AI in the Product Data Feed: Enriching Attributes, Optimising Titles, at Scale

Every catalogue campaign is only as good as the data it is built from. Titles, attributes, categories, images – dynamic product ads are assembled from them, Advantage+ Shopping sorts your range by them, every retargeting builds on them. The problem: with large product ranges, clean feed maintenance is simply impossible to do manually. This is exactly where AI comes in – not as a magic wand, but as a tool that handles language and structure work at a volume no team can manage by hand. This article shows the use cases that prove themselves in practice, the guardrails without which it becomes risky, and a workflow that combines both.

The feed is the data foundation – not the appendix

In catalogue marketing an uncomfortable truth applies: your ad is not designed, it is built from data. The product feed supplies title, image, price and attributes – and every weakness in it is delivered a thousand times over. Dynamic product ads show users exactly the products your feed describes; what that foundation looks like, we described in the article Dynamic Product Ads & Catalogue Setup. Advantage+ Shopping decides based on the same data which products to show to whom. And retargeting also draws on the catalogue when it brings viewed products back to mind.

The feed is therefore not a technical appendix, but the shared data foundation of all catalogue campaigns. Improve it, and you improve every one of these campaigns at the same time – which is why we treat Feed Hacking as a discipline of its own, connecting product data with real performance KPIs and building dynamic segments from it. The prerequisite, however, is that the base data is right: complete attributes, titles that sell, consistent categories. And this is exactly where large ranges regularly fail.

Why manual feed maintenance doesn’t scale

With fifty products you can write every title by hand. With five thousand you can’t. Variants multiply the problem – every size, every colour is its own data record that has to be correct. Add to that the dynamics: ranges change, seasonal goods come and go, suppliers deliver raw data of highly varying quality. One sends clean attributes, the next packs everything important into running text, the third abbreviates colour names according to internal logic.

The result looks the same in many shops: the top sellers are well maintained, the long tail is neglected. Material details are missing there, titles start with article numbers, categories contradict each other. For delivery this means: part of the range competes with the handbrake on, because the platform cannot classify it cleanly.

The classic way out – a content team working through the feed – only works to a degree. By the time the team has been through it once, the range has long since turned over again, and the costs grow linearly with the number of products. This kind of work – repetitive, language-based, at high volume – is exactly the case modern language models are built for: they scale with the range without the effort growing to the same extent.

What AI can concretely do in the feed

The following use cases have proven themselves in practice – not as a gimmick, but as work that previously simply remained undone. A clear prioritisation makes sense here: first come the fields that influence delivery the most – titles and mandatory attributes – then the finer points such as channel variants and image checks.

Rewriting titles based on search logic. Supplier titles follow internal logic; users and algorithms need search logic: product type, brand and purchase-deciding attributes up front, because titles get cut off depending on the placement. A cryptic raw title thus becomes a title that clearly names product, material and colour. AI builds such titles from raw data and description following a fixed pattern – for ten thousand products in a single run, consistently to the same specifications.

Extracting missing attributes from descriptions. Material, colour, fit or occasion often sit in the running text of the product description, but not as a structured attribute in the feed. For the platform, the information therefore doesn’t exist. Language models are strong at exactly this extraction work: they read the description and fill the empty attribute fields – field by field, product by product.

Unifying categorisation. Shops that have grown have categories that have grown: the same product is called knitwear here, jumper there, tops elsewhere. AI can assign products to a unified taxonomy and flag contradictions – the basis for product sets and automatic groupings to work cleanly.

Varying description texts per channel. What a search channel needs in sober information differs from what works in a Meta catalogue ad. From one master description, channel variants can be generated: factual and complete here, condensed and benefit-oriented there – without anyone writing every variant by hand.

Checking image selection. Multimodal models support image checks: does the main image match the description? Is it a cut-out or a busy environment shot? Is the matching image missing for a colour variant? Such checks don’t replace a creative team, but they find outliers in ranges nobody reviews in full.

How much AI takes over on the creative side – and where humans remain indispensable – we described in the article AI Creatives for Meta & TikTok Ads. The same basic attitude applies to the feed: the machine delivers volume, the human sets the framework and keeps the judgement.

Limits and guardrails

As big as the leverage is – without guardrails, AI in the feed quickly becomes a risk. Three rules have proven non-negotiable.

Always spot-check AI output. Language models hallucinate, and with product facts that is poison. An invented material claim – pure cotton on a polyester shirt – is not a question of style, but produces returns, disappointed customers and, in doubt, legal problems. That is why no AI run goes live unchecked. Spot checks follow a fixed scheme, with particular attention to fact fields such as material, measurements and care instructions. A useful rule of thumb: everything a customer could complain about gets checked.

Rules before models. Whatever can be done deterministically does not belong in the AI. Price formatting, availability logic, appending the brand name to every title – for these there are rules that always work the same way and transparently. AI comes in where language has to be interpreted. Whoever reverses this order buys unpredictability in places where it earns nothing.

Versioning and rollback. Every enrichment run needs a documented state before and after. If a systematic error shows up in the spot check – say, a prompt that transfers colour names incorrectly – you must be able to withdraw the entire run, instead of repairing ten thousand fields by hand. That sounds like dry IT hygiene, but in practice it decides whether you can automate boldly or have to fear every change.

The workflow: export, enrichment, review loop, import

A four-stage cycle has proven itself, deliberately working outside the live system:

1. Export. The current feed is pulled as a working copy – the AI never works directly on your shop’s live data.
2. Enrichment. First the deterministic rules run, then the AI takes over the language and interpretation work: titles, attributes, categories, channel variants.
3. Review loop. Automatic plausibility checks catch the rough errors – field lengths, mandatory fields, prohibited terms. Then comes the human spot check with a focus on fact fields. If the run fails, it goes back into enrichment, not into the shop.
4. Import. Only the reviewed state is imported, versioned – with the option of reverting to the previous state at any time.

The rhythm is decisive: feed enrichment is not a project you complete once, but an ongoing process. New products keep arriving, suppliers change their data formats, channels adjust their requirements. Whoever has built the cycle cleanly once lets it run continuously – new products pass through it automatically, existing data is refreshed on a regular schedule. This way data quality rises with every pass, instead of decaying again after the project ends. And that is exactly when the basis emerges on which feed segments built on performance criteria unfold their full value: enrichment delivers the clean data, segmentation turns it into strategy.

Conclusion

The feed is the data foundation of all catalogue campaigns – and AI makes its maintenance scalable for large ranges for the first time. Titles built on search logic, extracted attributes, unified categories, channel variants and image checks are the use cases with the greatest leverage. But the leverage only works with guardrails: spot checks instead of blind trust, rules before models, versioning instead of a one-way street. Whoever sets this up as an ongoing cycle improves not one campaign, but the foundation under all of them. In the free account check we look not only at your campaign setup but also at where your feed stands today – and where enrichment would make the biggest difference.