Product Data Cleansing Services

Professional Product Data Cleansing Services for Catalog Quality, Completeness and Attribute Accuracy

We provide expert product data cleansing outsourcing solutions for eCommerce businesses, retailers, distributors and marketplace sellers whose product catalogs have accumulated quality issues — incomplete attributes, inconsistent naming, duplicate products, outdated specifications, incorrectly mapped categories and attribute values that do not conform to platform or system requirements. Poor product data quality directly reduces catalog performance: products with incomplete attributes are excluded from filtered search results; duplicate listings split reviews and ranking signals; inconsistent naming prevents reliable catalog search and cross-platform matching.

Our professional offshore product data cleanup team in India performs systematic catalog audits to identify quality issues by type and frequency, then applies targeted cleaning — standardising values, completing missing fields, removing duplicates, correcting category assignments and enriching thin product data from available sources. Every cleaning step is documented so your team can verify what was changed and why.

Both one-time catalog quality improvement projects and ongoing monthly maintenance arrangements are available. The most cost-effective long-term approach combines an initial comprehensive cleanup with regular maintenance cycles that prevent quality from deteriorating back to the state that made the cleanup necessary.

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✓ Catalog Quality Audit ✓ Attribute Standardisation ✓ Duplicate Product Removal ✓ Missing Field Completion ✓ Ongoing Catalog Maintenance

Trusted & Secure

🔒NDA Protected 🌐GDPR Aware ✅99.9% Accuracy 🎯Free Pilot Batch ⚡Fast Turnaround 🌍45+ Countries Served

5000+ Completed Projects

90% Returning Clients

16+ Years Experience

45+ Countries Served

50+ Professionals Team

Service Overview

Expert product catalog cleanup solutions that restore quality, completeness and searchability

Catalog audit and quality issue profiling
Duplicate product identification and resolution
Attribute value standardisation and normalisation
Missing required attribute completion
Category reclassification where incorrect
Ongoing catalog quality maintenance cycles

Product catalog quality problems follow predictable patterns: attributes entered with multiple spellings for the same value (red / Red / RED / scarlet); numeric attributes with inconsistent unit notation; titles with irregular capitalisation and formatting; category assignments that were made quickly without checking the taxonomy; duplicate products from different import sources or supplier feeds; and attributes left blank because the source data did not provide them. Each of these problems has a specific solution, and a professional product data cleansing outsourcing team applies those solutions at the scale your catalog requires.

We begin every cleansing project with a catalog audit — reviewing your product file against quality criteria and producing a report showing issue types, frequencies and distribution before any cleaning is committed. This gives you a clear picture of the actual problem before deciding on the scope and priority of correction work.

Our India-based product data cleansing team has worked across Amazon, eBay, Shopify, Magento, WooCommerce, Flipkart and custom platform catalog formats — understanding the platform-specific attribute requirements and data standards that must be met for catalog performance and compliance.

Cleansing Services

Expert Product Data Cleanup for Every Quality Issue Type

Each cleansing step is documented with before-and-after values so your team can verify every change made to the catalog.

Catalog audit and quality issue mapping

We audit your product catalog systematically to produce a quality issue inventory before any cleaning work begins. The audit covers: completeness (percentage of products with each required attribute populated); consistency (whether the same attribute values are formatted the same way across all products); validity (whether attribute values conform to platform or system requirements); uniqueness (whether duplicate products exist based on SKU, GTIN, product name or attribute fingerprint); and accuracy (whether categorisation, specification values and product identifiers are correct). The audit report prioritises issues by business impact — which quality problems most directly affect search visibility, conversion rate, compliance and operational efficiency — so your team can make an informed decision about the cleaning scope and priority order.

Attribute value standardisation

We standardise inconsistent attribute values across your catalog so filtering, faceted search, reporting and platform compliance all function correctly. Standardisation applies to: unit notation (cm vs centimetres vs CM vs "cm."), colour naming (red vs Red vs "Ruby Red" where the attribute schema requires "Red"), boolean fields (yes/Yes/YES/Y/1 standardised to one consistent format), material names (cotton vs Cotton vs 100% Cotton where a standard term is required), size notation (S/M/L vs Small/Medium/Large standardised to platform convention), brand names (varying capitalisation and punctuation of the same brand name standardised consistently) and numeric formats (decimal separator, trailing zeros, unit notation). Standardisation rules are documented and applied consistently across every product in the affected attribute.

Duplicate product identification and resolution

We identify duplicate products in your catalog using SKU matching, GTIN/barcode cross-reference, product name similarity and attribute fingerprint comparison. For each identified duplicate pair or group, we apply your merge rules — which record is the master, how to handle title or description differences between duplicates, how to merge review counts where the platform supports it, and whether to redirect the duplicate listing to the master. Uncertain near-duplicates that share many but not all identifying attributes are flagged for your review with both records preserved rather than automatically merged in a way that could incorrectly combine genuinely distinct products.

Missing attribute completion and data enrichment

We complete missing attribute fields from available sources — product titles that contain the missing attribute value in unextracted form, product descriptions that reference specifications not captured in attribute fields, supplier data sheets, manufacturer websites, similar product records in the same catalog and any reference databases applicable to the product category. For attributes that cannot be completed from available information, we flag the specific missing field with a note indicating which source type would provide the value — supplier datasheet, physical measurement, official product specification — so your team knows what action is needed rather than just seeing a blank field.

Ongoing product catalog quality maintenance

We provide recurring catalog quality review on a monthly or quarterly cycle — checking new products added since the last cycle for attribute completeness and format compliance, identifying newly introduced inconsistencies in attribute values from different import sources, reviewing category assignments for new additions and delivering a quality report showing current completeness and consistency metrics for key attribute fields. Ongoing maintenance prevents the gradual quality deterioration that accumulates in any actively growing catalog and maintains the improvements achieved in the initial cleanup project.

Inputs and Output

We work with the files you already have

📂 Source formats we accept

Product catalog exports (Excel, CSV, XML)
Platform product data exports
Supplier feed files for cross-reference
Category tree and attribute documentation
Current quality issue lists or concerns

📤 Delivery formats

Cleaned product catalog files
Catalog audit report with issue inventory
Standardisation rule documentation
Duplicate resolution files with merge decisions
Monthly quality metrics reports

How It Works

How we manage product data cleansing projects

Source and Platform Review

We review your supplier files, platform category requirements, existing listing structure and brand guidelines before any entry begins. Flat file column requirements, variation theme rules and your approval workflow are confirmed in writing before production starts.

NDA and Account Access Setup

NDA signed before any source files or platform credentials are shared. Seller Central, Shopify or platform access scoped strictly to catalog entry functions — no payment, pricing rule or account settings access beyond project scope.

Pilot Listing Batch

20–50 products processed across your key categories and returned for your review. Title format, attribute mapping, variation structure and image coordination confirmed before full production is committed.

Batch Production with Attribute Validation

Full catalog processed in controlled batches. Required attribute completeness, category compliance, variation integrity and image mapping checked between every phase before the next batch begins.

Upload Review and Error Correction

Platform processing reports reviewed after submission. Field-level errors identified, corrected in the source file and resubmitted without requiring your team to diagnose each failure.

Delivery and Ongoing Account Support

Completed batches delivered with a specific exception log noting SKU, field and issue for items your team needs to address. For recurring arrangements the same team maintains your account, improving familiarity with every cycle.

Need your product catalog audited and cleaned for quality and completeness?

Share a sample of your product catalog. We run a free audit on the sample and report back on quality issue types and frequencies before you decide on the full project scope.

Get a Free Catalog Audit Sample →

Free catalog audit returned within 24-48 hours.

Why Outsource to SDES?

Why eCommerce businesses outsource product catalog work to SDES India

Platform-specific attribute requirements confirmed before any entry begins
Pilot batch approval before large catalogs are committed to production
Attribute mapping verified — upload error rate consistently below 1%
Variation structures built correctly the first time, not rebuilt after errors
Upload error reports reviewed and corrected as part of every project cycle
No long-term contract — scale up for launches, peaks and catalog updates

Your catalog is the foundation of your marketplace revenue. Attribute gaps exclude listings from filtered search. Incorrect categories reduce relevance. Broken variation structures fragment review counts. Every data quality problem in your catalog has a direct revenue consequence — and we treat every field with that in mind.

We handle the systematic, detail-intensive work of building and maintaining product catalog data so your team can focus on supplier relationships, pricing strategy and sales growth rather than flat file templates and upload error reports.

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Industries We Support

Expert product data quality solutions for catalog-dependent businesses

eCommerce Retailers

Catalog quality improvement for direct-to-consumer stores improving search performance, conversion rate and marketplace compliance.

Marketplace Sellers

Attribute quality improvement for Amazon, eBay, Walmart and marketplace catalogs with compliance-specific cleanup focus.

Manufacturers

Product master data quality for PIM systems, product data sheets and retail partner data feeds.

Distributors

Multi-brand catalog quality management, duplicate resolution from multiple supplier feeds and attribute standardisation across product ranges.

Commerce Agencies

Catalog quality improvement services for clients preparing for platform migrations, marketplace launches and catalog expansion.

Brand Owners

Brand catalog data quality management and product master data standards enforcement across retail channels.

Quality and Security

Accurate output, handled securely

Every platform engagement begins with a signed NDA and access limited to the catalog management functions your project requires. We do not request permissions beyond the current scope — no payment methods, pricing rules, promotional settings or account configurations are accessed or modified.

Required attributes that cannot be completed from your source data are flagged specifically with the attribute name and reason — never left blank silently or filled with placeholder values that would fail platform review or misrepresent your product to buyers.

Upload error reports from Amazon, eBay, Shopify and other platforms are reviewed and corrected as part of every batch cycle. Your team should not need to investigate and diagnose field-level upload failures — that is part of the service.

🔒 NDA Protected Before files are shared

🌐 GDPR Aware EU data handling

✅ 99.9% Accuracy Multi-level QA checks

🛡️ Secure Transfer Encrypted file access

📋 Exception Log Every delivery

👥 Project Team Only Controlled access

Client Feedback

What clients say about our product data cleansing work

★★★★★

We needed 4,200 Amazon listings built across six categories from our supplier spreadsheets. The pilot confirmed the attribute mapping before we committed the full catalog. Final flat file upload error rate was 0.8% — far better than anything we had managed internally. SDES handled error corrections from Amazon's processing report without us needing to explain what went wrong.

Catalog Operations Manager Multi-Category Amazon Seller, USA

★★★★★

Our Shopify store launched with 2,400 apparel products in correct collections, variants properly configured and metafields populated. Delivery was three days ahead of our launch deadline. We now use SDES for ongoing monthly product additions and seasonal catalog updates.

Head of eCommerce Fashion Brand, UK

★★★★★

Our Magento catalog had five years of inconsistent attribute data across 8,000 products. SDES standardised the attribute vocabulary and delivered a clean import file with a documented change log. Our layered navigation started returning correct results the same week as the import.

Product Manager Industrial Supply Distributor, Australia

FAQs

Questions clients ask before outsourcing product data cleansing

How do I know how bad my catalog quality problem actually is?

The free catalog audit on your sample answers this question specifically — showing quality issue types, frequencies and distribution before you commit to any cleaning scope.

Can you standardise attribute values across a large catalog of tens of thousands of products?

Yes. Large catalog standardisation is processed in batches with consistency verification throughout.

How do you decide which duplicate is the master record?

Master record selection follows rules you define — typically the record with the most complete attribute data, the longest history or the primary platform listing. Ambiguous cases are flagged.

Can you enrich missing attributes from external sources?

Yes. We research missing attributes from supplier websites, manufacturer datasheets, similar catalog records and reference databases where the data is publicly available.

Can you maintain catalog quality on an ongoing basis?

Yes. Monthly or quarterly maintenance cycles prevent quality deterioration after the initial cleanup.

What format are the cleaned catalog files delivered in?

Excel, CSV, XML or your platform's import format — confirmed before production begins.

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