Scan the PDP
Use a product page that matters commercially: a priority product, a category template, a promotional item or a page where customers compare before buying.
PDP evidence fit
Would this product deserve to be suggested for the query that matters?
Scan a product page, then test the buyer query you care about. The inspector shows whether the PDP has enough public evidence to identify the product, match the intent and support a confident recommendation.
It does not predict rankings. It shows the visible and structured evidence a search engine, shopping surface or AI assistant could use, what it would still have to infer, and which change is worth testing first.
What it answers
Can this PDP win the buying question?
The useful question is not whether a product page has an AI score. It is whether the page can prove enough for a specific high-intent query: what the product is, why it fits, whether it is buyable, and what could make another retailer easier to recommend.
Test the buying question
Enter the kind of search or assistant prompt the product should be able to answer, then see whether the page evidence actually supports that match.
Decide what to change
Separate product truth, authority and transaction confidence so the next action is specific, testable and owned by the right team.
What comes back
A report you can act on without commissioning an audit.
The output separates relevance, product evidence and transaction confidence so the next step is specific enough for a template, feed, content or trading owner.
What you get back.
Query fit checker
Enter a search or AI shopping prompt and get an indicative PDP evidence-fit score, split by identity, intent and transaction confidence.
Recommendation blockers
See the exact reason this page might lose out: variant clarity, markdown price evidence, delivery, returns, identifiers, proof or product attributes.
Team-ready output
Turn the finding into a platform ticket, trading/feed check, content rule and measurement plan without needing to commission an audit first.
The point is measurement
A useful PDP fix should create a signal within 4-8 weeks.
Not every improvement will move revenue immediately. The plan separates fast evidence from slower commercial outcomes so the team can learn without waiting for perfect attribution.
This week
Fix answer-blocking gaps on one priority PDP: missing facts, unclear offer evidence, weak proof, poor comparison language or schema/page mismatch.
Weeks 2-4
Apply the pattern to a small SKU set. Compare amended PDPs against similar unchanged products by conversion, engagement, search demand and support friction.
Weeks 4-8
Review whether the amended pages are easier to classify, answer, compare and convert. Keep what moved a commercial signal; drop what was just theatre.
Checking
Starting PDP scan
Fetching the public product page and reading the evidence available to crawlers.
- Fetching public PDP HTML
- Reviewing structured product data
- Checking buying confidence signals
- Building the story and test plan
PDP Inspector
Would this product deserve to be recommended?
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Commercial verdict
Overall: Needs attention
Run a scan to see whether the PDP gives enough public evidence to support understanding, trust, comparison and recommendation.
This is a page-level diagnostic, not a ranking prediction or platform score.
Executive diagnosis
What we would tell the ecommerce director
The useful output is not a pass or fail. It is the clearest reading of what the PDP can prove, where it asks systems to infer, and which fix is worth testing first.
Product Story Reconciliation
Do the ingredients combine into a product story?
This reconciles the individual checks into a commercial judgement: what the product is, who sells it, what it is for and what a shopper would actually ask.
Recommendation confidence model
What makes the page recommendable or risky?
The score combines product truth, buyer confidence, commercial competitiveness, machine readability and trading usefulness.
Channel impact
Where this helps or hurts performance
Different systems need different proof. This translates PDP evidence into likely pressure points for search, shopping feeds, paid media, assistants, conversion and customer service.
Authority and assistant readBrand authority, buyability and what an assistant can safely understand
Answer readiness
What an assistant can safely understand
Run a scan to see what the public PDP evidence can support without guesswork.
Evidence gaps
Where the page asks people or systems to guess
Prioritised by commercial usefulness, not by technical neatness.
Deeper diagnostic viewsMeaning leakage, system views and the judgement layer
Commercial meaning leakage
Where the product story leaks outside the PDP
Run a scan to see whether visible product evidence survives into schema, feeds, search and assisted comparison.
System view replay
How different commerce systems may read this PDP
This is not a ranking prediction. It shows which public evidence each system can use confidently and where it may need to infer.
Decision layer
The judgement, not just the checks
The useful output is deciding what to do, what to ignore for now and what would prove the change mattered.
What to do next
The action plan
Prioritise changes that make the product easier to classify, explain, compare and buy. Each action needs an owner, an effort level and a signal the team can watch.
Prioritised actions
Implementation backlog
Measurement and modelled demandTest plan and generated query opportunities
Measurement plan
How to know whether the changes made a difference
The test plan uses one amended PDP set, one similar control set and signals your ecommerce team can already observe.
AI shopping prompt test
Would this PDP have enough evidence to be recommended?
These are realistic comparison questions the page should be able to support if the product is going to travel well beyond its own website.
Likely demand queries
What would someone ask that could lead to this product?
These are modelled query opportunities, not observed keyword data. They are generated from the detected product type, category, selected variant, visible offer signals and schema gaps in this scan.
Query fit checker
Would this PDP deserve to answer that buying question?
This tests evidence fit. It is not a ranking forecast.
Implementation output
Turn the finding into team-ready work
The report should leave ecommerce, trading, content, SEO and platform teams with usable next actions, not just a score.
Nomorecookies
Use this as a working note, not a sales funnel
The free inspector should give your team enough direction to act on one PDP or template without needing a paid audit. If you want an independent second read across a category, book a short PDP readiness review.
Detailed technical appendixStructured evidence, schema comparison and raw checks
Structured evidence
Is the machine-readable layer carrying the same story?
Schema is useful when it reinforces the public PDP evidence instead of leaving systems to guess from page layout.
Schema versus page
What humans see compared with what machines read
The strongest PDPs align visible evidence and structured data.
Detailed evidence checks
Appendix
Where the verdict comes from
Use this when content, trading, SEO, platform or development teams need the evidence behind the recommendation.
Product Truth Needs attention
Can the page clearly explain what the product is?
The page gives the basic product story, but often misses the extra detail that helps customers and systems understand suitability, variants and proposition.
Checks
- Clear product name
- Visible product type
- Material or ingredients
- Variant clarity
- Use case
- Fit or suitability notes
- Product identifiers
- Clear proposition
Top fix: Add product-specific use case, material, size, variant and identifier detail where relevant.
Structured Understanding Exposed
Can search engines and commerce systems parse the basics?
Structured data is one of the most common PDP gaps. A page may look clear to a human while giving machines incomplete product, offer, review, shipping or return information.
Checks
- Product schema
- Offer schema
- Price
- Currency
- Availability
- Brand
- SKU, GTIN or MPN
- Image data
- Review data
- Shipping and returns
Top fix: Review Product and Offer schema first, then add rating, review, shipping, return and FAQ markup where it genuinely exists.
Buying Confidence Needs attention
Does the page reduce hesitation?
The page may be missing reassurance at the point of decision: reviews, delivery, returns, support, FAQs, guarantee or payment confidence.
Checks
- Reviews visible
- Delivery visible
- Returns visible
- Decision-specific guidance
- Guarantee or care
- Support prompt
- FAQs
- Trust signals
- Payment confidence
Top fix: Bring the most important buying reassurance closer to the product decision, especially on mobile.
Content Intelligence Needs attention
Does the content reflect how customers actually decide?
Product content should reflect search, comparison, hesitation, returns, reviews and repeat purchase behaviour, not only the buyer's internal product notes.
Checks
- Decision-point content
- Decision-specific guidance
- Customer language
- Review themes
- Returns themes
- Suitability
- Comparison help
Top fix: Use customer and order insight to add decision-specific content: fit, routine, compatibility, dimensions, care, styling or expert guidance where relevant.
Image and Asset Readiness Strong
Can the product be understood visually?
A useful PDP usually needs product-only, detail, scale and use-case imagery. The exact mix depends on the product role and category.
Checks
- Product-only image
- Use-case image
- Detail image
- Scale image
- Variant images
- Video
- Useful alt text
Top fix: Check whether images answer the most likely buying questions, not just whether enough images exist.
Commercial Availability Needs attention
Can the buying promise be trusted?
Commerce systems and customers both need clear price, stock, delivery, returns, promotion and restriction information.
Checks
- Current price
- Availability
- Variant stock
- Delivery promise
- Returns promise
- Promotion clarity
- Shipping threshold
- Restrictions
Top fix: Make availability, delivery promise, returns promise and shipping cost or threshold clear on the PDP.
Technical Access Unknown
Can the page be accessed, crawled and interpreted?
The live check reviews access, crawl and interpretation signals once a public PDP URL is submitted.
Checks
- HTTP status
- Canonical
- Robots meta
- Indexability
- Title tag
- Meta description
- H1
- Mobile clues
- JS-rendering risk
Top fix: Validate HTTP status, canonical, robots, title, meta description, H1, indexability and JS-rendered content risk.
Raw priority list
What to fix first
- Add or improve Product and Offer structured data.
- Make delivery and returns information visible on the PDP.
- Add product-specific fit, use-case or suitability content.
- Improve image coverage with detail, scale or use-case assets.
- Add customer proof or review content where available.
What this is
A free public-page diagnostic that helps your team decide what to improve and how to test it.
What it is not
It is not a claim about your ChatGPT ranking, a replacement for analytics, or a disguised request for budget.
Where to use it
Start with five commercially important PDPs, make the no-regret changes, then measure the movement against a control group.