per industry, distributed across ten topic clusters. The prompts are geared towards purchasing decisions, i.e., questions people ask shortly before making a purchase or choosing where to buy. All prompts are brand-neutral, so no brand names appear in the questions. 

Search intent: Each industry covers four search intents, with 25 prompts per intent: 

• Best/Top/Test Winner: Recommendation questions, such as “Which vacuum cleaner is best for…?” 
• Comparisons: Contrasts, such as “Which is better, A or B?” 
• Prices/Where to buy: Questions about costs and sources, such as “Where can I buy and compare…?” 
• Criteria/Advice: Questions about selection criteria, such as “What should I pay attention to when…?” 

Market, language and time period: The prompts are in German and geared towards the German market. The survey was conducted in August 2026. 

Systems and repetitions: Gemini, ChatGPT and Google AI Overviews were observed. Each prompt was executed 15 times, five times per system. Individual responses were considered samples, which allows for a better classification of fluctuations. 

Database: The data comes from Peec AI exports. Fifty brands per industry were monitored, and the report focuses on the top 20 brands by visibility. 

Key figures

• Visibility: Shows how frequently an entity appears in the responses to the prompt set. 
• Share of Voice (SOV): Describes the proportion of an entity among all mentions in the observed dataset. 
• Sentiment: Rates the tone of the responses on a scale of 0 to 100. 
• Retrieval: Means that a source has been accessed. It does not automatically mean that the source is cited or that the brand appears in the final answer. 

E-commerce in context

E-commerce reaches an average visibility of 5.45 percent, an average SOV of 3.90 percent and an average sentiment of 57.40. Across all ten industries, the averages are 6.94 percent, 3.72 percent and 54.58. 

Concentration is also worth noting. Within the top 20 e-commerce brands, the HHI is 878 and the top 3 share is 37 percent. Around a third of the normalised SOV therefore sits with three entities. A higher HHI indicates a more uneven distribution. These values suggest a moderate concentration of mentions within this observed group. They are not a statement about market share. 

Search intents and source patterns

Search intent appears to influence the type of source that shapes a response. The e-commerce mix differs from the cross-industry pattern, which shows that the industry is a relevant lens. 

E-commerce source mix

The table below compares the leading source type per intent in e-commerce with the leading type across all ten industries: 

These figures describe the observed retrieval mix, not citation shares. The cross-industry figures are aggregated and do not refer to a single industry. 

Four patterns stand out in the data: 

• Comparisons lead across industries in three out of four intents. Across all industries, they appear in recommendation, comparison and pricing questions. In e-commerce, the picture is more split: listicles lead recommendations, and category pages lead pricing. 
• Consulting changes the mix of sources. For criteria and advice questions, how-to guides are more prominent. They account for 22.33 percent of retrieval in this intent across all industries, compared to 5.81 percent for recommendations. 
• Category pages carry the pricing intent in e-commerce. This may indicate that questions about availability and where to buy are answered at category level rather than on individual product pages. 
• The pattern is not universal. The mix varies by industry, so the observed patterns should be checked for each product and service field. 

The industry therefore serves primarily as a starting point. The crucial factor is identifying which source types are relevant for which search intent within your own product and category field. 

What does this mean for e-commerce marketing teams?

The data reveals a trend: depending on intent, responses draw on different source types. This may help brands position themselves more deliberately. The goal is not to be visible everywhere, which is practically impossible. It is to understand what you offer, where you have well-founded answers, and where visibility is worth the effort. 

• Treat answer types separately. Practical implication: a comparison should explain alternatives along with advantages and disadvantages. A category page should explain available options and relevant differences, not just navigation. 
• Keep prices, availability and delivery consistent. Category pages lead the pricing mix in e-commerce, so details should match on your shop, in comparisons and in listicles. Check whether prices, delivery times, return conditions and product specifications are described identically everywhere. 
• Explain the criteria and link them to products. How-to guides lead criteria questions. Selection criteria, such as key features, total cost and use-case fit, can be explained clearly and linked to suitable products, so that advice and offer do not diverge. 
• Review placements in listicles and comparisons. Identify which independent comparison and listicle sources appear for your categories, and check whether they describe your products correctly. Not every publication is equally relevant, so prioritise digital PR accordingly. 
• Keep information consistent. Product names, features, prices and terms should match on your website and in relevant external sources. Outdated or conflicting information increases the risk of inaccurate statements in generated responses. 
• Keep the SEO basics in place. Key content should be accessible, clearly structured and easy to navigate. This is a prerequisite for information access, but not a guarantee of recommendations in AI-generated responses. 
• Treat monitoring as an ongoing task. Brand mentions, retrieved sources and explicit citations can be tracked separately, ideally with a consistent prompt set. Analysis by category, intent and system complements traditional search and conversion metrics. 

Limits of expressive power

The results are valid only for this specific prompt set, time period and setup. They do not represent a complete measurement of an industry’s or brand’s visibility in AI systems. In particular, the following should be noted: 

• The prompt set shapes the outcome. Different questions or wording can lead to different answers. 
• Individual responses are samples. Small differences should not be overinterpreted. 
• Three systems are not one system. Gemini, ChatGPT and Google AI Overviews cannot simply be equated. 
• Retrieval is not citation. The benchmark does not link individual sources to specific brands, and it does not show which sources shaped the final answer. 
• Knowledge and search are not separate. No causal mechanisms can therefore be derived from the source mix. 
• Dialogue and personalisation are missing. Typical purchasing journeys cannot be reliably described on this basis. 
• Sentiment is not a quality assessment. It describes the tone of the responses in the dataset, not customer satisfaction or product quality. 

Read the full benchmark

This article provides an excerpt. The complete LLM Industry Benchmark includes, among other things: 

• The source mix for each of the ten industries across all four intent types 
• Visibility, SOV and sentiment within the benchmark 
• HHI and top-3 share within the top 20 of each industry 
• The cross-industry shares of the source types 
• The industry scorecards and the complete methodology 

Download the full LLM Industry Benchmark 

Had a look at the benchmark and want to strengthen your telecoms brand’s presence in AI answers? Get in touch with Peak Ace’s LLMO and GEO team. 

FAQ: AI Visibility in E-commerce

Which sources do LLMs use for e-commerce questions?

In our benchmark, listicles lead recommendation questions, comparison pages lead comparison questions, how-to guides lead criteria and advice questions, and category pages lead pricing and where-to-buy questions. These describe retrieved source types, not guaranteed citations, and they apply only to the prompt set and period examined. 

Does AI visibility depend solely on one’s own online shop?

Comparisons, listicles, guides and other third-party formats play a significant role. It is therefore worth checking the specific URLs that appear for your categories and separating your own content from relevant third-party sources. 

How does the e-commerce source mix differ from other industries?

Across all ten industries, comparison pages lead the recommendation and pricing mixes. In e-commerce, listicles lead recommendations and category pages lead pricing. Industry is a starting point; the decisive question is which source types matter for each intent within your own categories. 

Is AI visibility relevant for e-commerce brands?

It can be. A brand that appears in an AI-generated purchase answer may become part of a buyer’s considered options. The benchmark helps observe where and how this happens, alongside traffic, leads and conversions. It does not measure sales uplift or conversion effects. 

What is a GEO strategy for e-commerce?

Generative Engine Optimisation (GEO) combines SEO, content, digital PR and monitoring to improve visibility in AI-generated answers. For e-commerce, the observed source mix suggests reviewing category pages, comparison and listicle formats, practical advice and third-party coverage together, prioritised by intent. 

How can e-commerce brands test whether measures are effective?

In three steps: audit the sources and content for the relevant categories, check that prices, availability and specifications are current and consistent, and then remeasure with the same test questions under the same conditions.