Imagine a retailer running one catalogue across the UAE and Saudi Arabia. The campaign shows a discounted coffee machine. A shopper clicks, lands on a different colour, sees another currency and discovers that the discount needs a code at checkout. Marketing sees a visit. Merchandising sees a valid product. The shopper sees a broken offer.
Product feed quality is the consistency of the commercial offer as it moves from your catalogue into advertising and back to the buying journey. An uploaded file is only one part of that journey. Before increasing spend, establish whether its product, price, variant and market survive the click.
My starting point would be a small offer audit with merchandising, ecommerce and the campaign owner in the same room. Do not begin by rewriting every title with AI. First find the differences that could make a sensible customer abandon the purchase.
Product feed quality starts with the exact offer
A feed record should describe something the customer can buy under the stated conditions. A product family is not a specific size. A base price is not automatically the advertised promotional price. A warehouse item is not necessarily available through the delivery route offered to this shopper.
Google's product data specification requires submitted price and currency to match the landing page and checkout, and availability to agree across the relevant surfaces. It also requires accurate product identification and variant attributes. Those requirements make the audit practical: compare specific fields instead of debating whether the feed “looks optimised.”
Build one row per offer in your audit sheet. Record product identifier, variant, market, language, submitted price, sale conditions, availability, landing URL and time checked. Add the value actually seen on the page and at checkout. Finish with discrepancy, accountable owner and retest evidence. Screenshots help explain a defect; they should not replace its underlying record.
1. Choose the products that can expose a failure
Start with a deliberately mixed sample, not the neatest category. Include a high-spend product, a new arrival, a discounted item, several sizes or colours, a preorder, a recently sold-out item and a product offered in both Arabic and English. Add an item that uses different pricing in the UAE and Saudi Arabia.
This is a diagnostic sample, not a claim about the whole catalogue's accuracy. Its purpose is to expose failure types. Once you find one, measure its reach across all affected records. A colour mismatch on one shoe may originate in a mapping rule applied to an entire footwear range.
For each record, follow the exact submitted link in a fresh browser session. Check which variant is selected without further interaction. Then repeat after choosing another market or language and opening the original link again. This separates the intended offer from preferences silently remembered in a customer's browser.
2. Keep market and language attached to the record
A regional catalogue needs more than translated product names. The URL, currency, fulfilment promise and actual buying options must describe the intended market. An Arabic title attached to a link that opens a different offer is a mapping defect, not a translation task.
Google's price guidance requires matching submitted and checkout prices, and prohibits location-based landing-page price changes outside its supported regional pricing arrangements. A retailer using automatic country detection should therefore test the submitted URL independently of the assumptions built into that detection.
Ask merchandising to approve equivalent meaning in Arabic and English for capacity, pack size, compatibility and included accessories. Keep model numbers and identifiers intact. AI can propose cleaner copy, but it should not infer that an accessory is included or translate an uncertain compatibility claim into a confident one.
Maintain a rejected-claims queue for missing supplier evidence. A blank optional detail is easier to repair than a persuasive false specification copied into thousands of listings. The catalogue owner decides what the product is; the writing tool does not.
3. Compare the visible page with its machine-readable offer
The page a shopper reads and the structured data a platform reads can disagree. A promotion may change visible text while an older price remains in the page's Product or Offer markup. A variant selector may update the image while leaving the structured availability attached to the default item.
Google's supported structured data guidance maps product markup to Merchant Center attributes and identifies price, currency, availability and condition as useful inputs for automatic updates. Have the technical owner inspect those values for the exact landing URL, then compare them with the submitted record and the customer-visible offer.
Do not stop when the structured JSON parses. Valid syntax can describe the wrong colour or an expired discount perfectly. The pass condition is agreement about the same offer. A page template fix should be retested across affected product types, including sale items and unavailable variants.
4. Test the change, not just the snapshot
Static agreement at noon does not prove the feed survives a promotion starting later. Run controlled checks around price changes, sale expiry and availability updates. Record when the source changed, when the website changed, when the feed was submitted and when the platform reflected it.
Define who can pause promotion of an offer when those systems disagree. Marketing needs a usable escalation route to merchandising and engineering, with enough detail to distinguish a stale export from an incorrect source value. Resubmitting the same wrong record is activity without repair.
Google says automatic product updates are intended to address temporary mismatches and do not replace regular product data updates. Treat them as a supporting mechanism. They cannot establish which commercial promise your own teams intended to make.
The adjacent note on retail inventory accuracy deals with the upstream stock definition. This audit asks a different question: did the advertising channel publish the correct offer from that definition, and did the buying journey honour it?
Give the budget decision a better denominator
Report eligible offers separately from submitted offers. Track unresolved mismatches by affected spend, product importance, market and age. Add the time taken to propagate a commercial change and the number of defects that returned after an apparent fix. Avoid presenting a higher approval rate as proof of higher sales.
Only then investigate title relevance, images, bidding and demand. Clean data cannot guarantee a profitable campaign, but broken offers make performance harder to interpret. A low conversion rate means something different when a customer reaches the correct product at the advertised price.
This is useful work for marketing automation in Dubai: connect a commercial change to a verified customer-facing result. Product feed quality should be owned as part of trading, with a clear repair path and evidence after every material change. Buy more attention when the offer is ready to receive it.