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List management

Data Cleaning for E-Commerce Product Feeds, Google Merchant Center, Amazon Seller Central, Shopify Product Catalogues and Multi-Channel Listing Management

By Email ExtractorPublished 9 min read
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Product Data Quality and Revenue Impact

Product data quality directly affects advertising performance, organic search visibility, marketplace compliance, customer experience and return rates. Dirty product data causes feed rejections (products not shown), disapprovals (ads not running), poor search matching (products not found), customer confusion (wrong product received) and competitive disadvantage:

Platform Common data quality problems Revenue impact
Google Merchant Center Missing GTIN/MPN; incorrect product category; price mismatch between feed and landing page; missing required attributes (colour, size, material for apparel); policy violations (prohibited content, misleading claims) Products disapproved = not shown in Shopping ads or free listings; account-level suspension (all products removed) for repeated violations; up to 100% of Shopping ad revenue lost during suspension
Amazon Seller Central Missing or incorrect UPC/EAN; incorrect browse node; suppressed listings (missing bullet points, images, or required attributes); duplicate ASINs; incorrect variation relationships (parent-child) Suppressed listings not shown in search; Buy Box loss from incorrect pricing or condition data; listing removed for policy violations; A+ Content (Enhanced Brand Content) rejected
Shopify / WooCommerce Inconsistent product titles; missing alt text; inconsistent size/colour naming; missing SEO metadata; broken variant relationships; incorrect inventory sync across channels Poor organic search performance; customer confusion from inconsistent naming; overselling from inventory errors; high return rates from inaccurate descriptions
Multi-channel (ChannelAdvisor, Feedonomics, DataFeedWatch) Attribute mapping errors between platforms; inventory sync delays; pricing inconsistencies across channels; missing platform-specific required fields MAP (minimum advertised price) violations; channel-specific disapprovals; inventory overselling; marketplace suspension

Common Data Quality Problems

Title and description issues

Problem Example Impact Fix
Keyword stuffing "Men's Running Shoes Best Marathon Training Athletic Sport Sneaker Comfortable Lightweight Breathable" Google Merchant Center disapproval; Amazon listing suppression; poor customer experience Clean title: "[Brand] [Product Name] [Key Attribute] -- [Size/Color if variant]"; move keywords to description and backend search terms
Missing required attributes in title "Blue Shirt" (missing brand, size, gender, material) Low search relevance; missing from filtered searches; poor click-through rate Include brand, product type, distinguishing attributes: "[Brand] Men's Slim Fit Cotton Dress Shirt -- Blue, Large"
Inconsistent capitalisation "mens RUNNING shoes" vs "Men's Running Shoes" vs "MENS running SHOES" Unprofessional appearance; inconsistent brand presentation; poor customer trust Standardise to title case; apply programmatically to entire catalogue
HTML in text fields "&amp;" instead of "&"; "<br>" tags in titles; encoded characters Feed rejection; garbled display on marketplace; character count violations Strip HTML; decode entities; validate clean text before submission
Duplicate titles across variants Same title for Small, Medium, Large -- only difference is in variant attribute Customer cannot distinguish variants in search results; incorrect variant selection Include distinguishing attribute in title: "... -- Size Large" or use parent/child relationships correctly

Attribute and categorisation issues

Problem Example Impact Fix
Missing GTIN (UPC/EAN/ISBN) Product listed without barcode identifier Google: reduced visibility, may be disapproved for categories requiring GTIN; Amazon: listing cannot be created without UPC in most categories Obtain GTINs from GS1 (for own products) or from manufacturer (for resold products); use exemption process only when genuinely applicable
Incorrect product category / taxonomy Laptop listed under "Computer Accessories" instead of "Computers > Laptops" Product not shown in correct filtered searches; Google auto-categorisation may override; reduced relevance Map each product to most specific category available; Google uses its own taxonomy (google_product_category); Amazon uses browse nodes
Inconsistent colour naming "Midnight Blue" vs "Navy" vs "Dark Blue" vs "Blue (Dark)" for the same colour Customer cannot find product by colour filter; inventory confusion; returns when "Midnight Blue" is not what customer expected Standardise to platform colour taxonomy; Google and Amazon each have preferred colour values; use consistent naming across channels
Missing or incorrect size "One Size" when the product comes in S/M/L/XL; "Large" without specifying size system (US, EU, UK) Customer orders wrong size; high return rate; negative reviews Use platform size taxonomy; specify size system; include size chart link in description
Missing material / fabric composition Apparel listed without material information Required by Google for apparel; required by regulation in many jurisdictions (FTC textile rules); customer cannot assess quality Add material composition to product attributes and description; for apparel: include fibre content percentages
Incorrect weight / dimensions Package weight listed instead of product weight; dimensions swapped (height and width reversed) Incorrect shipping cost calculation; Amazon FBA storage fees miscalculated; customer receives product that does not fit expected dimensions Verify weight and dimensions against actual product; distinguish product dimensions from shipping dimensions

Pricing and inventory issues

Problem Example Impact Fix
Price mismatch (feed vs. landing page) Feed shows $29.99; website shows $34.99 (price updated on site but feed not refreshed) Google Merchant Center disapproval for price mismatch; customer confusion; trust damage Real-time or frequent feed refresh; automated price sync; price monitoring alerts
Missing sale price markup Product on sale but feed does not include sale_price and sale_price_effective_date Product does not show sale badge in Shopping ads; misses promotional opportunities; competitor with visible sale price gets the click Include sale_price and date range in feed; automate promotional pricing sync
Inventory out of sync Product shows in stock in feed but is actually out of stock on website Customer clicks ad, finds out of stock; wasted ad spend; poor user experience; Merchant Center disapproval for availability mismatch Real-time inventory sync; automatic feed update on stock changes; set threshold alerts
MAP (minimum advertised price) violations Product advertised below manufacturer's minimum price on one channel Manufacturer terminates authorised seller agreement; listing removed; brand relationship damaged Automated MAP compliance checking across all channels; alert system for price changes

Cleaning Workflow

Step 1: Export and audit

Action Details
Export product catalogue Export all products from primary system (Shopify, WooCommerce, ERP) as CSV including all attributes
Export marketplace feeds Export current feeds from Google Merchant Center, Amazon, and any other active channels
Audit completeness Count products with missing required attributes by platform; calculate fill rate for each attribute
Identify disapprovals Pull disapproval reports from Google Merchant Center and suppression reports from Amazon
Catalogue size assessment Total SKUs; active vs. inactive; variants per parent; categories used

Step 2: Standardise core attributes

Attribute Standardisation rule Tool / method
Product title [Brand] + [Product Name] + [Key Attribute] + [Variant Attribute]; title case; no keyword stuffing; under 150 characters Spreadsheet formula or script; manual review for top sellers
Description Unique per product (no duplicate descriptions across SKUs); no HTML in text feeds; accurate and complete; under platform character limit Script to strip HTML; manual rewrite for top sellers; template-based generation for variants
Colour Map all colour values to platform-approved values; one standard name per colour (not "Midnight Blue" on Google and "Navy" on Amazon) Colour mapping table; find-and-replace across catalogue
Size Platform-specific size format; include size system (US, EU, UK); consistent across all products in same category Size mapping table; separate size system field
GTIN / UPC Valid format (12 digits for UPC, 13 for EAN); check digit validated; matches actual product GS1 validation; manufacturer verification; barcode scanning verification
Category / taxonomy Most specific category on each platform; consistent across similar products Category mapping spreadsheet; platform taxonomy reference

Step 3: Deduplicate and reconcile

Action Details
Identify duplicate SKUs Upload product CSV to Email Extractor to find duplicate email addresses in supplier or vendor contact columns, then use spreadsheet deduplication for product-level duplicates: sort by GTIN, title or SKU; identify exact and near-duplicates
Reconcile cross-channel data Compare product data across all channels; identify inconsistencies in title, price, description, attributes and images
Merge variant data Verify parent-child relationships; ensure each variant has correct distinguishing attributes; remove orphaned variants
Resolve conflicting data When the same product has different data on different channels, determine authoritative source (usually the primary system or ERP) and push corrections to all channels

Step 4: Validate and submit

Action Details
Platform validation Run cleaned feed through platform-specific validators: Google Merchant Center feed rules; Amazon listing quality dashboard; Shopify bulk editor
Test submission Submit cleaned feed to staging/test environment if available; monitor for new errors
Monitor results After submission, monitor: disapproval count (should decrease); impression count (should increase for previously disapproved products); click-through rate (should improve with better titles); return rate (should decrease with accurate descriptions)

Contact Data in Product Operations

Product feed management involves extensive email communication with suppliers, manufacturers, distributors, marketplace representatives and logistics partners. Maintaining clean contact data across these relationships reduces delays in obtaining product information, pricing updates, inventory data and compliance documentation:

Contact type Why email accuracy matters Common problem
Supplier / manufacturer Product data requests (images, specifications, GTINs, compliance documents); pricing updates; inventory availability; new product launches Supplier contact changes with employee turnover; generic emails (info@, sales@) route to unmonitored inboxes; multiple contacts at same supplier for different functions (product data vs. pricing vs. logistics)
Marketplace representative Account health issues; listing disputes; programme enrolment; feature access; policy clarification Amazon and Google assign account managers who change periodically; marketplace communication increasingly through platform portals rather than email
3PL / fulfilment partner Inventory receipt; shipment tracking; storage issues; return processing Warehouse contact turnover; multiple warehouses with different contacts; seasonal staff with temporary email addresses
Marketing agency / feed management service Feed specifications; campaign changes; performance reporting; budget adjustments Agency staff turnover; project managers change; freelancers with changing email addresses

When managing supplier contact lists across CSV exports from ERP systems, email threads, order confirmations and supplier directories, periodically upload all source files to Email Extractor to extract and deduplicate email addresses. Suppliers appear across purchase orders, product data requests, compliance communications and logistics coordination, and maintaining one clean contact record per supplier prevents miscommunication and ensures product data requests reach the right person.

Metrics

Metric Before cleaning Target after cleaning How to measure
Google Merchant Center disapproval rate 5-20% of products Under 2% Merchant Center diagnostics
Amazon listing suppression rate 3-10% of listings Under 1% Seller Central listing quality dashboard
Feed attribute completeness 60-80% of fields populated 95%+ of required fields; 80%+ of recommended fields Attribute fill rate analysis
Product title consistency score Varies widely 100% following naming convention Programmatic title validation
GTIN coverage 50-80% of applicable products 95%+ of applicable products Count of products with valid GTIN vs. GTIN-required products
Price mismatch rate 1-5% of products at any given time Under 0.5% Automated price comparison (feed vs. landing page)
Return rate from incorrect description 5-15% of returns Under 3% of returns attributed to description inaccuracy Return reason analysis

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