Extract and Standardize
Product data cleaning starts with extraction and standardization. Extraction means separating out individual pieces of information that often end up in overloaded description fields. Standardization is the translation of inconsistent labels, terms and categories into your managed corporate vocabulary and standards. Our Wrangles “mine and align” all available information from raw data, preparing it for more advanced enrichment.
Classify and Categorize
Product classification is more important than ever. It’s crucial to search, it drives analytical insights, and is compulsory for logistics. As product lines expand and product experts retire, automation is the only way to excel at categorization. Classification is a strength of Natural Language Processing, and our Wrangles show it. They can classify your products into any number of hierarchical and flat schema. Training models on your specific products and categories is fast and extremely effective. In fact, our models consistently outperform status quo approaches at a small fraction of the time and expense.
Match Related Products
Product similarity is extremely valuable but difficult to assess accurately. Our Matching Wrangles use semantic information and numerical properties to quantify similarity. Similarity scores enable deduping, upsell/cross-sell tagging, SKU spec matching, and item cross-referencing at accuracy levels far beyond rules and “fuzzy” matching.
Product Information Management (PIM) Integration
One of the biggest challenges to PIM adoption is mapping disparate inbound data to a specific PIM configuration. Our Wrangles produce PIM-ready product data mapped to your specific PIM schema. Thus prepared, upserting data via files or APIs is a breeze.