Retail LLMO Lab|Tracking product and retailer visibility in AI search
A public research dashboard tracking how generative AI recommends and displays products and retailers for retail and e-commerce queries.
Tracks recommendations and retailer visibility for a family of four replacing major appliances.
Today's Highlights
—Retailers That Moved
Only retailers whose ranking changed since the previous observation are shown.
Today's Leader by Category
The highest-ranked retailer in each appliance category.
Observation Status
Run the same study for your products
This appliance dataset is one example. We can apply the same method to your products, competitors and target customers.
Daily Trend for Leading Retailers
Coverage = queries where the retailer appeared ÷ observed queries.For example, 24 of 25 equals 96%. First-position rate shows ranking strength, while Top 3 rate shows the depth of high visibility.
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Latest Ranking
Compared by first-position rateFirst-position rate: the share of queries where the retailer was recommended first.
Retailer Visibility Ranking
Aggregates the latest observation for each query and ranks retailers by breadth and position. Select a retailer to open its report.
| Rank | Retailers | Type | Queries? | Coverage? | First-position rate? | Second-position rate? | Third-position rate? | Top 3 rate? | Product links? | Appearances? |
|---|
Product Visibility Ranking
Shows which products AI recommended for the selected persona. Rates use the number of observed queries in the product's category as the denominator.
| Rank | Product | Categories | Format | Queries | Coverage | First recommendation rate | Top 3 rate | Average recommendation rank | Retailers |
|---|
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Query → Product → Retailer
Showing queries from all categories
Queries were submitted in Japanese and are retained below in their original wording as source evidence.
About this Lab
This public monitoring dashboard repeatedly asks the same questions to observe which electronics retailers and e-commerce sites generative AI presents as places to buy, then publishes mechanically aggregated results.
What We Observe
We prepare fixed shopper personas and queries, then submit them through the ChatGPT web interface. We record the answer, citations and sources, product cards, and the retailer list for each product in the order shown.
How Rankings Are Calculated
Within each query, retailers are ranked using the following sequence.
- More products linked to that retailer in the answer
- If tied, the higher position in the retailer list
- If still tied, the greater number of retailer appearances
All rates use the number of successfully observed queries as their denominator.
- Coverage: share of queries where the retailer appeared at least once
- First-position rate: share of queries where the retailer ranked first
- Top 3 rate: share of queries where the retailer ranked in the top three
How to Read the Data
- Generative AI answers can change each time, even for the same query. A daily observation is a point-in-time snapshot, and a ranking change does not necessarily indicate the effect of an optimization measure.
- The current dataset covers ChatGPT only and does not represent other generative AI systems.
- Price, stock and delivery text is recorded as displayed in the AI answer and may differ from the retailer's actual selling conditions.
- Product and retailer naming variations are normalized, with normalization decisions retained in the source research records.
- This dashboard does not judge retailer quality or superiority; it records only how retailers appear in AI answers.
Updates
Data is added after each observation and accumulated as a daily trend. Last updated is the time of the latest aggregation.
Product and service names are trademarks or registered trademarks of their respective owners.
Company
Stellagent, Inc. develops agentic commerce infrastructure for shopping and booking experiences in the AI agent era.
- Company name
- Stellagent, Inc.
- Representative
- Akihiro Suzuki, CEO
- Address
- WeWork, Ocean Gate Minatomirai 8F, 3-7-1 Minatomirai, Nishi-ku, Yokohama, Kanagawa, Japan
- Founded
- February 22, 2022
Contact
Contact us using the form below with questions about this research.
Stella LLMO
Stella LLMO measures how generative AI reads and recommends your products and services, then turns those findings into improvements. We can apply the same observation method used here to your products, competitors and target customers.