Glanbia Performance Nutrition: 1,200+ SKUs on 10 Amazon EU marketplaces, checked against the label
How label checks in Python, keyword-led listings and suppression fixes supported a sports-nutrition portfolio that grew from ~€620K to €1M+ a day.
Gazi El Halabi ·
In short
At eStoreLabs I ran catalogue and e-merchandising for Glanbia Performance Nutrition on 10 European Amazon marketplaces, on Seller Central and Vendor Central. I built a Python check of every listing against the product label, rebuilt listings around real search demand and restored 50+ suppressed ASINs. During my time on the account, daily portfolio revenue grew from about €620K to over €1M.
The problem
A sports-nutrition portfolio of 1,200+ SKUs, sold on ten European Amazon marketplaces through Seller Central and Vendor Central at the same time. At that scale, a small catalogue error is never small: it repeats across markets and languages. And in nutrition, a listing that does not match the label is not a typo. It is a compliance problem.
Context
From March 2025 to March 2026 I was Digital Specialist at eStoreLabs on the Glanbia Performance Nutrition account, working remotely from Spain. The portfolio included brands such as Optimum Nutrition, BSN and Isopure, across the EU4/EU8 marketplaces. Before that, at the same agency, I managed content for L'Oréal Iberia's Amazon business.
Approach
One catalogue, four workstreams: make every listing match the label, rebuild listings around how shoppers actually search, keep every ASIN live in every market, and launch new products without introducing new errors.
Tools
| Need | Tool |
|---|---|
| Seller and vendor operations | Seller Central and Vendor Central |
| Product content management and syndication | Salsify and Opera |
| Keyword research and market data | Helium 10 and Keepa |
| Listing checks against the label | Python script |
Process
- Check every listing against the label. I wrote a Python script that compared each SKU's Amazon title, bullet points and description with the product's label PDF and flagged ingredient discrepancies for review. With nutrition products there is no room for approximations: what the listing says has to be what the label says. Together with the rest of the catalogue automation, manual catalogue errors fell by 40%.
- Rebuild listings around real search demand. The listings were not well optimised, so I researched the best keywords for each SKU with Helium 10 and Keepa and rewrote the content around them. I also improved the main images and reworked the A+ hero modules so that the product itself is visible, not only a person training.
- Restore suppressed listings. 50+ suppressed ASINs were restored and complex variation issues resolved. One case shows why this takes judgment, not just tickets: a powdered energy shot, sold as a powder to be mixed and not ready to drink, was treated by Amazon as a ready-to-drink energy drink, like the canned drinks in that category, and was suppressed in several marketplaces at once. The work was to make the product's real format unambiguous in its listing data, market by market.
- Launch new products cleanly. New launches went out with their content, flat files, attributes and compliance checked before going live, governed alongside account health and EU compliance.
Result
- Daily portfolio revenue grew from about €620K to over €1M (+61%) during my time on the account.
- Conversion rate up 20% after the content and listing work.
- Markets grew 25–40% following the catalogue optimisation programme.
- 50+ suppressed ASINs restored.
- Manual catalogue errors down 40% through automation.
- 1,200+ SKUs managed across 10 marketplaces, on Seller Central and Vendor Central.
“His expertise in Amazon marketplace management is outstanding, particularly when it comes to listing optimization, variation management and resolving complex listing suppressions.” Anna Hoang, E-commerce Manager, Glanbia Performance Nutrition.
Lessons
- In regulated categories, the label is the source of truth. Check the listing against it automatically, not by eye, and do it for every SKU.
- Classification is content too. How Amazon reads a product decides whether it can be sold at all. A suppression is often a data problem, not a policy one. More on that in suppressed Amazon listings.
- Show the product. The hero image and the A+ hero module have to show what the shopper is buying.
- A fix only counts when it lands in every market. With ten marketplaces, the same correction has to reach all of them. That is the operating model described in Amazon marketplace management in Europe.
Since September 2026 I work as an independent Amazon and marketplace consultant, and I am open to senior roles: see my profile as an Amazon Marketplace Manager.
Stack: Seller Central · Vendor Central · Python · Helium 10 · Keepa · Salsify · Opera · A+ / B+ content
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