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AI Query Scanner
llms.txt GeneratorFleetquay
38of 40Roomy daily shoes
Why AI picks it
- Lists wide and extra-wide sizesFleetquay website
- Shows toe-box measurementsFleetquay guide
ChatGPT says
“Lists wide and extra-wide sizes.”
Get picked when shoppers ask AI.
Shoppers ask AI what to buy, and it answers with a shortlist. Trakkr shows who makes it, why, and how to get your products on it.
“running shoes for wide feet”40 answers
See every shortlist you’re on.
Buyers ask the same thing a hundred ways, and each question gets its own picks. Track the questions that matter and see how far you are from the top.
Beginner running shoes
Strivaro Running 1st, in 36 of 40
Marathon shoes
Strivaro Running 1st, in 37 of 40
Comfortable trainers
Strivaro Running 2nd, in 31 of 40
Stability shoes
Strivaro Running 2nd, in 30 of 40
Shoes for flat feet
Strivaro Running 4th, in 21 of 40
Shoes for wide feet
Strivaro Running 4th, in 14 of 40
- Strivaro Running
- Rivals
- Your gap to first
40 AI answers per question. Height shows how many named each brand.
Explore the evidenceGive AI a reason to pick you.
Trakkr reads the pages behind every pick. When AI can’t find what it needs, Trakkr writes the fix and ships it once you approve.
- You approve every change
- Ships to Shopify, WordPress, Webflow and GitHub
Comes in regular, wide (2E) and extra wide (4E), in US sizes 7 to 15.
Ship to Shopify+4expected
Strivaro Running
14of 40Trailform 2
- Wide sizes are missing from the page.Strivaro Running website
Follow every pick to checkout.
See the landing pages, visits and revenue attributed to AI referrals in your connected Google Analytics property.
30 days · AI referral revenue
The other $2,730 came from 214 more pages. Mini bars show 30 days of AI visits.
Google Analytics attributionThe shortlist forms before the product-page visit.
Conversational discovery joins attributes, audience, budget, reviews and availability into one decision. If the evidence is weak, the brand can disappear before paid media or conversion work gets a chance.
Buying momentPromptAnswer needsCommercial risk
01
Need-led discovery
Prompt
Best running shoes for wide feet
Answer needs
Fit attributes, use case, retailer evidence and independent context.
Risk
The product never enters the shortlist.
02
Constraint screening
Prompt
Skincare for sensitive skin under $40
Answer needs
Ingredient truth, compatible routine steps and current seller data.
Risk
The product appears, but not as a workable routine.
03
Ingredient exclusion
Prompt
High-protein snacks without artificial sweeteners
Answer needs
Complete nutrition and ingredient records across every seller.
Risk
The product is filtered out before taste matters.
04
Comparison and gifting
Prompt
Gifts for a cyclist who has everything
Answer needs
Audience fit, product difference, availability and trusted reasons to buy.
Risk
A better-documented peer owns the recommendation.
The AI answer is now a working shelf: it decides which products are considered, which attributes frame the comparison and which source earns the click.
Open the full readout.
Switch views to see how Trakkr breaks down visibility for ecommerce and consumer brands, from the market to the work that moves it.
Read the category by need, not one blended score.
See where each product line wins, which buying need is slipping and whether the problem sits in attributes, feeds or public evidence.
Strivaro Running
Category and need-state shelf position
- Portfolio shelf share
- 16.8%
- Across four product lines
- Prompts won
- 108
- Constraint-led shortlist prompts
- Attribute coverage
- 64%
- Buyer attributes with public proof
- Feed readiness
- 87%
- Owned, merchant and retailer seams
| Brand and need | Shelf share | 30d | Rank | Won / missed | Attributes | Feed |
|---|---|---|---|---|---|---|
Strivaro Running Performance footwear · Wide fit and daily miles | 18.7% | +2.4 | #3 | 31 / 14 | 74% | 91% |
Fieldwork Skin Sensitive skincare · Routine under a budget | 24.2% | -1.3 | #2 | 36 / 11 | 68% | 86% |
Peak Pantry Functional snacks · Protein and ingredient exclusions | 9.6% | -3.8 | #5 | 18 / 29 | 52% | 77% |
Tidewell Premium hydration · Everyday training and travel | 14.8% | +0.6 | #4 | 23 / 21 | 63% | 94% |
Opportunity
Peak Pantry lost 3.8 points. The product record carries nutrition values, while retailer and feed records cannot verify the sweetener constraint buyers ask about.
Strivaro Running
Performance footwear · Wide fit and daily miles
18.7%
+2.4- Rank
- #3
- Attributes
- 74%
- Feed
- 91%
Strong retailer fit language, but owned width and drop data lag.
Fieldwork Skin
Sensitive skincare · Routine under a budget
24.2%
-1.3- Rank
- #2
- Attributes
- 68%
- Feed
- 86%
Ingredients agree across sources, while routine-level pages are thin.
Peak Pantry
Functional snacks · Protein and ingredient exclusions
9.6%
-3.8- Rank
- #5
- Attributes
- 52%
- Feed
- 77%
Nutrition is present, but sweetener and formulation evidence conflict.
Tidewell
Premium hydration · Everyday training and travel
14.8%
+0.6- Rank
- #4
- Attributes
- 63%
- Feed
- 94%
Feed health is strong, but the premium comparison case is weak.
View 1 of 6
Move from the executive signal to the exact lost prompt.
Choose a buyer question and an AI engine. The answer, shortlist position, sources, competitor, risk and generated work item all update together.
Selected intent
Build a shortlist around fit, stability and daily use before visiting a product page.
Wide fit · daily training · stability
ChatGPT answer
On shortlist#3
Answer readout
Strivaro Running enters the shortlist for its roomy forefoot and stable platform, but two alternatives are easier to verify because width options, heel-to-toe drop and sizing guidance agree across more sources.
Reason to recommend
Roomy forefoot and stable everyday ride.
Evidence in the answer
- Mixed evidence
Trailform 2 product page
Owned product page
Wide fit is named, but toe-box shape and drop are not explicit.
- Supports brand
Stride House listing
Retailer
The retailer describes a stable ride and broad forefoot.
- Mixed evidence
Run Forum fit thread
Community
Fit comments are positive, while length guidance is inconsistent.
Observed gap
The exact width class, toe-box geometry and heel-to-toe drop are missing from the product record.
Generated action
Add fit dimensions and drop to the PDP, structured data and merchant feeds, then publish a wide-fit category guide.
Commercial risk
Shortlist click moves to a better-documented alternative.
Leading alternativeApex Motion
View 2 of 6
See the attribute that costs the shortlist.
Product detail, structured data, merchant feeds and retailer listings need to answer the same buyer constraint. Coverage without agreement still creates doubt.
Trailform 2 attribute coverage
65% avg.Width options
High demandVariant values agree across the PDP and product feed.
Toe-box shape
High demandRetailers describe the shape, while the owned record does not.
Heel-to-toe drop
High demandNo explicit value appears on the Trailform 2 product page.
Stability use case
High demandThe claim is clear, but supporting construction details are thin.
Materials
Medium demandMaterials stay consistent across owned and retailer listings.
Merchant and feed readiness
81% avg.Google Merchant Center
94%Core identifiers, variants and availability are current.
Owned Product markup
83%Required fields are present; buyer attributes remain sparse.
Retailer PDP syndication
69%Priority retailers carry older fit and use-case copy.
AI merchant feed seam
78%The export is structurally ready, but attribute depth needs work.
Reason to recommend
What the public record says
Roomy forefoot
supportsRetailer and community language agrees.
7 source signals
Stable daily ride
supportsThe use case is consistent, but construction proof is light.
5 source signals
Runs short
mixedSizing advice conflicts across retailer and community sources.
4 source signals
Long-run cushioning
weakToo little public evidence supports this use case.
2 source signals
View 3 of 6
The answer is built across the market, not on your site alone.
Trakkr keeps the retailer, owned, editorial, review, community and video mix visible so teams know which public record is carrying or weakening the recommendation.
Retailer listings
Retailer PDPs · marketplaces
31%Citation mix
Retailer fit copy carries more weight than the owned page for two prompts.
Owned pages
PDPs · category pages · FAQs
27%Citation mix
Strong product facts, weak need-state and comparison coverage.
Editorial and reviews
Buying guides · specialist reviews
19%Citation mix
Independent comparisons support peers more often.
Communities
Reddit · specialist forums
14%Citation mix
Fit and formulation discussion is useful but sometimes stale.
Video pages
YouTube · indexed creator pages
9%Citation mix
Demonstrations are present, while product facts are hard to extract.
Retailer versus owned
31% vs 27%
The answer set cites retailer pages more often than owned pages. That turns syndication accuracy into a brand issue, not just a channel task.
View 4 of 6
Diagnose the brand, line, market or programme that owns the gap.
A portfolio view keeps a weak retailer programme from hiding inside a strong brand score and gives every action a clear operating owner.
ViewScopeShelf shareCoverageLeading needWeakest need
01Brand
Strivaro Running
Shelf share18.7%
Coverage76%
Leading need
Wide-fit daily miles
Weakest need
Long-run cushioning
02Product line
Trailform
Shelf share21.4%
Coverage71%
Leading need
Stable road training
Weakest need
Technical fit comparisons
03Market
United States
Shelf share16.9%
Coverage82%
Leading need
Daily trainers
Weakest need
Local availability
04Programme
Retail partner feed
Shelf share12.6%
Coverage64%
Leading need
In-stock variants
Weakest need
Fit consistency
Peer set
Performance and training
Nike · Gymshark
Public names a team might include when defining a category peer set.
Beauty and skincare
The Ordinary · Glossier
Public names a team might include when mapping a beauty shortlist.
View 5 of 6
Generate the work for the exact attribute buyers ask about.
The queue covers product data, category pages, comparisons, reviews, retailer records and community sources. Every item keeps the observed prompt and proof gap attached.
Strivaro merchandising queue
Each action starts with a prompt and an evidence gap
Product dataHigh
Add width class, toe-box shape and heel-to-toe drop to Trailform 2 records
The answer can verify availability, but must infer the fit attributes that decide the shortlist.
Observed: Wide-fit running prompt · ChatGPT and GeminiDeliverable: PDP specification block, Product markup fields and feed-ready attribute copy.
Ecommerce
Category pageHigh
Publish a wide-fit running shoe category page organised by need
Products are reachable, but no crawlable page joins width, stability and mileage use cases.
Observed: Wide feet and daily miles promptDeliverable: Category page with linked products, fit definitions and attribute-level comparisons.
Merchandising
ComparisonMedium
Explain the Trailform 2 trade-offs against a leading max-cushion shoe
The product is described well in isolation, but not against the choices the buyer names.
Observed: Long-run and stability comparison promptsDeliverable: Factual comparison covering fit, drop, stability, weight class and intended use.
Brand
ReviewMedium
Give suitable reviewers a source-backed Trailform 2 fact pack
Independent guides discuss the category, but do not carry Strivaro technical detail.
Observed: Perplexity citation traceDeliverable: Current product facts, testable use cases and a clear correction contact.
Communications
RetailerHigh
Correct fit and sizing copy across priority retailer listings
Two retailer pages use older fit language that conflicts with the current variant record.
Observed: Google AI retailer-versus-owned traceDeliverable: Retailer content packet plus a live listing verification checklist.
Wholesale
Community sourceMedium
Answer repeat sizing questions with a transparent owned fit guide
Shoppers repeat the same length and toe-box questions, while the owned answer is incomplete.
Observed: Run Forum and community source traceDeliverable: Crawlable fit guide, update note and approved factual responses for community teams.
Community
View 6 of 6
Measure the answer, the evidence and the route to change it.
AI visibility becomes useful when the category, buyer need, product, model, market, source and date stay joined from signal to action.
- 01Map demand
Group real buyer questions by category, audience, attribute, need, market and product line.
- 02Read the shelf
Check which products appear, their order, the reasons given and the alternatives named.
- 03Trace the source
Keep owned pages, feeds, retailer listings, reviews, communities and cited pages attached to the answer.
- 04Diagnose the record
Compare the buyer attribute with the product, merchant, retailer and public evidence that should support it.
- 05Generate the work
Create a product-data, category, comparison, review, retailer or community-source action for the exact gap.
- 06Verify the change
Run the same prompt group again and preserve the market, model and date so movement stays explainable.
Results vary by prompt, location, model, product record and date.
Public sources behind the product model
- OpenAIIntroducing shopping research in ChatGPT (opens in a new tab)OpenAI describes conversational, constraint-led product research across public retail pages and other quality sources.
- OpenAI HelpShopping with ChatGPT Search (opens in a new tab)Merchant selection can use product metadata from third parties or merchants, including factors such as availability and quality.
- Google SearchProduct structured data (opens in a new tab)Google documents how product pages and merchant feeds can expose attributes, availability, reviews, shipping and returns.
- Merchant CenterCreate a product file for Merchant Center (opens in a new tab)Google defines product attributes as the data points that describe items in a merchant file.
- Google SearchHelp Google understand your ecommerce site structure (opens in a new tab)Google recommends crawlable paths from category and subcategory pages to products, backed by structured data and feeds.
Every answer and figure in the examples on this page is made up to show how Trakkr works, including those for real brands. The other company names are made up too.
Questions, answered.
The practical questions behind AI product discovery.
It is the share and position a brand or product earns when shoppers ask AI systems for recommendations, comparisons, gifts or products that meet specific needs. Useful measurement keeps the prompt, market, model, category, product, sources and date attached to every result.
Ask anything elseAsk anything else
The Agent answers from our docs, methods and pricing, and links to the source.
Be the pick.
Add your website and the questions your buyers ask. Track the answers, inspect the evidence and decide which changes to make.
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Every question your buyers askchecked daily in eight AI models