How to Merge Multiple Supplier Catalogs into One Clean Structure

Merge multiple supplier catalogs: why the real challenge is not importing data

Every ecommerce team that sources from more than one supplier eventually hits the same wall. Data arrives in different formats, with different naming conventions, and with conflicting values for the same product. If you try to merge multiple supplier catalogs by simply stacking spreadsheets, the result is predictable: duplicate SKUs competing against each other, overwritten values that were correct before the merge, and channel feeds that pass validation one day and fail after the next supplier refresh.

The root cause is rarely technical complexity. It is the absence of a governed merge framework. In this guide, you will learn a practical approach to schema mapping, identity resolution, field-level conflict resolution, and ongoing governance that keeps your unified catalog clean week after week.

If your supplier data arrives in inconsistent formats before you even start merging, our guide on standardizing supplier product data with AI is a useful first step.

Define a canonical schema before comparing any records

The most common mistake teams make is jumping straight into deduplication before their data is comparable. When one supplier labels a field "Product Color" and another uses "Shade," or when dimensions arrive in centimeters from one source and inches from another, matching logic built on raw data produces fragile, unreliable clusters.

The fix is to define one canonical product schema and map every incoming supplier feed into it before any matching runs. Think of this schema as the internal language your catalog speaks, regardless of how many external dialects feed into it.

A practical canonical schema covers at least five layers:

1. Identity fields -- internal product ID, GTIN/EAN/UPC, MPN, brand, and supplier-specific SKUs.
2. Commercial fields -- price, currency, stock quantity, lead time, minimum order quantity.
3. Merchandising fields -- title, category path, variant attributes (color, size, material), and media references.
4. Compliance fields -- product condition, age group, safety certifications, energy labels, and material composition where applicable.
5. Governance metadata -- source system identifier, ingestion timestamp, confidence score, and data lineage trail.

Locking these layers down before merge means you map once and export to any channel. Tools like Lasso simplify this with AI-assisted field mapping and validation gates, replacing brittle manual scripts with a repeatable, auditable process.

For teams managing large product taxonomies, aligning your schema with a well-structured product taxonomy for ecommerce makes downstream categorization significantly easier.

Treat catalog deduplication as identity resolution

Deduplication is not a text-similarity exercise. It is an identity resolution problem: determining which records across multiple feeds represent the same real-world product. Treating it that way changes how you design your matching pipeline.

A layered matching strategy works best:

Layer 1 -- Deterministic keys. Match on normalized GTIN plus brand, or MPN plus brand where GTIN is unavailable. These are high-confidence matches that can auto-merge safely.

Layer 2 -- Composite business keys. For products without standard identifiers, combine brand plus model plus a core specification tuple (such as size, material, or capacity). This catches products that lack GTINs but are clearly the same item.

Layer 3 -- Fuzzy and semantic matching. Use title similarity, description embeddings, and attribute-level comparison to propose candidate matches. These should populate a review queue, not trigger automatic merges.

Layer 4 -- Variant separation. Ensure your logic distinguishes true duplicates from valid variants. A red version and a blue version of the same product are not duplicates; collapsing them into one record destroys sellable options. Clear product variant modeling rules prevent this.

Layer 5 -- Confidence tiering. Assign confidence scores to every proposed match. Auto-merge above a high threshold, route mid-confidence pairs to human review, and block low-confidence pairs entirely.

Practical guardrails to maintain merge stability:

• Store both raw source values and normalized values for every merged field so you can audit decisions later.
• Make match rules idempotent: re-running the same data through the same rules should produce identical clusters.
• Version your rules so that when clusters shift, the business team can trace why.

If products are missing standard identifiers, which is a common blocker for reliable matching, our guide on fixing missing EAN and GTIN issues covers practical remediation strategies.

Resolve conflicts with field-level precedence, not a single master supplier

After deduplication clusters records, the next challenge is survivorship: deciding which value wins for each field when sources disagree.

The instinct to pick one "master supplier" and trust everything from that source is tempting but flawed. A supplier who provides excellent product specifications may have unreliable pricing, while your ERP holds authoritative stock levels but lacks rich merchandising content. The answer is a field-level source-of-truth matrix.

Here is how a practical precedence matrix looks:

• Price and availability: ERP or PIM system first, with a validated supplier fallback.
• Brand name and official part numbers: manufacturer or brand-owner feed takes priority.
• Dimensions and weight: source with the most recently verified timestamp and lowest historical error rate.
• Title and description: curated or generated content layer, but only after factual fields (identifiers, specs) have passed validation.
• Product media: select the highest-resolution primary image, retain alternate images from secondary sources.

When values conflict, apply resolution modes explicitly:

1. Trust-ranked overwrite -- the highest-ranked source wins outright.
2. Most-recent verified -- the latest validated value wins, useful for volatile fields like stock or lead time.
3. Consensus merge -- if two independent trusted sources agree, accept the value; if they disagree, escalate.
4. Human adjudication -- required for regulated fields, high-value products, or attributes with material impact on compliance.

This is where a tool like Lasso reduces manual effort substantially. Predictable conflicts are resolved automatically based on your defined rules, while genuinely ambiguous cases surface in review queues with full context from all contributing sources.

For a broader checklist of the data quality dimensions that matter during conflict resolution, see our product data quality checklist.

Validate every channel export after merge

A merged catalog is not the same as a publish-ready catalog. Each sales channel has its own requirements for attribute presence, formatting, and value constraints. A record that looks perfect in your internal system can still get rejected or suppressed by a channel's validation engine.

Build channel-specific validation into your post-merge pipeline:

Structural validation -- confirm every required field is populated and correctly typed. Missing identifiers, blank titles, or malformed price values are the most common rejection triggers across channels.

Value-level validation -- check that attribute values fall within accepted ranges or enumerated lists. A color value of "Midnight Ocean" may be perfectly descriptive, but if the channel expects values from a controlled vocabulary, it will flag or ignore the listing.

Cross-field consistency -- verify that related fields are logically coherent. Price and availability should align, variant parent-child relationships should be intact, and category-specific required attributes should be present based on the assigned product type.

Identifier integrity -- ensure GTINs pass check-digit validation, brand names match the channel's brand registry where applicable, and no identifier conflicts exist across your catalog. Our guide on catalog validation frameworks covers how to structure these checks systematically.

The goal is to catch issues before they reach the channel, not after listings get suspended. When you are publishing products across multiple channels, automated pre-export validation is the difference between smooth launches and emergency feed fixes.

Operationalize the merge as a continuous loop, not a one-time project

The most damaging misconception about catalog merging is treating it as a one-time data cleanup. Supplier data changes constantly: new products arrive, prices update, attributes get corrected, and suppliers themselves come and go. A merge framework that only runs once decays within weeks.

Build a repeatable merge cycle that runs on every supplier data refresh:

1. Ingest and map -- pull supplier refreshes into your canonical schema using established field mappings.
2. Deduplicate -- run identity resolution with confidence tiering against the existing catalog.
3. Apply survivorship -- execute field-level precedence rules to resolve conflicts on new and updated records.
4. Validate exports -- run channel-specific validation on every output feed.
5. Publish and monitor -- push approved records to channels and track exception rates.
6. Refine rules -- feed recurring exceptions back into your matching and survivorship rules.

Track KPIs that reveal real merge quality over time:

• Duplicate rate per 10,000 SKUs after merge (target: declining trend).
• Conflict rate by field -- identifies which attributes cause the most disagreements and need better source-of-truth rules.
• Exception resolution time -- how quickly ambiguous matches or conflicts get cleared from review queues.
• Channel rejection rate -- percentage of merged records rejected or suppressed per channel after export.
• Listing stability -- how often a previously approved listing gets flagged or removed after a subsequent merge cycle.

When the same conflicts recur week after week, that is not normal catalog noise. It is rule debt: gaps in your matching or survivorship logic that need explicit fixes. Addressing rule debt is consistently the fastest path to cleaner listings and fewer emergency interventions.

For teams evaluating tools to support this workflow, Lasso pricing outlines options by catalog size and operational maturity. If you want to scope a merge framework around your specific suppliers and channels, book a walkthrough to see how the pipeline fits your stack.