What product data enrichment means
Enrichment adds useful information to a product record: materials, dimensions, compatibility, assigned identifiers, clearer descriptions, relevant images and appropriate categories. The aim is an accurate, consistent listing that helps someone decide whether the product meets their needs.
Cleansing and enrichment often happen together. Cleansing fixes existing values, such as mixed units, duplicate records or an incorrect size. Enrichment supplies missing detail. Correct errors before building more copy or channel exports around them.
More information is only useful when it is supported. A missing specification should lead to a supplier check or a review task, rather than a plausible guess.
What enrichment adds to a listing
Take a clothing listing that arrives from a supplier as "BLK JKT W 12". That code may be enough for an internal system, but a shopper needs to know what the jacket is for, which size is being offered and what makes it suitable for their use.
Channel requirements provide a useful starting point. Google's Merchant Center specification sets apparel requirements that depend on the destination, product and market, including colour, gender, age group and size. Supply a valid GTIN when required and one has been assigned; never create an identifier to fill an empty field.
| Field | Supplier record | After enrichment | Evidence to check |
|---|---|---|---|
| Title | BLK JKT W 12 | Women's waterproof hiking jacket, black, UK size 12 | Confirmed product type, performance and variant |
| Variant | Size only | Black · UK 12 · female · adult | Supplier variant record and sizing system |
| Material and performance | Missing | Recycled polyester shell, taped seams, 10,000 mm waterproof rating | Manufacturer's specification for this exact product |
| Identifier | Missing | Assigned GTIN for the correct variant, if available | Supplier barcode list or manufacturer record |
| Category | Outerwear | A suitable category for each destination | Channel taxonomy and product classification |
| Description and fit | One line from the price list | Intended use, verified specifications, care and a reviewed fit note | Manufacturer guidance and consistent customer feedback |
| Images | One packshot | Packshot, on-model view and useful detail shots, with appropriate alt text | Assets that accurately show this product and variant |
The product has not changed; the explanation has. Customers can now compare relevant details instead of decoding a supplier abbreviation. Accurate attributes also support channel filters where those attributes are used. That improves the information available for discovery without guaranteeing a particular search or AI ranking.
Why poor product information matters commercially
Product information can end up spread across supplier files, spreadsheets, an ERP, a PIM and the store's CMS. When those records are updated separately, a correction in one place may never reach the others.
A description stays unchanged after a supplier revises the specification. One channel shows a different size from the product page. Support keeps answering a question that the listing never addresses. These are useful signals for deciding where enrichment should begin.
In Syndigo's 2026 consumer study, 43% of UK shoppers reported recently abandoning a purchase because they could not find enough information. The study covered 8,736 adults across six countries; the 43% figure is its UK result, not a percentage of all ecommerce purchases.
Returns make inaccurate information costly after the sale as well. ZigZag and Retail Economics estimated the value of UK online returns at £27.3 billion in 2024. That is the value of returned goods, not the bill for processing them, and it includes returns for many reasons. Enrichment is most relevant where the customer received something different from what the listing led them to expect.
Use your own data to find those problems. Repeated fit complaints, compatibility questions or returns marked "not as described" can point to information that needs correcting or explaining more clearly.
How AI shopping changes the job
AI assistants can draw on product feeds, public retail pages, reviews, images and other sources. They do not work only from the structured fields in your own catalogue. OpenAI's shopping guidance describes using merchant and third-party information alongside the shopper's request and relevant context.
A missing attribute does not automatically remove a product from every answer. It can, however, leave the assistant without clear evidence that the item meets a specific requirement. If someone asks for a waterproof jacket under £150, an unsupported waterproof claim is no better than a missing one. The useful response is to verify the specification and make it easy to find.
Price and stock consistency matter too. Google documents feed-to-page conflicts as a potential cause of eligibility or display problems. Avoid assuming that every AI assistant applies the same rejection rule; keep the offer accurate wherever a customer or platform reads it.
The UK opportunity is growing. Adobe's UK holiday report found that traffic from AI sources to retail sites rose 329% during November and December 2025 compared with the same period in 2024. Adobe also noted that the overall volume was still relatively small. The figure measures referral growth, not AI's share of retail sales or a guaranteed conversion lift.
For European retailers, product research through web search and merchant feed integrations are different routes. A direct OpenAI feed is optional when ChatGPT already crawls your site. OpenAI's merchant programme still describes its shopping rollout as live in the US, so confirm the integration's market coverage before investing in a feed. European product pages can still be relevant to ordinary web search and research.
Enrichment gives both shoppers and software better evidence to work with. Whether it changes visibility, clicks or sales needs to be measured for your products and markets.
Where to find the missing information
Start with sources that identify the exact product: supplier records, manufacturer specifications, manuals and assigned identifiers. Then use customer feedback and search behaviour to find the questions those records leave unanswered.
Different sources serve different purposes. A technical document can establish a material or performance rating. A review can reveal that customers struggle with the fit. A competitor's page can suggest what to investigate, but it is not proof that your product has the same specification.
| Source | Useful information | How to use it carefully |
|---|---|---|
| Supplier and first-party records | Specifications, variant details, manuals, support questions and returns reasons | Match records to the exact item and resolve conflicting versions |
| Product databases and identifiers | Standardised details or identity checks using GTINs and manufacturer references | Coverage varies; a barcode lookup does not guarantee a complete or authoritative specification |
| Compliance records | Traceability, safety information and category-specific declarations | Use the documentation required for that product and market; never infer compliance |
| Reviews and customer feedback | Recurring questions, fit experiences and practical use cases | Separate customer opinions from verified specifications; review patterns before changing advice |
| Search and intent data | Terms shoppers use, common questions and zero-result site searches | Use it to prioritise explanations, not as evidence for a product claim |
| Competitor and public web research | Comparison topics and possible gaps in your own listing | Verify facts against your own product; do not copy another seller's content or specifications |
| Images | Visible features, pattern, shape and suggested descriptive tags | Review visual inferences; photographs cannot reliably establish fibre composition, dimensions or performance ratings |
Check compliance by product and market
The EU General Product Safety Regulation has applied in the EU and Northern Ireland since 13 December 2024. It covers products within its scope, with exemptions and interactions with sector-specific rules; it does not apply to all goods or to sales confined to Great Britain. The UK government's GPSR factsheet explains the Northern Ireland position.
Where the rules apply, traceability, product identification and relevant safety information need to be checked as part of catalogue work. The EU's Ecodesign for Sustainable Products Regulation also introduces a Digital Product Passport framework. Its information requirements depend on the product and the applicable rules; it is not a universal passport requirement for every product today.
A practical enrichment process
Treat enrichment as a recurring workflow. The following eight steps help you improve a catalogue without losing track of what changed or why.
- Audit the catalogue. Start with important products and categories, checking missing attributes, weak descriptions, image gaps and repeated customer questions. A free catalogue audit can help identify a starting point.
- Clean existing records. Correct conflicting values, mixed units and duplicates. Agree which attributes each product category needs.
- Match products and variants. Use stable internal IDs, assigned GTINs, manufacturer part numbers and brand details to avoid attaching information to the wrong item.
- Fill gaps from reliable evidence. Record the source of each added value. Flag unresolved fields rather than guessing.
- Write the explanation. Turn verified attributes into titles, descriptions, bullet points and answers that address shoppers' questions.
- Review changes. Prioritise human review for claims about safety, health, sizing, compliance and performance, as well as conflicting or uncertain information.
- Publish for each destination. Map approved content to channel requirements and localise language, currency, size conventions, delivery information and consumer-facing tax treatment for the market.
- Monitor and repeat. Track errors, customer questions, return reasons, relevant traffic and sales as new products and updates arrive. Record which changes were published and when.
What ChatGPT can help with
An assistant such as ChatGPT or Claude can help extract candidate attributes from documents, draft descriptions and suggest categories. Give it the relevant source material and a clear rule: use supported facts, identify uncertainties and leave unknown values unresolved.
For one listing, that can be a useful way to produce a first draft. At catalogue scale, you also need dependable product matching, validation, version history, review, publishing and measurement. Those functions can be built around a model through integrations and software; a manual chat does not provide the whole workflow by itself.
Keep updates tied to the source record
Suppliers add products and revise specifications. Prices change, and new feedback raises questions. An enrichment process needs a way to revisit affected records, not just produce a set of descriptions once.
Plan for review as well as generation
Writing thousands of drafts quickly is useful only if the team can validate and publish them reliably. Prioritise products by commercial importance and information gaps, batch similar work and give uncertain changes a clear review path.
Measure the outcomes you care about
Content completeness is one measure; it does not establish sales impact. Compare customer questions, relevant return reasons, traffic and conversion alongside it. For AI visibility checks, use repeated observations in the countries and languages you serve. A single answer is not a stable ranking.
How enrichment works with a PIM and feed tools
Enrichment is an activity, while a PIM is a system for managing product information. The roles overlap: many PIMs include tools for enrichment, validation, localisation and automation. Akeneo's PIM overview explicitly includes enrichment. Feed tools can also transform or supplement data.
The useful distinction is which part of the work each tool handles in your setup, and who is responsible for completing it.
| Function | Main responsibility | What to check in your setup |
|---|---|---|
| Product information management | Centralise, govern and distribute catalogue records; may also support enrichment | Which system holds approved values, manages versions and owns review |
| Feed management | Map, transform and deliver product data to channel formats | Validation, destination requirements, updates and any enrichment already built in |
| Enrichment workflow | Find, verify, add and improve product information | Evidence, matching, review and writeback, whether inside a PIM or a separate service |
If listings remain incomplete, look for the unfinished work before buying another layer of software. The answer may be a better supplier process, clearer ownership, an existing feature you have not configured or a dedicated enrichment service.
Where Tellis fits
Tellis works with a retailer's existing catalogue systems to audit product information and enrich it using specifications and shopper signals. Our agents help identify gaps, produce proposed improvements and return approved information to the system of record. The aim is a repeatable workflow that continues as the catalogue changes.
Changes are staged for review, with category rules and configurable options for publishing lower-risk updates. Integration and review requirements should be agreed around the systems you use and the types of products you sell.
In our BuyWholeFoodsOnline work, we reported 3× faster content production and a 54% increase in ChatGPT referral traffic. These are published client results, not a guaranteed outcome for another catalogue or proof that a single edit caused the traffic increase.
For teams managing a large range with limited capacity, the starting point is to identify where information is missing, unreliable or difficult to keep current. That gives you a concrete scope for enrichment and a way to judge whether the work is helping.
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Find the gaps in your catalogue
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Product data enrichment FAQ
How does product data enrichment work
It compares a product record with the information customers and channels need, then adds verified detail from appropriate sources. Match the product and variant first, record the evidence and review uncertain changes before publishing.
What is an example of product data enrichment
A supplier's "BLK JKT W 12" record becomes a clearly described jacket with verified colour, size, material, relevant images and an assigned identifier where available. The improved listing helps customers understand the offer; it does not guarantee inclusion in an AI answer.
Can AI enrich product data
Yes. It can help extract information, classify products and draft content. Check outputs against the source, keep unknowns visible and review claims that affect suitability, safety or compliance. A convincing sentence is not evidence.
Which enrichment tools should I use
Start with the work you need to complete and the capabilities your PIM, CMS and feed tools already provide. Look for reliable source matching, validation, review, writeback and monitoring. A dedicated service is useful when it fills a gap in that workflow.
Does better product data guarantee AI visibility
No. It gives assistants clearer information to evaluate, but recommendations also depend on the shopper's request, available sources, eligibility and the platform's selection process. Measure changes for your own products rather than treating enrichment as a guaranteed ranking mechanism.
Sources
- Google Merchant Center product data specification · Attribute and channel requirements.
- Syndigo State of Product Experience 2026 findings · UK shopper result and six-country study.
- ZigZag and Retail Economics returns research · Estimated value of UK online returns in 2024.
- Adobe UK holiday results · UK AI referral growth during November–December 2025.
- OpenAI shopping guidance and merchant programme · Product information sources and feed access.
- UK government GPSR factsheet · Scope and Northern Ireland application.
- European Commission ESPR guidance · Digital Product Passport framework.
- Akeneo PIM overview · PIM functions, including enrichment.
- Tellis published client results · BuyWholeFoodsOnline metrics.