Two Amazon product managers, Scott Nelson and Patricia Fabregat, joined Ashley Carson, senior director of brand management at Acosta Group, for a discussion moderated by Matt Leigh, SVP of customer success at Syndigo.
Their conversation surfaced 3 practical takeaways for suppliers working to improve the accuracy, governance, and discoverability of their product content.
1. Fewer Entry Points Can Reduce Data Errors
Amazon is using the Global Data Synchronization Network (GDSN) to streamline how suppliers provide the supply chain data needed for new-item setup, ordering, receiving, and fulfillment. More than 2,000 vendors connected and published through GDSN in the past year.
The data includes package hierarchy, GTINs, quantities, weights, dimensions, pallet data, and layer data. Amazon’s roadmap applies fulfillment and receiving requirements across 2025–2026, with catalog enrichment expanding during 2026–2027.
Supply Chain Connect in Vendor Central helps suppliers assess ASIN coverage, completeness, and validity, view GDSN values alongside legacy fields, and identify issues that need attention. The practical benefit is a clearer view of where product data may be incomplete or inconsistent before those issues affect downstream operations.
2. Your Data Can Outrank Other Contributors on Your Listing
Brand Elevation gives brands more control over which product content Amazon prioritizes. Brand administrators can rank Partner Accounts in Brand Registry, while suppliers can rank Vendor Codes within those accounts in Vendor Central.
For each attribute, Amazon evaluates contributions in ranked order and uses the highest-ranked valid value. If the first-ranked Vendor Code has no value for an ASIN, the system moves to the next source in the hierarchy.
The ranking hierarchy matters, but it does not replace strong content governance. Suppliers still need complete, current attributes, clear ownership, and a process for checking what is live rather than assuming that submitted content was published as intended.
3. Incomplete Data May Disappear from Consideration
As AI-assisted shopping expands, incomplete product data can limit how confidently a product is surfaced or evaluated. If the available content does not address the requirements in a shopper’s request, an AI system may not have enough information to recommend the product.
That raises the bar for content completeness. Brands need structured product information that clearly addresses relevant benefits, ingredients, compatibility, and other decision criteria. The goal is not only to rank in search, but also to make the product understandable to the systems helping shoppers compare options.
Turn the Takeaways into a 30-Day Plan
Across all 3 takeaways, the operating model is consistent: maintain complete, structured, current data; assign 1 accountable owner; use a repeatable workflow; and measure what is live, not only what was submitted. Tools that compare submitted content with published content can help teams identify gaps and resolve them before they affect the customer experience.
For the Next 30 Days:
- Review a focused set of high-priority ASINs and compare the product information submitted with what is currently live.
- Map manual steps and duplicate entry points where incorrect or inconsistent data can enter the process.
- Confirm that required and relevant optional attributes are accurate, complete, and supported by the product information available to the brand.
The roadmap will continue to evolve, but the immediate work is clear. Suppliers that improve data quality, ownership, and monitoring now will be better prepared as Amazon expands these capabilities. Watch the full panel discussion for the complete conversation and Q&A.
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