Claro

Browse Resources

Free tools, a product-data glossary, neutral comparisons, step-by-step playbooks, articles, and evergreen guides for catalog teams — matching, deduplication, classification, enrichment, AI validation, and AI search.

Glossary
  1. 01 What Is Entity Resolution?
  2. 02 Golden Record Product Data: What Is a Canonical Product Record?
  3. 03 What Is Fuzzy Matching?
  4. 04 Deterministic vs Probabilistic Matching
  5. 05 Confidence Score in Data Matching: A Practical Guide
  6. 06 What Is Data Provenance?
  7. 07 Write-back: Safely Updating ERP and PIM Systems After AI Validation
  8. 08 What Is Record Linkage?
  9. 09 What Is Schema Mapping?
  10. 10 Product Data Normalization: What It Is and Why It Matters
  11. 11 What Is Master Data Management (MDM)?
  12. 12 What Is a PIM? Product Information Management Explained
  13. 13 What Is Schema Drift?
  14. 14 Product Knowledge Graph: What It Is and Why It Powers AI Search
  15. 15 Supplier Scorecard: How to Grade Vendor Data Quality
  16. 16 SKU vs MPN vs GTIN: What Each Identifier Does and Why All Three Matter
  17. 17 What Is Generative Engine Optimization (GEO)?
  18. 18 Schema.org Product Structured Data: The Complete Guide
  19. 19 What Is Product Content Syndication?
  20. 20 What Is GDSN? The Global Data Synchronisation Network Explained
  21. 21 What Is a Data Pool? GS1, GDSN, and Synchronized Product Data
  22. 22 What Is Unit of Measure (UOM) in Product Data?
  23. 23 GTIN vs EAN vs UPC: The Definitive Guide for Product Data Teams
  24. 24 What Is a GLN? Global Location Number Explained
  25. 25 ECLASS IRDI Format: Structure, Segments, and Why It Matters
  26. 26 ETIM in BMEcat: Structured Product Classification for Technical Catalogs
  27. 27 UNECE Rec 20 Unit Codes Explained
  28. 28 What Is a CAS Number?
  29. 29 SVHC Candidate List: What It Is and Why Product Data Teams Track It
  30. 30 What Is RAL Classic? The Color Standard for Product Catalogs
  31. 31 IP Rating Explained (IP54, IP65, IP67)
  32. 32 IP Rating Chart (IEC 60529): What Every Digit Means
  33. 33 IK Rating Explained: The Impact-Resistance Scale for Product Data Teams
  34. 34 How to Read an ATEX Marking
  35. 35 ATEX Zone Classification: Zones 0-2 and 20-22 Explained
  36. 36 IEC 60309 Colors and Clock Positions Explained
  37. 37 NEMA Enclosure Types Explained
  38. 38 CENELEC Cable Designation: HD 361 Type Codes Explained
  39. 39 BSP Thread Dimensions: Nominal Sizes, TPI, and Catalog Matching
  40. 40 Is Claro a PIM? What It Does Instead
  41. 41 IES vs LDT Photometric Files
  42. 42 ETIM EC000042: Miniature Circuit Breaker
  43. 43 ETIM EC000141: The Contactor Classification Code Explained
Comparisons
  1. 01 Fuzzy Matching vs Entity Resolution: Which Does Your Catalog Actually Need?
  2. 02 PIM vs MDM vs DAM: Which System Does What?
  3. 03 Akeneo vs Pimcore: PIM Platform Comparison for Distributors
  4. 04 Salsify vs Syndigo: Which Platform Fits Your Syndication Stack?
  5. 05 SEO vs GEO for Product Catalogs: What Your Data Needs to Win Both
  6. 06 Build vs Buy Entity Matching: In-House Scripts vs a Matching Platform
  7. 07 GDSN vs Direct Feed: Which Syndication Path Fits Your Catalog?
  8. 08 Build vs Buy Catalog Infrastructure: A Total-Cost Comparison
  9. 09 ETIM vs UNSPSC vs eCl@ss: Which Classification Standard Does Your Catalog Need?
  10. 10 Product Taxonomy Comparison: ETIM vs UNSPSC vs Google Product Category
  11. 11 ECLASS vs ETIM for Distributors: Which Classification Standard Do You Need?
  12. 12 GTIN vs MPN vs SKU: Which Product Identifier Does What?
  13. 13 GLN vs GTIN: Two GS1 Identifiers That Break Catalogs When Mixed
  14. 14 CAS Number vs EC Number: Which Identifier to Store and Why
  15. 15 SVHC vs SCIP vs REACH Annex XVII: Which Obligation Applies to Your SKU?
  16. 16 IP54 vs IP65 vs IP67: Which Ingress Protection Rating Fits Your Product Record?
  17. 17 NEMA vs IP Ratings: Enclosure Ingress Standards Compared
  18. 18 NPT vs BSP vs Metric Threads: Side-by-Side Comparison
  19. 19 ATEX vs IECEx: What Product-Data Teams Need to Know
  20. 20 IK08 vs IK10: Impact Ratings in Product Data
  21. 21 H05VV-F vs H07RN-F: Cable Code Comparison for Accurate Catalog Records
  22. 22 PIM vs Spreadsheet: When Does a Catalog Outgrow Excel?
  23. 23 Catalog Data Management Platform vs PIM Add-Ons
Playbooks
  1. 01 How to Deduplicate a Product Catalog
  2. 02 Match Supplier Catalog to Inventory: A Step-by-Step Playbook
  3. 03 Build a Golden Product Record: Step-by-Step Playbook
  4. 04 Auto-Merge Confidence Threshold: How to Set and Tune It
  5. 05 Find Alternative Suppliers in Your Catalog: A Step-by-Step Playbook
  6. 06 How to Find Functional-Equivalent Products Across Suppliers
  7. 07 PIM Migration Deduplication: Migrate Catalogs Without Duplicates
  8. 08 Onboard a New Supplier Range in 24 Hours
  9. 09 Map Supplier Attributes to Your Schema: A Step-by-Step Playbook
  10. 10 Extract Product Specs From PDFs With Full Traceability
  11. 11 Supplier Data Scorecard: How to Build and Run One
  12. 12 Catalog Data Drift: How to Detect and Fix It
  13. 13 Validate AI Product Data Before Publishing
  14. 14 Make Your Catalog AI-Search Ready: A GEO Playbook
  15. 15 Product Schema Markup at Scale: A Catalog Team Playbook
  16. 16 Fix Google Merchant Feed Errors: A Step-by-Step Validation Playbook
  17. 17 ETIM Classification Workflow for Distributors
  18. 18 Validate ETIM XML Export: A Step-by-Step Playbook
  19. 19 Validate ECLASS in BMEcat: A Step-by-Step Playbook
  20. 20 How to Identify an IEC 60309 Plug From Markings
  21. 21 Identify Thread Diameter and Pitch: A Catalog Enrichment Playbook
  22. 22 How to Extract Safety Data Sheet (SDS) Data for Every SKU
  23. 23 How to Keep Taxonomy and Catalog in Sync as Both Change
  24. 24 How to Group Product Variants Into One Family
  25. 25 How to Validate SKUs Before They Enter Your Catalog
Articles
  1. 01 Agentic Commerce Runs on Machine-Readable Product Data — and Who Maintains It Is the Open Question
  2. 02 Agentic AI Is Now a Default in Retail — Which Is Exactly Why It Still Needs Humans in the Loop
  3. 03 How Product Matching Actually Works at Scale (and Why LLMs Alone Aren't Enough)
  4. 04 AI-Ready Product Data: Why Agents Can't Read Most Catalogs (and What to Fix)
  5. 05 GEO for Product Data: When AI Replaces the Browser, Your Catalog Is the Storefront
  6. 06 AI Won't Replace Your ERP — It Can Finally Make the Data Inside It Trustworthy
  7. 07 Your Catalog Is the Context AI Uses — Product Metadata Is Now Operational Infrastructure
  8. 08 Bots Now Outnumber Humans Online. The Real Question Is Whether They Can Use Your Catalog.
  9. 09 You Deployed AI Search and Relevance Got Worse — The Catalog Was Never Ready
Guides
  1. 01 Speed Up Supplier Onboarding: Why It Takes Weeks and How to Cut It to Days
  2. 02 Cost of Manual Supplier Data Entry: What Distributors Actually Lose
  3. 03 Supplier Onboarding Checklist for Distributors
  4. 04 Clear a 5,000-SKU Backlog in 90 Days Without Hiring
  5. 05 Fuzzy Matching at Scale Problems: Why Scripts Break and What to Do Instead
  6. 06 Reconcile Supplier Catalogs: A Practical Guide for Distributors
  7. 07 Catalog Matching Cost Savings: A Distributor's Sourcing Guide
  8. 08 Duplicate SKUs and Pricing Problems: How to Detect, Merge, and Prevent Them
  9. 09 Reversible Product Merge: Deduplicate Your Catalog Without Losing History
  10. 10 Cost of Duplicate Products: The Hidden Margin, Fulfillment, and Analytics Damage
  11. 11 Which Classification Standard Do You Need: ETIM, UNSPSC, or eClass?
  12. 12 Classify an Inherited Catalog: A Practical Workflow
  13. 13 Classification Drift: How to Detect, Measure, and Stop It
  14. 14 Complete Product Record Fields: All 58 You Need to Stay Sellable
  15. 15 AI Enrichment Hallucination: How to Ground Every Attribute in Source Docs
  16. 16 Fill Missing Product Attributes With Provenance
  17. 17 Trust AI-Generated Product Data: A Practical Validation Framework
  18. 18 AI Output Provenance: Why Every AI Enrichment Needs a Source Link
  19. 19 Human in the Loop Data Review for Product Catalogs
  20. 20 ChatGPT Product Recommendations: Why Competitors Appear and You Don't
  21. 21 GEO for Ecommerce Catalogs: Make Your Products Citable by AI Engines
  22. 22 How AI Shopping Agents Work: Retrieve, Rank, and Verify
  23. 23 Product Data AI Search Visibility: What Your Catalog Needs to Get Cited
  24. 24 Manufacturer Price Update Cost: What Distributors Actually Spend
  25. 25 Margin Leakage in Supplier Price Files: How to Catch It Before It Ships
  26. 26 Monitor Competitor Prices Without Drowning in False Alerts
  27. 27 ERP to Ecommerce Data Gap: Bridge 150k SKUs to a Live Storefront
  28. 28 ERP Integration Gaps in Product Data (and How to Close Them)
  29. 29 Manage Multichannel Product Feeds from One Source of Truth
  30. 30 Barcode Errors in Supplier Feeds: A Field Guide to Finding and Fixing Them
  31. 31 Catalog Launch Errors: 7 Field-Level Failures That Bounce Feeds
  32. 32 How to Clean Up the Item Master in NetSuite
  33. 33 Vertical SaaS Catalog Data: Why You Inherit the Chaos and How to Stop It
  34. 34 Build vs Buy Catalog Data API: A Platform Team Decision Guide
  35. 35 SAP Material Master Data: Fixing Duplicates and Missing Attributes
  36. 36 Deterministic Product Enrichment API: Choosing Traceable Over Black-Box
  37. 37 Do I Need a PIM? A Decision Guide for Multi-Supplier Catalog Teams
  38. 38 When a PIM Is Overkill: A Sizing Guide for Catalog Teams
  39. 39 Does a Manufacturer Actually Need a PIM?
  40. 40 Your PIM Isn't the Problem — Your Inbound Supplier Data Is
  41. 41 How to Choose a PIM (When You Actually Need One)
  42. 42 Keeping Product Data in Sync Between Shopify and Your ERP/PIM
  43. 43 Product Data Import in Dynamics 365: Avoiding Duplicate Records
  44. 44 SAP Business One + Ecommerce: Closing the Product-Data Gap