Product matching accuracy depends on attribute richness. Sparse product data produces weak matches. Here's how to annotate product catalogs — manually and with AI — to make matching reliable.
Text matching misses products that look identical but are described differently. File-based matching adds images, PDFs, and documents to the comparison — combining visual and textual signals for accurate results.
Most CPG forecasting failures aren't analytics problems — they're harmonization problems. Internal SKUs, syndicated codes, and retailer GTINs speak different languages, and the cost of translating between them is quietly destroying forecast accuracy.
Product images contain brand names, model numbers, colors, and condition details that aren't in your spreadsheet. AI attribute extraction turns visual information into structured fields ready for matching.
Different suppliers describe the same products differently. Learn how to match catalogs by name, SKU, specs, and AI embeddings to build a unified product taxonomy.