Ask a store owner whether their product data is good, and they will usually say it is fine. Ask them to filter the catalogue by material, or to list every product missing a GTIN, and the answer changes. The data was fine for the supplier, who wrote it for a spreadsheet, but not for a shopper trying to filter, a search engine trying to understand or a marketplace trying to match.
Enrichment is the work of closing that gap. It is often the most valuable and least glamorous part of an ecommerce project, because every channel, from your own site to Google Shopping to AI assistants, depends on it. This guide explains what enrichment covers, where the data comes from, how AI helps and how to keep it honest. It builds on Shopify metafields, bulk product upload and AI search and ecommerce visibility.
What enrichment adds
| Layer | Examples | Used by |
|---|---|---|
| Identifiers | GTIN, MPN, brand, SKU | Marketplaces, Google Shopping, matching |
| Category and taxonomy | Correct product type and channel category | Navigation, feeds, search |
| Attributes | Material, colour, size, dimensions, weight, capacity, compatibility | Filters, comparisons, feeds, answers |
| Titles | Structured, searchable names | Search results, ads; see title formulas |
| Descriptions | Clear, accurate copy | Product pages, feeds |
| Media | Images with alt text, additional angles, video | Pages, listings; see alt text |
| Compliance data | Origin, warnings, certifications, ingredients | Legal and marketplace requirements |
| Relations | Variants, bundles, accessories, replacements | Cross-sell, search, recommendations |
Not every attribute is worth the effort. Prioritise those that shoppers filter on, that channels require and that reduce returns. A short, well-chosen list per category is better than a long, patchy one.
Define the target schema
Before enriching anything, define what “complete” means for each category. A rug needs size, pile height, material, backing and care. A phone case needs compatible models, material and protection type. A mug needs capacity, material and dishwasher safety. Mark each attribute as required, recommended or optional, with allowed values or formats, and note where the channel requires it. This schema is the specification for the project and the basis for measuring completeness.
| Attribute | Required? | Format | Allowed values |
|---|---|---|---|
| Material | Required | List | Linen, cotton, polyester, blends with percentages |
| Colour | Required | List | Standard colour names, with a mapping from supplier colours |
| Dimensions | Required | Number with unit | Centimetres, with a conversion for inches |
| Care instructions | Recommended | Text or list | Machine washable, hand wash, dry clean |
| GTIN | Required for many channels | 8, 12, 13 or 14 digits | Validated check digit |
| Country of origin | Recommended | List | ISO country names |
Sources of data
- Supplier and manufacturer files. Often the best source, if you can get structured data. Ask for it.
- Manufacturer websites and spec sheets. Useful for verification, within copyright and terms.
- Industry databases and GS1 data. For identifiers and verified brand data.
- Your existing text. Descriptions often contain attributes in prose that can be extracted.
- Your images. A vision model can read labels, packaging and visible features, though with caution.
- Customer questions and reviews. Reveal attributes shoppers care about.
- Your team. For items with unique knowledge, a person adds the value.
Where AI helps
Language and vision models are good at turning messy input into structured output, which is exactly what enrichment needs. Typical uses include extracting attributes from descriptions (“100% linen, 140 x 220 cm” into material and dimensions), normalising values (“Navy”, “Dark blue” and “Midnight” into one colour), classifying products into a taxonomy, drafting titles and descriptions from attributes and reading text from images. They can also suggest likely values for missing fields, which is where the risk lies.
| Task | AI suitability | Guardrail |
|---|---|---|
| Extract values stated in the text | High | Require the supporting phrase; reject if absent |
| Normalise to controlled values | High | Map to an allowed list; send unmatched to review |
| Classify into taxonomy | High | Confidence threshold and sampling |
| Draft titles and descriptions | Medium to high | Use only structured facts; review |
| Read labels from images | Medium | Human check for regulated data |
| Infer missing attributes | Low | Do not publish inferred specifications without a source |
| Identifiers (GTIN) | Do not guess | Take from authoritative sources only |
The rule that keeps enrichment honest is that every published value should trace to a source: a supplier file, a manufacturer document, a phrase in the existing text or a person’s decision. A model that “fills in” a plausible material for a product with no stated material is creating a false claim, which can lead to returns, complaints and, in regulated categories, legal exposure. See generative AI in ecommerce: risks.
A pipeline
- Audit. Measure completeness per category and attribute against the schema.
- Collect. Gather supplier data and other sources, and link them to products by SKU or GTIN.
- Extract and normalise. Pull values from text, images and files, mapping to allowed values.
- Validate. Check types, ranges, units, allowed values and identifiers; compare with sources.
- Score confidence. High-confidence, sourced values flow through; low-confidence ones go to review.
- Review. A person checks a sample of auto-approved values and all flagged ones.
- Load. Update the platform through import, API or app, with a backup and a log.
- Monitor. Track completeness and errors, and re-run when new products arrive.
Our AI product data enrichment service runs this pipeline, with bulk product upload for loading and catalogue automation for ongoing maintenance.
Validation rules worth having
- Units are present and consistent.
- Numbers are within plausible ranges for the category.
- Colour and material values come from allowed lists.
- GTINs pass check-digit validation and match the product.
- Titles stay within length limits and contain required elements.
- No banned words, claims or prices in descriptions.
- Variants have consistent option names and values.
- Required attributes are not blank.
Keep it clean: ownership and maintenance
Enrichment decays. New products arrive with the old gaps, suppliers change their files, and well-meaning staff edit values by hand. Treat product data as a managed asset. Assign an owner for each category who decides what the attributes mean. Put the schema and the allowed values in a shared document. Make the pipeline run on every new product and on every supplier file update, and report completeness weekly. When a value is changed by hand, record who changed it and why, and protect it from being overwritten by the next automated run. A short monthly review of the lowest-scoring categories is enough to keep quality from slipping.
| Practice | Why |
|---|---|
| Category owner | Someone decides what is correct |
| Shared schema and value lists | Keeps people and tools aligned |
| Source field per value | Traceability when something is wrong |
| Locks on manual edits | Prevent automation overwriting verified data |
| Weekly completeness report | Shows drift early |
| Re-run on new products | New items meet the standard from day one |
Enrichment for marketplaces and feeds
Each channel has its own attribute requirements, and the same product may need different fields for Google Shopping, Amazon, eBay and Etsy. Keep one rich master record and map it to each channel, rather than editing each channel by hand. Where a channel requires a value you do not have, flag it for sourcing and hold the product back until it is complete, instead of publishing with blanks or guesses. Channel diagnostics, such as Merchant Center item issues, are a free quality check that tells you exactly which attributes are missing, so use them as a to-do list. See Google Shopping feed errors and eBay listing automation for examples.
Measure the gain
| Metric | Before and after |
|---|---|
| Attribute completeness | Share of required attributes filled, by category |
| Feed errors and warnings | Merchant Center and marketplace diagnostics |
| Filter usage and results | How often shoppers use filters, and conversion after filtering |
| Search visibility | Impressions and clicks for product queries |
| Conversion and returns | Especially for products where size and fit were unclear |
| Time to list a new product | Process efficiency |
A worked example
A store has 5,000 products. An audit finds that only 52 percent of required attributes are filled, 900 products lack GTINs and 1,400 have supplier-written titles that fail the channel’s rules. Manual completion would take about 6 minutes per product, or 500 hours. A pipeline that extracts and normalises from supplier files and text fills about 80 percent of the gaps automatically, leaving roughly 1,000 products with flagged items for review at 3 minutes each, which is 50 hours. Add 30 hours for set-up and quality checks. At $22 an hour, the cost is about $1,760 against $11,000 for manual work. After enrichment, feed warnings fall by around two thirds and filtered category pages show better conversion. The figures are illustrative.
Mistakes to avoid
- Enriching without a schema, producing inconsistent fields.
- Allowing AI to invent values.
- No source tracking, so errors cannot be traced.
- One-off projects with no process for new products.
- Overwriting good data without a backup.
- Skipping sample review.
Sources and further reading
Figures and rules in this guide come from the primary pages below. Platform limits and prices change, so open the page before you build on a number.
- Google Merchant Center Help: product data specification, attributes for Shopping feeds
- GS1 US: GTIN and product identification, product identifier standards
- schema.org: Product, structured data vocabulary for products