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AEO for Ecommerce: Your Product Data Is the Content

Updated September 2026 · Written and maintained by the Progression Agency strategy team

Shopping answers are assembled from structured attributes, not from prose. A missing attribute is not a disadvantage — it is exclusion from every query that constrains on it.

On this page · 10 sections
  1. For a retailer, the product data is the content
  2. A missing attribute is exclusion, not demotion
  3. Accuracy, and why wrong data is worse than missing data
  4. Returns, delivery and warranty: the terms that decide the order
  5. Fit, compatibility and the prose that actually earns
  6. What belongs to somebody else
  7. Measuring it
  8. The reference tables, in one place
  9. Everything else we have written on search, AI and getting found
  10. Video: structured data, product feeds and AI answers

The short answerFor a retailer this is a data quality project before it is a content project. Where-to-buy, price, availability and option questions are answered from product data aggregated across merchants and marketplaces, and a product missing an attribute is dropped from any query that constrains on it rather than ranked lower. The prose that genuinely earns is the layer a schema cannot carry: compatibility and fitment, sizing in text, honest comparisons between the pairs shoppers actually weigh, and the returns, delivery and warranty terms that decide the order — which are usually written well and published somewhere unreachable.

Attribute and feed behaviour reflects our own assessment of how constrained shopping queries resolve. Specific feed requirements differ by platform and change; those belong in each platform’s own documentation.

For a retailer, the product data is the content

Shopping answers are assembled from structured attributes aggregated across merchants, and prose plays almost no part in that.

Shopping answers are assembled from structured data

When someone asks where to buy something, what it costs, whether it is in stock or which options exist, the answer is built from product data aggregated across merchants and marketplaces. Prose plays almost no part in that. A beautifully written category page attached to products with thin attributes contributes nothing to the answer.

Which makes this a data quality problem first

The single highest-return project for most retailers is not a content programme. It is an audit of attribute coverage across the catalogue, followed by filling the gaps. That is unglamorous, it is usually an operations job rather than a marketing one, and it moves more than anything else on the list.

The exception is the layer structured data cannot carry

Whether a product fits a particular situation, whether it is compatible with something the shopper already owns, how two specific options differ for a specific use, and what happens after the order. Those are the questions a schema cannot express and a page can, and they are the ones retailers publish least of.

A missing attribute is exclusion, not demotion

For a query constraining on a field your product does not carry, the product is dropped from consideration rather than ranked lower.

Constrained queries filter on attributes

A shopper rarely names a product. They describe a need with constraints attached: a size, a capacity, a material, a compatibility requirement, a maximum price. Candidates are gathered and then the constraints are applied against product attributes.

Products without the attribute are dropped from consideration

Not ranked below the ones that have it — removed. For a query constraining on a field your product does not carry, the product does not exist. That is a much harsher penalty than the usual mental model of incomplete data, and it applies per attribute and per query.

Which attributes matter is an empirical question

Start from the constraints that actually appear in queries in your categories — dimensions, capacity, compatibility, material, power, certification, age suitability — rather than from the minimum required fields of any particular feed specification. The required fields get you listed; the constraint fields get you selected.

Variants need the same discipline

A parent product with complete attributes and variants with partial ones fails in exactly the same way for anyone whose constraint lands on a variant field. Size, colour, capacity and configuration each need their own complete record.

A parent product with complete attributes and variants with partial ones fails in exactly the same way for anyone whose constraint lands on a variant field.

Accuracy, and why wrong data is worse than missing data

Stock and price are checkable immediately and publicly, and repeated mismatches reduce how much of your data is relied on generally.

Stock and price are checkable in a way prose is not

If your data says a product is available at a price and it is not, the error surfaces immediately and publicly. Repeated mismatches degrade how much of your data is relied on generally, which affects products that were never wrong.

Audit end to end, not at the source

Data that is correct in your system and wrong by the time it reaches a marketplace or an aggregator is wrong for the purposes of an answer. The audit has to follow the data all the way out rather than stopping at export.

Feeds decay quietly

Categories get added, fields get renamed, a supplier changes a specification format, a mapping silently stops populating. Nothing alerts you, and the symptom is a slow reduction in the queries you are a candidate for. A scheduled coverage check is the only thing that catches it.

Returns, delivery and warranty: the terms that decide the order

Shoppers weigh these directly against price, and on otherwise well-run stores they are usually published somewhere unreachable.

These are among the most decisive facts you publish

How long the returns window is, who pays for return postage, whether opened items can be returned, how long a refund takes, how long delivery takes to a given destination, what it costs and above what threshold it is free, and what warranty applies and who administers it. Shoppers weigh these directly against price.

Publish them as text, not in a modal or a PDF

Terms hidden behind a click, rendered by script after load, or supplied as a downloadable document are functionally unpublished. This is the most common self-inflicted gap we see on otherwise well-run stores: the policy is good, clear and unreachable.

Be specific about destinations and conditions

‘Fast, free shipping’ is not a delivery time and not a threshold. Times by destination, costs by tier, the cut-off for same-day dispatch and what happens with oversized items are all specific, checkable and directly comparable, which is exactly why they get used.

The same work serves conventional rankings and paid shopping surfaces, which run on the same product data; AEO and SEO and feed quality are one project here rather than three.

The same work serves conventional rankings and paid shopping surfaces, which run on the same product data; AEO and SEO and feed quality are one project here rather than three.

Fit, compatibility and the prose that actually earns

Three content types a schema cannot carry, which is exactly why they are the prose worth writing.

Compatibility is the question marketplaces cannot answer

Will this fit my model, will it work with what I already own, what adapter is needed, which version superseded which. Structured data carries a compatibility list at best; it does not carry the reasoning, the exceptions or the common mistakes. A page that does is the source for those questions.

Sizing and fit guidance is the same shape

Not a size chart image, which is unreadable, but measurements in text, how the item runs relative to expectation, what to do between sizes, and how fit differs across the range. This is the single most common cause of returns in several categories, so the page pays twice.

Honest comparisons between things people actually weigh

Not a matrix of your whole catalogue. The two or three specific pairs that shoppers genuinely choose between, compared on the dimensions that separate them, including which one is wrong for which use. A comparison that concludes the more expensive option is always better is promotional and is treated as such.

Say when not to buy

The most citable line in any comparison is the one identifying who should not buy either option and what they should look at instead. It is also the line that reduces returns, which is a second return on the same page.

What belongs to somebody else

Best-in-category, brand quality and cheapest-retailer are settled by reviewers, aggregated sentiment and price data respectively.

Best-in-category belongs to reviewers

Category publishers and testing outlets hold that ground, and a retailer asserting that its own product is the best in a category is doing marketing, which is discounted. Where you have genuinely independent testing to cite, cite it.

Brand quality belongs to aggregated sentiment

Whether a brand is good is answered from accumulated reviews and coverage across the whole market. A retailer page saying a brand it stocks is excellent adds nothing.

Cheapest-retailer belongs to price aggregation

That is a data question answered from data, and the only lever is being accurate and competitive, not being persuasive about it.

Manufacturer specifications belong to manufacturers

Copying a manufacturer’s description verbatim, as thousands of retailers do, produces duplicate content that distinguishes you from nobody. The distinguishing material is what you add: fit, compatibility, comparison and terms.

Copying a manufacturer’s description verbatim, as thousands of retailers do, produces duplicate content that distinguishes you from nobody.

Measuring it

Two constrained product questions phrased as a shopper would, plus one terms question — and attribute coverage tracked directly as the leading indicator.

Three questions, monthly

Two constrained product questions in your categories, phrased the way a shopper would with a real constraint attached, and one terms question about returns or delivery. Two assistants, recorded verbatim with the date.

What counts as progress

Being a candidate for constrained queries at all, which is a function of attribute coverage, and having your terms quoted correctly. Track attribute coverage directly as well — it is the leading indicator and it moves before anything visible does.

What we would not claim

Answers vary between sessions and change without notice, and shopping answers move with availability and price across the whole market. A month of observations is a trend, not a rank.

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The reference tables, in one place

Four tables follow: where each question is answered from, the attribute audit checklist, vague against specific terms, and what prose is worth writing. They summarise the sections above and are meant to be usable on their own.

Where each ecommerce question is answered from
QuestionPrimary sourceResolved byWinnable?
Is it in stock and what does it costMerchant data and marketplacesDataYes
Does it fit or work with [other thing]Merchant pagesProseYes
What is the returns policyMerchant pagesTermsYes
How long does delivery take hereMerchant pagesTermsYes
What is the difference between A and BMerchant and reviewer pagesProseYes
What size should I orderMerchant pagesProseYes
What is the best [category]Category reviewersEditorialNo
Is [brand] any goodAggregated sentimentSentimentNo
Which retailer is cheapestPrice aggregationDataNo
Attribute audit checklist
StepWhat to checkWhy
Coverage by categoryWhich fields are populated, on what share of productsGaps are exclusions
Constraint fieldsThe attributes that appear in real queriesRequired fields only get you listed
Variant completenessEvery size, colour and configuration separatelyA constraint can land on a variant
Units and formatsConsistent and machine-readableInconsistent units fail comparisons
End-to-end accuracyData as it arrives at each destinationCorrect at source is not enough
Mapping integrityWhether every field still populates after changesFeeds decay silently
Refresh frequencyHow often stock and price update downstreamStale data erodes trust in all of it
Vague against specific
TopicWhat most stores sayWhat works
Delivery‘Fast, free shipping’Times by destination, costs by tier, dispatch cut-off
Returns‘Easy returns’Window, who pays postage, condition rules, refund timing
Warranty‘Fully guaranteed’Length, what is covered, who administers a claim
SizingA chart imageMeasurements in text, how it runs, what to do between sizes
CompatibilityA list of model numbersThe list plus exceptions and common mistakes
Comparison‘Our premium option’Dimension-by-dimension, including who should not buy either
Product descriptionThe manufacturer’s copyWhat you add: fit, use, comparison, terms
What prose is worth writing
Page typeWorth it?Why
Compatibility and fitment guidanceYesStructured data cannot carry reasoning or exceptions
Sizing and fit in textYesChart images are unreadable and returns are expensive
Honest A-versus-B comparisonsYesThe only prose that genuinely earns here
Returns, delivery and warranty as textYesDecisive, and usually unreachable
Use-case guidance for a categorySometimesOnly where genuinely specific
Best-in-category listiclesNoReviewer territory, and self-serving from a retailer
Lifestyle and brand copyNoNothing retrievable in it

Everything else we have written on search, AI and getting found

AI, AEO and what is changing

Websites and design

Choosing and working with an agency

Social, content and brand

By industry and by situation

Talk to us about your attribute coverage

We start by auditing attribute completeness across a sample of your catalogue and showing you which constrained queries you are currently excluded from, before anything else is discussed.

/contact

Not sure which of these applies to you?Tell us the situation and we will say plainly what we would do first, and what we would not.

Talk it through

Video: structured data, product feeds and AI answers

Background viewing only. The attribute-exclusion model and the audit checklist above are written out in full and are not drawn from these.

Getting found in search

AI, AEO and what is changing

Paid media and lead generation

Websites and design

Choosing and working with an agency

Social, content and brand

By industry and by situation

For a retailer, the feed is the content
The availability question is settled almost entirely by structured data. The comparison question is the only one where written pages carry real weight, and it is the one most retailers publish least of.
How a shopping answer is assembled
Step four is the one retailers underestimate. A missing attribute is not a small disadvantage; for a query that constrains on that attribute it is complete exclusion.
Which ecommerce questions a retailer can realistically win
Six questions, and five of them are answered from data or terms rather than from marketing. The best-in-category and brand-quality questions belong to reviewers and aggregated sentiment.
Where a retailer should spend effort first
The first two are data quality projects rather than content projects, and they are where the largest gains in this sector sit.
Ecommerce queries by what resolves them
The right-hand column is merchant-specific and winnable. The top-left is where category reviewers sit and a retailer is not going to displace them.
A sensible order of work
Prose comes last deliberately. A comparison page attached to products with incomplete attributes is a well-written page about items that are being excluded before it is read.
Ecommerce AEO, in numbers
This is the one sector in the tier where the highest-value work is a data quality exercise rather than a writing exercise.

Frequently asked questions

Does AEO work for ecommerce?
Yes, but the highest-return work is a data quality project rather than a content one. Shopping answers are assembled from structured product data; prose only earns on fit, compatibility, comparison and post-purchase terms.
Why does product data matter more than content?
Because where-to-buy, price, availability and options questions are answered from aggregated merchant data. A well-written category page attached to products with thin attributes contributes nothing to that answer.
What does ‘a missing attribute is exclusion’ mean?
That for a query constraining on a field your product does not carry, the product is dropped from consideration rather than ranked lower. It is a harsher penalty than most retailers assume, and it applies per attribute and per query.
Which attributes should we prioritise?
The constraints that actually appear in queries in your categories — dimensions, capacity, compatibility, material, power, certification, age suitability — rather than the minimum required fields of a feed specification. Required fields get you listed; constraint fields get you selected.
Do variants need the same treatment?
Yes. A parent product with complete attributes and variants with partial ones fails identically for anyone whose constraint lands on a variant field. Size, colour, capacity and configuration each need a complete record.
Why is wrong data worse than missing data?
Because stock and price are checkable immediately and publicly. Repeated mismatches reduce how much of your data is relied on generally, which affects products that were never wrong.
Where should the data audit stop?
At the destination, not at export. Data that is correct in your system and wrong by the time it reaches a marketplace or aggregator is wrong for the purposes of an answer.
How do feeds go wrong without anyone noticing?
Categories get added, fields get renamed, a supplier changes a specification format, a mapping silently stops populating. Nothing alerts you; the symptom is a slow reduction in the queries you are a candidate for.
Why are returns and delivery terms so important?
Because shoppers weigh them directly against price. Window length, who pays return postage, refund timing, delivery times by destination and free-shipping thresholds are decisive and directly comparable.
What is the most common self-inflicted gap?
Good, clear terms that are unreachable — hidden behind a click, rendered by script after load, or supplied as a downloadable document. The policy is fine and functionally unpublished.
What is wrong with ‘fast, free shipping’?
It is not a delivery time and not a threshold. Times by destination, costs by tier and the dispatch cut-off are specific, checkable and comparable, which is why they get used.
Why is compatibility content valuable?
Because structured data carries a compatibility list at best and not the reasoning, the exceptions or the common mistakes. A page that carries those becomes the source for compatibility questions.
Is a size chart image enough?
No. An image is unreadable to retrieval. Measurements in text, how the item runs relative to expectation, what to do between sizes and how fit varies across the range is the usable version — and it reduces returns, so it pays twice.
How should comparison pages be built?
Around the two or three pairs shoppers genuinely weigh, compared on the dimensions that separate them, including which option is wrong for which use. A comparison concluding the expensive option always wins is promotional and treated as such.
Should we really say when not to buy?
It is usually the most citable line on the page, and it reduces returns. Identifying who should not buy either option and what they should look at instead is a second return on the same content.
Can we win ‘best [category]’?
No. Category publishers and testing outlets hold that ground, and a retailer asserting its own product is best is doing marketing. Where you have genuinely independent testing to cite, cite it.
Should we use the manufacturer’s product description?
Thousands of other retailers already have, which makes it duplicate content that distinguishes you from nobody. The distinguishing material is what you add: fit, compatibility, comparison and terms.
What should we measure?
Two constrained product questions in your categories phrased as a shopper would, and one terms question about returns or delivery. Two assistants, monthly, recorded verbatim with dates.
What is the leading indicator?
Attribute coverage. Track it directly — it moves before anything visible does, and it is the thing that determines whether you are a candidate at all.
Can you guarantee we appear in AI answers?
No, and nobody can honestly. Shopping answers move with availability and price across the whole market, and responses vary between sessions. What can be committed to is complete, accurate, reachable data and terms.
Does this apply to marketplace sellers too?
The attribute discipline applies identically, and the terms layer partly transfers to the marketplace’s own policies. The prose layer is harder to own because you may not control a page at all.
We already produce a lot of content. Should we stop?
We would reallocate. Attribute completeness and reachable terms outrank every content project here, and the prose that does earn is fit, compatibility and honest comparison rather than lifestyle or category copy.

Sources and further reading

  1. Google Search Essentials — SEO starter guide
  2. Google: creating helpful, reliable, people-first content
  3. Google: intro to structured data
  4. Google: LocalBusiness structured data
  5. Google: FAQPage structured data
  6. Google: Article structured data
  7. Google: Product structured data
  8. Google: title links in search results
  9. Google: control your snippets
  10. Google: robots.txt introduction
  11. Google: sitemaps overview
  12. Google: consolidate duplicate URLs
  13. Google: redirects and Search
  14. Google: JavaScript SEO basics
  15. Google: multi-regional and multilingual sites
  16. Google Search Central Blog
  17. Google: get started with Search Console
  18. Google: how local search results are determined
  19. Google Business Profile: prohibited and restricted content
  20. Google Business Profile: address and service area guidelines
  21. Google Business Profile: review policy
  22. Google Business Profile: add or edit categories
  23. Google Ads: location targeting settings
  24. Google Ads: about negative keywords
  25. Google Ads: about Quality Score
  26. Google Ads: importing offline conversions
  27. Google Ads: about Smart Bidding
  28. Google Ads: about Performance Max
  29. Google Local Services Ads: eligibility and screening
  30. Google Ads: keyword match types
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  32. Google Analytics 4: attribution models
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  35. US Census: Statistics of US Businesses
  36. Bureau of Labor Statistics: New Jersey data
  37. BLS: Occupational Employment and Wage Statistics
  38. NJ Department of Labor: labor market information
  39. New Jersey Business Action Center
  40. US Small Business Administration: New Jersey district
  41. USA.gov: business resources
  42. web.dev: Core Web Vitals explained
  43. web.dev: Largest Contentful Paint
  44. web.dev: Cumulative Layout Shift
  45. web.dev: Interaction to Next Paint
  46. Google PageSpeed Insights
  47. Google Rich Results Test
  48. Google Search Console
  49. W3C Markup Validation Service
  50. Schema.org: LocalBusiness type
  51. Schema.org: Service type
  52. Schema.org: FAQPage type
  53. Schema.org: HowTo type
  54. W3C: WCAG 2.2 quick reference
  55. FTC: CAN-SPAM Act compliance guide
  56. FCC: telemarketing and robocall rules (TCPA)
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  107. Global Music Rights
  108. PRS for Music (UK)
  109. PPL (UK)
  110. SOCAN (Canada)
  111. APRA AMCOS (Australia)
  112. GEMA (Germany)
  113. SACEM (France)
  114. SIAE (Italy)
  115. JASRAC (Japan)
  116. IFPI
  117. RIAA
  118. National Music Publishers Association
  119. Harry Fox Agency
  120. SoundExchange
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  122. Epidemic Sound
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  128. Creative Commons
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  130. FTC: advertising and marketing
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  135. US Copyright Office
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  138. US Copyright Office: fair use FAQ
  139. USPTO: trademarks
  140. UK Advertising Standards Authority
  141. ACCC (Australia)
  142. Competition Bureau Canada
  143. GDPR overview
  144. California Consumer Privacy Act
  145. COPPA
  146. FTC: children’s privacy
  147. W3C Web Accessibility Initiative
  148. W3C: WCAG
  149. W3C: captions
  150. W3C: making audio and video accessible
  151. ADA.gov
  152. WebAIM
  153. Epilepsy Foundation
  154. Pew Research: internet and technology
  155. DataReportal
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  158. Interactive Advertising Bureau
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  165. Schema.org: HowTo
  166. Schema.org: FAQPage
  167. Schema.org: Organization
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  169. Google: video structured data
  170. CapCut
  171. Adobe Premiere Rush
  172. DaVinci Resolve
  173. Canva
  174. Descript
  175. VEED
  176. Kapwing
  177. Otter.ai
  178. Later
  179. Buffer
  180. Hootsuite
  181. Sprout Social
  182. Google Analytics
  183. Google Search Console
  184. Google Analytics developer docs
  185. GA4: events and conversions
  186. Matomo
  187. Plausible Analytics
  188. Similarweb
  189. UK Information Commissioner’s Office
  190. Office of the Privacy Commissioner of Canada
  191. Australian OAIC
  192. European Data Protection Board
  193. EU data protection
  194. EU Digital Services Act
  195. Ofcom
  196. FCC
  197. AIGA
  198. Nielsen Norman Group
  199. Smashing Magazine
  200. web.dev
  201. MDN: web media
  202. MDN: the video element
  203. ISO 21001 (reference)
  204. Buma/Stemra (Netherlands)
  205. STIM (Sweden)
  206. Teosto (Finland)
  207. Koda (Denmark)
  208. TONO (Norway)
  209. IMRO (Ireland)
  210. SGAE (Spain)
  211. ZAiKS (Poland)
  212. KOMCA (South Korea)
  213. MCSC (China)
  214. CISAC
  215. World Intellectual Property Organization
  216. TikTok: creating videos
  217. TikTok: exploring videos
  218. TikTok: privacy settings
  219. TikTok: growing your audience
  220. TikTok Creator Academy
  221. TikTok Effect House
  222. TikTok for small business
  223. Instagram: Reels help
  224. YouTube: Shorts best practice
  225. How YouTube recommends
  226. Pinterest Predicts
  227. Snapchat for Business
  228. Hootsuite blog
  229. Social Media Examiner
  230. Marketing Week
  231. Adweek
  232. Google Search Essentials — SEO starter guide
  233. Google: creating helpful, reliable, people-first content
  234. Google: intro to structured data
  235. Google: LocalBusiness structured data
  236. Google: FAQPage structured data
  237. Google: Article structured data
  238. Google: Product structured data
  239. Google: title links in search results
  240. Google: control your snippets
  241. Google: robots.txt introduction
  242. Google: sitemaps overview
  243. Google: consolidate duplicate URLs
  244. Google: redirects and Search
  245. Google: JavaScript SEO basics
  246. Google: multi-regional and multilingual sites
  247. Google Search Central Blog
  248. Google: get started with Search Console
  249. Google: how local search results are determined
  250. Google Business Profile: prohibited and restricted content
  251. Google Business Profile: address and service area guidelines
  252. Google Business Profile: review policy
  253. Google Business Profile: add or edit categories
  254. web.dev: Core Web Vitals explained
  255. web.dev: Largest Contentful Paint
  256. web.dev: Cumulative Layout Shift
  257. web.dev: Interaction to Next Paint
  258. Google PageSpeed Insights
  259. Google Rich Results Test
  260. Google Search Console
  261. W3C Markup Validation Service
  262. Schema.org: LocalBusiness type
  263. Schema.org: Service type
  264. Schema.org: FAQPage type
  265. Schema.org: HowTo type
  266. W3C: WCAG 2.2 quick reference
  267. US Census Bureau QuickFacts: New Jersey
  268. US Census Bureau: American Community Survey
  269. US Census: Statistics of US Businesses
  270. Bureau of Labor Statistics: New Jersey data
  271. BLS: Occupational Employment and Wage Statistics
  272. NJ Department of Labor: labor market information
  273. New Jersey Business Action Center
  274. US Small Business Administration: New Jersey district
  275. USA.gov: business resources
  276. Google – product structured data
  277. Google – content accessibility guidance

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