E-commerce Product Data Cleansing: The Complete Guide to Clean Catalog Data

E-commerce product data cleansing: why clean data drives revenue

E-commerce product data cleansing is the foundation of every successful online retail operation. Without clean, accurate product information, your listings get rejected by marketplaces, your ads underperform, and your customers abandon carts or return products at alarming rates.

The financial impact is substantial. Research shows that mid-market e-commerce companies with 10,000 to 100,000 SKUs lose an average of 23% of potential revenue to bad product data. On a $50 million business, that translates to roughly $11.5 million in lost revenue annually. Gartner estimates that organizations lose between $12.9 and $15 million per year due to poor data accuracy alone.

Where does this revenue disappear? The breakdown is revealing:

• 2-4% from cart abandonment caused by variant selection errors and missing product details
• 6-9% from inventory inaccuracy showing incorrect stock status
• 5-7% from broken product recommendations
• 8-12% from poor on-site search performance

The good news is that these losses are preventable. A structured approach to product data cleansing can recover significant revenue while reducing operational costs. This guide covers the techniques, tools, and strategies that leading e-commerce teams use to maintain clean catalog data at scale.

For a foundational understanding of how cleansing fits into the broader data quality picture, see our guide on product data cleansing vs enrichment vs normalization.

Common data quality issues that kill conversions

Before you can fix data quality problems, you need to recognize them. These are the issues that most frequently damage e-commerce performance:

Duplicate and proliferated SKUs

Multiple SKU entries representing the same product create chaos across your operations. This happens when different suppliers use inconsistent naming conventions, when data migrates between systems without proper deduplication, or when manual entry creates variations.

The impact is measurable: duplicate SKUs cause 34% higher bounce rates on search results pages. Shoppers see the same product multiple times, inventory fragments across entries (showing items out-of-stock while units sit under different SKU identifiers), and your analytics become unreliable.

Missing product attributes

Incomplete product data triggers immediate consequences. Research shows 83% of shoppers immediately abandon sites when they cannot find the information they need. Missing attributes also cause feed rejections on Google Merchant Center, Amazon, and other channels.

Critical attributes that are frequently missing include:

• Shipping dimensions and weight
• Material composition and care instructions
• Compatibility information
• Size charts and fit details
• Technical specifications

Inconsistent descriptions across channels

When the same product appears differently across your website, marketplaces, and advertising channels, customers lose trust. Studies indicate that 53% of US shoppers abandon carts when encountering conflicting or confusing product details, and 60% of product returns stem from misleading descriptions.

Invalid or missing identifiers

GTINs, EANs, UPCs, and MPNs are not optional metadata—they are critical for marketplace visibility and product matching. Invalid barcodes can link your product to incorrect listings, while missing identifiers reduce your visibility in Google Shopping and other comparison engines.

For specific guidance on handling identifier issues, see our article on missing EAN and GTIN in listings.

Data decay

Product data does not stay accurate on its own. Information decays at approximately 70% per year as specifications change, prices update, suppliers modify details, and market conditions shift. Without continuous cleansing processes, even a perfectly clean catalog degrades rapidly.

Data cleansing techniques and best practices

Effective product data cleansing follows a systematic approach. These techniques address the most common quality issues:

Standardization and normalization

Standardization ensures consistent formatting across all product attributes. This includes:

• Attribute vocabulary control: Mapping variations like "XL," "X-Large," "Extra Large," and "extra-large" to a single canonical value
• Unit standardization: Converting "15 cm," "150 mm," and "0.15 m" to one consistent format
• Case and punctuation rules: Ensuring brand names, color values, and material descriptors follow consistent capitalization
• Measurement formatting: Standardizing decimal separators, date formats, and measurement conventions for each target market

Duplicate detection and removal

Modern duplicate detection uses multi-attribute matching algorithms rather than simple exact-match comparisons. Effective approaches include:

• Fuzzy matching on product titles to catch near-duplicates with minor spelling variations
• Image similarity analysis to identify products with different SKUs but identical photos
• Attribute clustering to group products that share key specifications
• Parent-child relationship validation to ensure variant structures are correct

Validation and verification

Every record should pass validation checks before entering your production catalog:

• Required field validation: Ensuring every product has title, price, availability, at least one image, and category assignment
• Identifier integrity: Validating GTIN check digits and format compliance
• Logical consistency: Flagging records where "in stock" status conflicts with zero quantity
• URL and image verification: Confirming that all linked resources are accessible

Text and markup cleanup

Raw product data often contains artifacts that degrade listing quality:

• Stray HTML tags from copy-paste operations
• Control characters and encoding errors
• Excessive whitespace in titles and descriptions
• Promotional text that violates channel policies

For a comprehensive validation approach, see our catalog validation framework.

Automated vs. manual cleansing: when to use each

The debate between automated and manual data cleansing misses the point. The most effective approach combines both, using each where it excels.

When automation wins

Automated cleansing is superior for:

• High-volume, rule-based tasks: Format standardization, unit conversion, and vocabulary mapping across thousands of SKUs
• Continuous monitoring: Real-time validation at data entry points catches errors before they propagate
• Pattern detection: AI-powered systems identify duplicates and anomalies that human reviewers would miss at scale
• Speed and consistency: Automated processes apply the same rules uniformly without fatigue or variation

Modern AI-powered cleansing tools can process entire catalogs in minutes, detecting issues that would take human teams weeks to identify manually.

When manual review is essential

Human judgment remains critical for:

• Edge cases and exceptions: Products that do not fit standard patterns require contextual understanding
• Brand-sensitive content: Ensuring product descriptions maintain brand voice and comply with manufacturer guidelines
• Complex categorization decisions: Determining the correct taxonomy placement for ambiguous products
• Quality assurance: Validating that automated corrections are accurate before publishing

The hybrid approach

The most successful e-commerce teams implement a hybrid workflow:

1. Automated first pass: AI-powered tools scan incoming data, apply standardization rules, flag potential issues, and auto-correct high-confidence problems
2. Human review queue: Low-confidence corrections and flagged anomalies route to human reviewers for decision
3. Feedback loop: Human decisions train the automated system, improving accuracy over time
4. Continuous monitoring: Automated alerts notify teams when data quality metrics decline

Tools like Lasso implement this hybrid model, combining AI-powered detection with human-in-the-loop validation to achieve both speed and accuracy.

Measuring data cleansing ROI

Data cleansing is an investment, and like any investment, it should deliver measurable returns. Track these metrics to quantify impact:

Feed acceptance rate

The percentage of products that pass channel validation without rejection. Improvements here directly translate to more products visible to shoppers. Track this metric separately for each channel (Google Merchant Center, Amazon, Meta) since requirements differ.

Attribute completeness score

Measure the percentage of products with all recommended attributes populated. Research shows that products with complete attributes significantly outperform sparse listings in both search visibility and conversion rate.

Return rate by data quality

Segment your return data by product data completeness. Products with accurate specifications, clear images, and detailed descriptions consistently show lower return rates than products with incomplete information.

Search performance metrics

Track on-site search metrics including:

• Zero-result query rate
• Search-to-purchase conversion
• Search refinement rate (how often shoppers modify their initial query)

Clean, well-structured product data improves all of these metrics by enabling accurate search matching and filtering.

Time savings

Quantify the hours your team spends on manual data fixes, feed error resolution, and customer service inquiries related to incorrect product information. Effective cleansing automation can reduce this time by 80% or more.

For a comprehensive starting point, use our product data quality checklist to establish baseline measurements.

Getting started with data cleansing

If your catalog has accumulated years of data quality issues, a systematic approach prevents overwhelm:

Week 1: Audit and prioritize

Start by understanding your current state. Export your catalog and analyze:

• Total SKU count and duplicate rate
• Attribute completeness by category
• Feed rejection rates by channel and error type
• Return rate correlation with data quality

Prioritize categories by revenue impact. Cleaning your top 20% of products by sales volume often recovers 80% of the available value.

Week 2: Establish standards

Define your canonical data model:

• Required vs. optional attributes by category
• Accepted values for each attribute (size scales, color names, material types)
• Naming conventions for titles and descriptions
• Image requirements and quality standards

Document these standards so they can be enforced consistently.

Week 3: Implement automation

Deploy automated cleansing for high-volume tasks:

• Standardization rules for known variations
• Validation checks at data entry points
• Duplicate detection algorithms
• Missing attribute flagging

Week 4: Process and monitor

Establish ongoing processes:

• Review queues for flagged items
• Regular audit schedules
• Quality metric dashboards
• Escalation procedures for systematic issues

Data cleansing is not a project with an end date—it is an operational capability that requires continuous attention.

When you are ready to move from manual processes to a governed, scalable approach, Lasso's AI-powered platform automates cleansing, standardization, and validation while keeping humans in the loop for quality assurance. Book a demo to see how automated cleansing can transform your catalog operations.