How to Handle Missing EAN/GTIN and Still List Products

Missing EAN/GTIN in product listings: why it matters more than ever

Missing EAN/GTIN in product listings is one of the most persistent data quality issues in ecommerce, and its consequences have grown sharper heading into 2026. According to research on product identifiers, as marketplaces and advertising platforms tighten their product matching algorithms, the gap between well-identified and poorly-identified products keeps widening in terms of visibility, ad eligibility, and conversion performance.

The core challenge is not just about having a barcode. It is about whether your product data pipeline reliably captures, validates, and routes identifier information before it reaches any sales channel. When that pipeline breaks down, teams end up in reactive mode, chasing disapprovals and manually patching feeds instead of scaling their catalog.

This guide walks through the practical reality of missing EAN/GTIN: when identifiers are truly required, what fallback strategies actually work, common validation pitfalls, and a structured workflow to keep products listed without accumulating long-term data debt. If your feeds already suffer from broader structural issues, start with our product data quality checklist to address foundational gaps first.

Understanding GTIN formats and why channels enforce them

Before diving into fallback strategies, it helps to understand what channels are actually looking for. According to GTIN definition standards, GTIN (Global Trade Item Number) is an umbrella standard maintained by GS1 that includes several formats:

• EAN-13: the most common format globally, used across Europe and internationally (13 digits)
• UPC-A: primarily used in North America (12 digits)
• GTIN-14: used for case and pallet-level identification (14 digits)
• EAN-8: compact format for small products (8 digits)

According to barcode basics research, each format includes a check digit, a calculated final number that validation systems use to confirm integrity. This is why you cannot simply invent a GTIN or repurpose an internal SKU in the GTIN field. Platforms run checksum validation, and invalid numbers trigger immediate errors.

Channels enforce GTIN because it enables accurate product matching. When platforms receive a valid GTIN, they can cross-reference the product against their global catalog, pull in enriched specifications, and serve higher-quality listings and ads. Without GTIN, the platform has to rely on weaker signals like title matching and attribute inference, which reduces confidence and often leads to limited performance warnings.

Industry standards are also evolving. The GS1 Sunrise 2027 initiative is transitioning toward 2D barcodes that can encode GTIN alongside batch numbers, expiry dates, and URLs, which means the importance of having clean, validated GTIN data in your product master will only increase. Teams that treat GTIN as optional metadata today will face more expensive remediation later.

When GTIN is required versus when you can use fallback identifiers

The rules vary by platform, but predictable patterns exist that you can build operational logic around. According to guidance on unique product identifiers, here is how major platforms handle identifier requirements:

General Platform Requirements

Research shows that products submitted without any unique product identifiers are difficult to classify and may not be eligible for all programs or features. If the product does have a GTIN assigned by the manufacturer, including this in your product data can significantly increase performance. According to platform data, online advertisers who follow proper GTIN requirements see a 40% increase in click-through rate and a 20% increase in conversions.

Products Without GTIN

Some products legitimately do not have a GTIN assigned. If you are the only seller of a product, if your product is a store brand, or if the product is custom-made, handmade, or vintage, it generally will not have a GTIN. For these products, you should:

1. Set the appropriate identifier flag (such as identifier_exists to false on some platforms)
2. Provide accurate brand and mpn attributes
3. Ensure your product titles and descriptions are detailed enough for algorithmic matching

Custom goods, handmade items, vintage products, and retailer-created bundles are the main categories where platforms accept no-GTIN submissions. But the burden of proof is on you. If platform systems detect that a GTIN exists for your product and you have not provided it, the listing will be penalized.

For teams managing large feeds, this creates a classification problem. You need to know, at scale, which products should have GTIN and which legitimately do not. A catalog validation framework can help you build those rules systematically rather than handling exceptions one by one.

Marketplace-Specific Considerations

Different platforms have varying degrees of flexibility. Some require a standard product identifier for most categories, but offer exemption paths for products that genuinely lack one. The exemption process is typically brand-specific and category-specific, and approval is not automatic.

The most common failure pattern is timing. Teams discover they need an exemption after the product is already scheduled for launch, which creates delays and missed windows. A better approach is to build identifier status checks into your pre-listing workflow, evaluated per brand and category before any launch date is set.

For more on channel-specific requirements, our product feed management guide for 2026 covers platform requirements in detail.

Common GTIN validation errors and how to prevent them

Even when teams have GTIN data available, validation errors account for a significant share of feed rejections. According to catalog validation research, understanding the most frequent failure modes helps you build preventive rules.

Invalid check digit. The last digit of any GTIN is calculated from the preceding digits. A single transposition error makes the entire code invalid. This is the most common cause of GTIN rejection and typically originates from manual data entry or poorly formatted supplier exports.

SKU in the GTIN field. Internal SKUs and GTIN have completely different structures, but data mapping errors frequently place one in the other's field. According to product identifier guidance, SKUs are not recognized by platforms and will not affect your product feed or search results properly. Since SKUs often contain letters or non-standard digit counts, these are usually caught immediately.

Parent GTIN applied to all variants. Products with size, color, or material variants should each have their own GTIN. Copying the parent product's GTIN across all variants triggers duplicate errors and causes matching confusion. This is especially common when catalog data is managed in spreadsheets rather than structured PIM systems.

Outdated or recycled GTINs. GS1 rules state that GTINs should not be reused for at least 48 months after a product is discontinued. Using recycled codes creates ghost matches where your new product gets linked to an old catalog entry.

Fabricated GTINs. Never invent a GTIN. Platforms validate against GS1 databases, and fabricated numbers lead to listing suspension or account-level penalties. If you do not have a GTIN, use the proper fallback path instead.

Building automated checks for these issues into your feed pipeline is essential. Lasso can run GTIN format validation, check digit verification, and duplicate detection as part of your standard feed processing workflow, catching errors before they reach any channel.

Building a reliable fallback identity layer

When GTIN genuinely does not exist for a product, your goal is to construct an alternative identity that is stable, trustworthy, and accepted by your target channels. According to best practices for product identifiers, here is how to build that fallback layer:

Brand plus MPN as primary fallback

For most channels, the combination of brand and mpn (manufacturer part number) is the first accepted alternative. Research shows that MPNs are useful when a product does not have a GTIN or when you are selling highly technical or customized items. But quality matters:

• The brand value must be the canonical manufacturer brand name, not your store name or a generic label
• MPN must be the actual manufacturer part number, not an internal reference code
• Both fields must be consistently formatted across your entire catalog

A product data cleansing guide can help you establish normalization rules for these fields so they hold up across channels.

Stable internal SKU as governance anchor

While SKU alone rarely satisfies external channel requirements, it is the critical link in your internal data governance. Your SKU strategy should follow three rules:

1. Immutability: once assigned, a SKU should never change
2. Uniqueness: no two products share a SKU, ever
3. No recycling: retired SKUs stay retired permanently

When GTIN data arrives later (from a supplier update, a GS1 registration, or a data enrichment process), a stable SKU lets you map it back to the correct product record without breaking existing channel integrations.

Documentation and traceability

For every product where you claim no GTIN exists, maintain evidence:

• Supplier confirmation that no manufacturer barcode is assigned
• Category-level documentation explaining why GTIN is not applicable
• Exemption approval records from platforms where relevant

Without this trail, the same products get re-investigated every quarter, consuming operations time with zero incremental value. If you are merging data from multiple suppliers, our guide on merging supplier catalogs explains how to maintain traceability through the consolidation process.

Category-level identifier policies

Not every product category has the same GTIN coverage rate. According to catalog management best practices, industrial parts, artisanal goods, custom-manufactured items, and locally-sourced products often have legitimate no-GTIN rates above 30%. Define category-specific rules that specify:

• Expected identifier pattern (GTIN required, fallback accepted, or no-GTIN expected)
• Which fallback fields are mandatory for that category
• Who owns exception approval and what SLA applies

This prevents your operations team from applying blanket rules that either block too much inventory or let too many poorly-identified products through.

A structured workflow to list products without GTIN and remediate over time

The best-performing ecommerce teams avoid the two extremes: blocking every product that lacks a GTIN (losing revenue on sellable inventory) and publishing everything regardless of identifier quality (accumulating feed errors and channel penalties). Here is a workflow that balances speed with governance:

Step 1: Classify at ingest. Every product entering your catalog gets one of three labels: gtin_required (a GTIN should exist and must be sourced), fallback_eligible (no GTIN exists and the product qualifies for brand+MPN listing), or needs_review (insufficient data to determine status).

Step 2: Validate per destination. Run channel-specific validation rules before export. Different platforms have different identifier requirements, and a one-size-fits-all check will either be too strict or too lenient. According to validation framework research, data quality controls should include required fields, completeness scoring, rules, and error flags you can act on.

Step 3: Auto-publish low-risk fallback cases. Products with verified no-GTIN status, complete brand+MPN data, and a stable SKU can be published automatically to channels that accept fallback identifiers. This keeps sellable inventory live without manual intervention.

Step 4: Route exceptions to human review. Uncertain cases go to a reviewer who owns that product category, with a defined SLA (for example, 48 hours for high-velocity SKUs, 7 days for long-tail items).

Step 5: Set a remediation clock. For products labeled gtin_required, define a strict deadline for sourcing the GTIN (for example, 14 days for active products). Track this as an operational metric, not just a data quality nice-to-have.

Step 6: Measure the business impact. Compare performance metrics (impressions, clicks, conversion rate, return rate) between fully-identified products and fallback-identified products. Research shows that properly identified products can see 40% higher click-through rates and 20% more conversions, making the case for investing in identifier completeness far more convincingly than abstract data quality scores.

Step 7: Feed results back to suppliers. Include identifier completeness in your supplier data quality standards. Suppliers who consistently deliver products without GTIN when one exists should know about it and have a path to improve.

Lasso supports this entire workflow natively. You can configure identifier classification rules, run channel-specific validation, route exceptions to review queues, and track remediation progress from a single platform. If your team currently manages this through spreadsheets and manual checks, it is worth exploring how automation changes the operational math. Check pricing or book a session to talk through your specific catalog challenges.