# What Is Catalog Enrichment in Ecommerce?

> What catalog enrichment is, what data it completes on each product and how to tell original data, inferred data and the human decision apart.

URL: https://clione.ai/glossary/catalog-enrichment/
Lang: en

A [product page](/glossary/product-page/) with a name, a price and a photo can be bought from, but it does not answer what the shopper is asking: what it is made of, what size fits, who it is for, what code identifies it. Catalog enrichment is the work that closes that gap, product by product. It is not the customer data enrichment of a CRM, which adds information about people, and it does not mean making descriptions longer: it is **completing and correcting the data of each product and knowing where each value comes from**. It matters to you because whatever you do not write, every reader has to guess, whether a person, Google or an AI assistant, and each one guesses in its own way.

## What it covers

Enriching a catalog touches four layers of data, and each one has its own readers.

- **Attributes.** Color, material, size, pattern, measurements: the [product attributes](/glossary/product-attributes/) that let people filter, compare and choose. The product data specification of [Google Merchant Center](/glossary/google-merchant-center/) organizes them by section and, for clothing and accessories, **requires color, size, gender and age group in free listings**.[^gmc-spec]
- **Identifiers.** The [GTIN](/glossary/gtin/), the brand, the [MPN](/glossary/mpn/) and the [SKU](/glossary/sku/) of each item, which say exactly which product it is and set it apart from similar ones. When a product is sold in several sizes or colors, each combination is one of its [product variants](/glossary/product-variants/) and carries its own identifier.
- **Text.** The [product description](/glossary/product-description/), the frequently asked questions, the title and the [meta description](/glossary/meta-description/), written from the data above and not in place of it.
- **Markup.** The product page's [structured data](/glossary/structured-data/), usually in [JSON-LD](/glossary/json-ld/), and the [product feed](/glossary/product-feed/) sent to each platform. Google explains that providing both structured data on the page and a Merchant Center feed **helps it understand your data and verify it**, and that in some experiences it combines the two sources.[^google-product-sd] That is why the page, the markup and the feed have to say the same thing.

## Original data, inferred data and the human decision

An enriched catalog is not measured only by how many fields are filled in, but by whether you can say where each value came from. There are three possible origins, and it pays not to mix them.

**Original data** is what only the person who makes or sells the product can provide: the barcode, the composition on the label, the measured weight, the warranty. It cannot be deduced from anything. Google is blunt about this for the GTIN: submit it only if you are sure it is correct, **do not guess it or make up a value**, and do not submit one for a product that does not have it. If you are the only seller or the product is your own brand, it will normally have no GTIN, and then the brand and the MPN are enough.[^gmc-gtin]

**Inferred data** is obtained from other data. It can be a simple deduction (the color, taken from the variant name "Navy / M"; the category, taken from the description) or a text a language model writes from the product page. Google reminds you that what an AI generates can contain inaccuracies, known as [hallucinations](/glossary/ai-hallucination/), and asks you to check it by hand before publishing it, including in the elements that appear in results: the title, the meta description, the structured data and the images' [alt text](/glossary/alt-text/).[^google-genai-content] Merchant Center goes a step further: **titles and descriptions written with AI are submitted separately**, in the `structured_title` and `structured_description` attributes, with the `digital_source_type` sub-attribute set to `trained_algorithmic_media`.[^gmc-ai-content] The platform itself makes you separate the inferred from the original.

**The human decision** is not a third kind of data, but the step that decides what gets published. Approving an inferred value does not turn it into an original one: it is still inferred, but now it has an owner and a date. That record is what lets you go back when a value turns out to be false, and that is why enrichment goes hand in hand with [content versioning](/glossary/content-versioning/).

## In a store

**Fictitious example.** A mountain clothing store enriches the product page of a waterproof jacket, navy variant in size M. This is the worksheet for a single product, with the origin of each field in plain view:

| Field                     | Value                             | Origin                                          | Who decides                                                                 |
| ------------------------- | --------------------------------- | ----------------------------------------------- | --------------------------------------------------------------------------- |
| GTIN                      | The one the manufacturer assigned | Original: label                                 | Nobody infers it; if it is missing, ask the manufacturer                    |
| Material                  | Polyamide exterior                | Original: composition label                     | The merchant copies it as is                                                |
| Waterproof rating         | Empty                             | No data                                         | Left empty until the spec sheet arrives                                     |
| Color                     | Navy                              | Inferred: variant name                          | The merchant confirms it                                                    |
| Pattern                   | Solid                             | Inferred: product photo                         | The merchant confirms it                                                    |
| Description               | 120-word text                     | Inferred: written with AI from the fields above | Reviewed before publishing; in Merchant Center, in `structured_description` |
| Frequently asked question | "Is it good in heavy rain?"       | Inferred: depends on the waterproof rating      | Not published while that field is empty                                     |

Look at the third row: **an empty field is better than a made-up value**, and a frequently asked question that depends on it waits too. Each tool handles the origin column in its own way; Clione, for example, marks as AI-inferred the quality scores it calculates for each product and publishes nothing to the store until you press Push.


Enriching is not generating descriptions in bulk. A new description written over a product page with no material, no size and no identifier sounds complete and leaves the same gaps. And Google warns that using generative AI to create many pages without adding value for the user may violate its policy against [scaled content abuse](/glossary/scaled-content-abuse/).[^google-genai-content] The order is the reverse: data first, then the text that tells it.


## How to check it

1. **Pick the ten products you sell the most** and open them next to Merchant Center's product data specification. Note which required and recommended attributes they are missing.[^gmc-spec]
2. **Mark the origin of each filled field**: original, inferred or "I don't know." The "I don't know" ones are the finding, because nobody can defend a value whose origin they do not know.
3. **Look for suspicious GTINs**: the same code on two different products, or a code on an own-brand product. If the product has no GTIN, Google asks you not to submit one.[^gmc-gtin]
4. **Compare the product page, its markup and the feed** for a single product: color, size and [price and availability](/glossary/price-and-availability/) have to match in all three.[^google-product-sd]
5. **Review the AI-generated text**: who approved it, when, and whether in Merchant Center it goes in `structured_title` and `structured_description`.[^gmc-ai-content]

## Related concept: PIM

A [PIM (product information management)](/glossary/pim/) is the system where product data is stored and managed; enrichment is the work done on that data, inside a PIM, in the platform's admin or in a spreadsheet. Having a PIM does not enrich anything by itself: it organizes the fields, but someone has to fill them in and decide where each value comes from. Nor is it the same as a [catalog audit](/glossary/catalog-audit/), which is the diagnosis beforehand: it says what is missing, and enrichment completes it.

To keep going, the [catalog and entities reading path](/learn/catalog-and-entities/) puts the rest of the terms in order, from the attribute to the [product graph](/glossary/product-graph/).