Every catalog has a number nobody wants to look at: the share of products where half the fields are empty. A brand exists, a price exists, and then the specification table is blank, there is one image instead of eight, and the barcode column is a column of nulls.

The usual answer is to buy a PIM. But a PIM is a place to keep product information, not a place where product information comes from. Move an incomplete catalog into a PIM and you get the same gaps in a better interface. The hard part of product data enrichment was never the storage. It is the sourcing: finding the specifications, identifiers, and media that your records are missing, for thousands of SKUs, without paying someone to open thousands of tabs.

This guide covers what enrichment actually involves, where the data comes from, and which parts of the process break first.

What Product Data Enrichment Is (and What It Isn't)

Product data enrichment is the process of taking incomplete product records and adding the information that is missing — specifications, attributes, identifiers, categories, descriptions, and images — so that each product is complete enough to be found, filtered, compared, and bought.

It gets confused with three neighbouring processes, and the distinction matters because the tools are different:

• Extraction turns a product page into structured fields. It creates a record from a source. See product data extraction for how that works.

• Cleansing fixes what is already there: typos, broken encoding, HTML left in a description, prices stored as text.

• Normalization makes existing values consistent: Blue, blue, and NAVY BLUE become one value; 42 EU, EU42, and 8 UK land in the same size scale.

• Enrichment adds what is not there at all. An empty field stays empty until data arrives from outside the record.

Cleansing and normalization work on your own data. Enrichment requires a source. That is the whole problem in one sentence, and it is the part most enrichment software leaves to you.

It is also worth separating the two halves of an enrichment project early, because they need different tools. Sourcing is finding the missing values and getting them into a structured file: matching, extraction, normalization of the incoming data. Application is deciding what happens to your catalog: which fields to overwrite, which source wins a conflict, how the result is reviewed and published. The second half belongs in your own systems — a PIM, an ERP, or the store platform itself. The first half is where projects stall, and it is what the rest of this guide is mostly about.

What an Under-Enriched Catalog Looks Like

The symptoms are consistent across industries:

• No identifiers. GTIN, EAN, UPC, or MPN missing on a large share of SKUs. Without them you cannot match your products to anyone else's, cannot list on most marketplaces, and cannot re-run an import without creating duplicates.

• Attributes buried in text. The title reads Bosch GSB 18V-55 Cordless Hammer Drill 2x2.0Ah Case. Voltage, battery capacity, tool type, and bundle contents are all in there. None of them are in a field.

• Thin specification tables. Five rows where competitors show thirty.

• One image. Or one image and a logo.

• Inconsistent categorization. The same product type sitting in three different branches of the tree because three different people added it.

Each of these has a direct cost. Faceted navigation and internal search only work on fields, so an attribute inside a title is invisible to your own filters. Google Shopping disapproves items with missing GTIN or ambiguous attributes, and marketplaces may reject or limit listings when required attributes are missing or invalid. Returns rise when buyers guess at dimensions, compatibility, or fit. And as product discovery increasingly happens through AI assistants and conversational search, a product with no structured attributes has nothing for a model to interpret — it cannot be compared, so it does not get recommended.

Before starting any enrichment project, measure it. Fill rate per attribute, per category: what percentage of SKUs in Power Tools have a wattage value, how many have more than three images, how many have a barcode. That table tells you what to enrich and in what order, and it becomes the metric you report against later.

Where Enrichment Data Comes From

This is the question that decides whether an enrichment project ships. There are five realistic sources.

Manufacturer and brand websites. The richest and most authoritative source. Official specifications, professional photography, model numbers, documentation, compatibility lists. If a product exists, its maker has described it properly somewhere. The limitation is coverage: you will find your top brands easily and struggle with long-tail suppliers.

Supplier and distributor sites. The most common case in practice. Distributors publish complete product information on their websites while sending you a PDF price list with a SKU and a number. The data you need is already public — it is just on a web page instead of in a feed.

Retailer product pages and marketplaces. Useful when the manufacturer publishes little, and useful for a different reason: they show you which attributes buyers in a category actually expect to see. If eight retailers all list "sleeve length" and you don't have that field, that is a signal about your schema, not just your data.

Syndication databases. Icecat, GS1, and category-specific content pools. Excellent coverage where they cover a category — consumer electronics, IT, office supplies — and effectively nothing outside it. Worth checking first, because when a database covers your catalog it is the cheapest option available.

Generation. Writing descriptions, inferring categories, and normalizing values with a model. This is a transformation of data you already have, not a new source of facts. More on the boundary below.

In most projects the answer is a combination: check a syndication database first, take specifications and images from manufacturer pages for the brands you carry, and fall back to retailer pages for the long tail. All three of the web-based options are the same technical job — turning public product pages into structured fields at scale.

The Enrichment Workflow, Step by Step

Steps 1 and 2 are decisions about your own catalog. Steps 3 to 5 are data collection work and can be handed to a data provider. Steps 6 and 7 happen inside your systems, where the enriched values meet your live products.

1. Audit the catalog

Fill rate per attribute, per category. Decide which gaps are worth closing. Missing barcodes on 200 SKUs you sell every day matter more than missing "country of origin" on 8,000.

2. Choose the source per segment

Different parts of a catalog need different sources. Split by brand or category rather than looking for one source that covers everything, because that source does not exist.

3. Match your products to the source

Connect each of your SKUs to the corresponding product page. This is where most projects stall — see the next section.

4. Extract the fields

Pull specifications, attributes, identifiers, descriptions, and images from the matched pages. Extract at the variant level: a jacket in five sizes and three colours is fifteen records, each with its own SKU and its own attribute values.

5. Normalize

Standardize units, value vocabularies, and category paths so that data from multiple sources reads as one dataset. Normalizing the incoming data is part of collection and should be done before delivery. Reconciling it with the vocabularies already used in your own catalog is a separate job, and it belongs to whoever owns the schema.

6. Merge with precedence rules

This step happens in your system, not in the dataset. Decide in advance which source wins. A typical hierarchy: your own verified data beats manufacturer data, manufacturer data beats retailer data, retailer data beats generated content. Never overwrite a populated field with a lower-priority value; fill empty ones only. Keep a source column on every enriched attribute so you can trace and roll back.

7. Keep it in sync

Enrichment is not a one-time project. Manufacturers add specifications, replace photography, and revise model numbers. Specifications and media change slowly enough for a monthly or quarterly refresh; prices and stock, if you take them from the same source, need daily runs.

Product Matching: the Step Most Enrichment Projects Fail At

Everything downstream depends on connecting your record to the right source record. Get one match wrong and you have written the wrong dimensions onto a live product page.

Matching works in tiers:

• Identifier match. GTIN, EAN, UPC, MPN, or ASIN. Exact, reliable, and available for a smaller share of your catalog than you would like. When you have it, use it and stop.

• Brand plus model number. Strong, but model numbers are written a dozen ways: GSB 18V-55, GSB18V55, GSB-18V-55. Normalize before comparing.

• Fuzzy title match. Brand, product type, and key attributes compared as text. Effective for a first pass, unreliable as a final answer. Anything below a confidence threshold goes to manual review rather than into the catalog.

Three traps worth knowing. Variants: a single source page often covers a product that exists in your system as twelve SKUs, and the match must resolve to the right one. Bundles: drill + two batteries + case is not the same product as the bare tool, even though the titles are 90% identical. Retailer-specific models: large retailers commission variants with unique model numbers that will never match anything.

Matching is also what makes enrichment reusable — the same mapping between your SKUs and external pages powers competitor price comparison later.

Attribute Extraction and Normalization

Pulling attributes out of titles and descriptions

Not every attribute has a field of its own. In most catalogs, a significant part of the useful information sits inside the title and the description body. Nike Air Max 270 Men's Running Shoes Black 42 EU has no colour field, no gender field, and no size field, yet the string contains all three.

Attribute extraction parses these out into separate columns. The difference is between a dataset you can filter and a dataset you have to clean first. The same applies to specification blocks written as prose: "Made from 320gsm brushed cotton, machine washable at 30°C" contains three attributes that belong in three fields.

Apparel and fashion: the hardest category to normalize

Apparel product information enrichment is where normalization earns its keep. Sizes exist in EU, UK, US, and numeric scales, and they differ by brand. Colours are marketing names — Sand, Oatmeal, Ecru — that need mapping to a filterable base colour while keeping the original for display. Materials arrive as compositions rather than single values. Fit, rise, and sleeve length are inconsistently named across retailers.

The rule that survives contact with real catalogs: keep the original value and add the normalized one in a separate field. Overwriting destroys information you will want back, and the source string is often what customers search for.

Where AI Helps and Where It Invents Specifications

AI is genuinely useful for a specific slice of enrichment work: normalizing values to a controlled vocabulary, mapping categories to a target taxonomy, extracting attributes from unstructured text, translating and rewriting descriptions, and generating channel-specific titles from structured fields.

What it should not be asked to do is supply facts. A model asked for the battery capacity of a product it has no data on will produce a plausible number. In an enrichment pipeline that number goes straight onto a product page and eventually into a return.

The workable division:

• Model transforms, rules validate. Every generated value passes a check — is the unit valid, is the value inside a sensible range for the category, does the size exist on that scale.

• Facts come from a source, prose can be generated. Specifications, dimensions, compatibility, and identifiers are extracted. Descriptions and titles can be written.

• Sample and review. Spot-check a percentage of each batch before it reaches the storefront, weighted toward high-traffic SKUs.

An AI product data enrichment process without a validation layer is a fast way to fill a catalog with confident errors.

Enrichment for Channels: Google Shopping, Marketplaces, On-Site Search

Different destinations demand different fields from the same catalog.

Google Shopping uses product identifiers such as brand, GTIN, and MPN, with requirements depending on the product and whether a manufacturer-assigned identifier exists. Missing or incorrect product data can lead to disapprovals or reduced visibility.

Marketplaces each publish their own required attribute list per category, and they enforce it at listing time. Enrichment for a marketplace is largely about meeting a schema you do not control.

On-site search and faceted navigation run on your own fields. Every attribute stuck inside a description is a filter your customers cannot use.

This is also where product tagging and enrichment get conflated. Tagging assigns labels to products for merchandising — sale, new arrival, gift guide. Enrichment adds factual attributes. Tags help you organize what you sell; enriched attributes are what make products findable in the first place, and no amount of tagging compensates for an empty specification table.

Content and Legal Boundaries

There is a real line between structured facts and creative content.

Specifications, dimensions, identifiers, materials, and category structures are factual product information. Collecting publicly available product data of this kind is generally permitted, and reusing structured facts is the normal basis of catalog work across the industry.

Descriptions and photography are a different matter — they are the source's content, protected as such. Copying a manufacturer's description verbatim into your storefront is both a legal question and an SEO problem, since duplicate descriptions across dozens of retailers give search engines no reason to rank yours. Use the source description as input and rewrite; use manufacturer imagery only where your supplier agreement permits it, which it often does for authorized resellers.

For the broader picture, see our guide on whether web scraping is legal.

Where the Data Comes From, and Who Does What

Enrichment is usually described as a software category, which hides the fact that it is two jobs with two different owners.

Your side is the catalog: what to fill, which values to trust, what gets published. That decision layer sits with your merchandising team and whatever system holds your products, whether that is a PIM, an ERP, or the store platform. No external service should be making those calls for you.

The other side is supply. Somebody has to read several thousand manufacturer, supplier, and retailer product pages and return the specifications, identifiers, attributes, and images that your records are missing, in a structure your systems can read.

That second job is what ShopScraping does. It is a product data extraction platform, not an enrichment platform — it supplies the external product data that catalog enrichment runs on. Pages from any source you name are turned into structured fields, normalized and deduplicated before delivery, one row per variant, delivered as CSV, XLSX, JSON, or a Google Sheet on the schedule you set. Attributes that exist only inside titles and descriptions are parsed into separate columns on request. If the destination is a store platform rather than an analysis workflow, catalog import shapes the same data into Shopify-, WooCommerce-, or schema-ready feeds.

What happens next — matching the file against your live products, applying precedence rules, reviewing, publishing — stays with you.

Tell us which attributes are missing and which sources to read, and we will prepare a sample from your actual catalog before you commit to anything. See what can be extracted, or get in touch.

FAQ

What is product data enrichment?

Product data enrichment is the process of adding missing information to existing product records — specifications, attributes, identifiers, categories, descriptions, and images — so that products are complete enough to be found, filtered, and compared. Unlike cleansing, which fixes existing values, enrichment requires data from an external source.

How is enrichment different from data cleansing?

Cleansing corrects data you already have; enrichment adds data you do not have. A product with a misspelled brand needs cleansing. A product with no specifications needs enrichment, and no software can perform it without a source to draw from.

Can product data enrichment be automated?

Largely, yes. Matching, extraction, normalization, and delivery all automate well. What does not automate away is the decision layer: which sources to trust, which fields to overwrite, and where to set the confidence threshold below which a match goes to human review.

Do I need a PIM to enrich product data?

No. A PIM helps you manage and distribute enriched data across channels, and it becomes valuable as catalogs and channel counts grow. But it does not produce the missing attributes. Many teams enrich first and load the result into whatever system they already run, including a spreadsheet-based workflow.

Where do enrichment attributes actually come from?

Manufacturer and brand sites, supplier and distributor sites, retailer and marketplace product pages, and syndication databases such as Icecat or GS1. Most catalogs need a combination, split by category or brand.

How often should enriched product data be refreshed?

Specifications, descriptions, and images change slowly — monthly or quarterly is usually enough. If you take pricing and stock from the same sources, those need daily or weekly runs.

Does a data provider enrich my catalog, or supply the data?

Two different services, often sold under the same word. An enrichment or PIM platform manages product information inside your business: workflows, approvals, channel distribution. A data provider supplies the missing values from external sources as a structured file. Most projects need both, and the file has to exist before the platform has anything to manage.

What is the best product data enrichment software?

It depends on which half of the problem you have. Enrichment and PIM platforms are good at storing, governing, and distributing product information. They are not sources of product data. If your gap is missing attributes rather than missing process, start with sourcing — decide where the data will come from, then choose the system that will hold it.