Ecommerce Product Data Quality: How to Fix the Catalog Issues Costing You Sales

The Hidden Cost of Bad Product Data

Poor product data costs ecommerce businesses in three ways:

1. Listing suppression Amazon, Walmart, and other marketplaces suppress listings that fail data quality checks. A missing mandatory attribute, an image that doesn't meet spec, or a price outside acceptable range can take a product offline without warning.

2. High return rates Products with inaccurate dimensions, misleading descriptions, or missing compatibility information generate returns. A 15% return rate on a product can wipe out its profitability entirely.

3. Lost search visibility Search algorithms on every major marketplace and search engine use structured product attributes as ranking signals. Incomplete attributes mean reduced visibility for relevant search queries.

The Most Common Product Data Problems

Missing mandatory attributes

Every marketplace has a category-specific attribute set with mandatory fields. Missing even one mandatory field can cause suppression.

Common mandatory fields that get missed:

• GTIN (UPC/EAN/ISBN) — required on Amazon and Walmart
• Brand — must match Brand Registry on Amazon
• Material composition — required for all apparel
• Product dimensions (assembled) — required for most furniture and appliances
• Age rating — required for toys and children's products

Inconsistent attribute formatting

When the same attribute appears in different formats across your catalog, it creates poor filter experiences and inconsistent search matching.

Examples:

• Colour: "Red", "red", "RED", "Crimson Red", "red color" — all referring to the same thing
• Material: "100% Cotton", "Cotton 100%", "Pure Cotton", "cotton"
• Size: "Small", "S", "Sm", "SMALL"

The fix: a controlled vocabulary for each attribute. Define the exact allowed values and enforce them at upload.

Truncated or incomplete product titles

Many catalogs have titles that were generated programmatically (brand + SKU code) without keyword optimisation. These titles rank poorly and fail to communicate product benefits.

A complete marketplace-optimised title includes:

• Brand
• Product name
• Primary differentiating attribute (material, colour, size, model)
• Quantity or pack size where relevant

Incorrect categorisation

Products placed in the wrong category receive reduced visibility, may miss category-specific promotions, and can trigger compliance checks for the wrong product type.

Missing or non-compliant images

• No main image (listing suppressed)
• Main image with background other than white (Amazon requires pure white for the main image)
• Images below minimum resolution (Amazon: 1,000px on the longest side for zoom functionality)
• Logo or watermark on main image (violates Amazon and Walmart policies)

How to Run a Catalog Data Audit

Step 1: Export your full catalog

From Amazon: download a flat-file inventory report from Seller Central > Reports > Fulfillment > Inventory.

From Shopify: Admin > Products > Export > All products (CSV format).

Step 2: Check for suppressed listings

Amazon: Seller Central > Inventory > Fix Stranded Inventory (shows suppressed listings with reason codes).

Walmart: Seller Center > Items > Unpublished Items report.

Step 3: Run a completeness check

In your export spreadsheet, check completion rates for each attribute column:

• Flag any column with >5% empty cells for mandatory attributes
• Flag >20% empty cells for recommended attributes

Step 4: Check for formatting inconsistencies

Use Excel or Google Sheets:

• Sort each text attribute column alphabetically — inconsistent variants will cluster together
• Use a pivot table on the colour or material column to see all distinct values and their frequencies

Step 5: Validate image compliance

For Amazon: use the Image Issues section in Inventory Health (requires Brand Registry).

For bulk checks: use the Amazon Listing Quality Dashboard or a third-party tool like Helium 10's Listing Analyzer.

Data Quality Standards to Enforce

Title standard

[Brand] [Product Type] [Key Attribute 1] [Key Attribute 2] [Variant], [Pack Size]

Example: BrandName Bamboo Bath Towel Extra-Large 700gsm, Set of 2

Image standard

• Main image: pure white background (#FFFFFF), product fills 85%+ of frame, min 1500px on longest side
• Supporting images: lifestyle, feature callout, scale/size reference, back/side angles
• Number of images: minimum 4, target 7–8

Attribute standard

• Colour values: use the marketplace's approved colour palette (Amazon has a defined list)
• Dimensions: always in the same unit (cm for UK/EU, inches for US) with the same precision (1 decimal place)
• Weight: consistent unit (kg or g, not mixed)

Maintaining Quality at Scale

Onboarding template

Create a product upload template (Google Sheet or Airtable) with dropdown menus for controlled vocabulary fields. This prevents data entry errors at source.

Pre-upload QA checklist

Before submitting any listing:

• GTIN verified and unique
• Title follows the defined format
• Mandatory attributes complete
• Main image white background, correct resolution
• Price within expected range (sanity check)
• Category correct for product type

Weekly suppression review

Set a recurring weekly task: check the suppressed/unpublished listings report on every marketplace. Many suppression reasons are fixable in minutes — missing them for weeks costs significant revenue.

Data enrichment process

When new products arrive without complete supplier data, run a structured enrichment process:

1. Pull the product specification from the manufacturer's website
2. Cross-reference with competitor listings for attribute completeness
3. Fill gaps before uploading — never upload incomplete listings as "placeholders"

Tools for Catalog Data Quality

For large catalogs (1,000+ SKUs), a dedicated data management tool or outsourced catalog team provides better ROI than manual spreadsheet work.

eData4You provides catalog data audits, enrichment, and ongoing quality management for multi-channel ecommerce brands. See data entry services →