The digital bottom line: Customers only buy products that are easy to discover and evaluate. As such, rich, reliable product data is foundational to digital channels. But producing “digital ready” product data is difficult, especially on an industrial scale.
Our Product Data Wrangles automate data cleaning and enrichment so that you can meet this new standard. Together, we can make product data work effectively across all of your channels.
Product data often comes from suppliers and customers. Efficiently onboarding this data is critical to product information management. Machine learning and automation can map poorly formatted files to your standard schema with little to no intervention required.
Product data is mostly generated via data entry. As such, it’s rife with inconsistencies and errors. Cleaning and enriching corrects errors, standardizes attributes and values to your vocabulary, and fills in missing data via classification and cross-referencing.
Data work at scale is only possible with machine learning and automation. Legacy automation approaches rely on human specified rules. Now, semantic machine learning models such as Natural Language Processing (NLP) can automate any size of product data. In fact, the more data the better!
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.
Product classification is more important than ever. It’s crucial to search, it drives procurement insights, and is compulsory for logistics. As product lines expand and product experts retire, automation is the only way to excel at categorization.
Product similarity is extremely valuable but difficult to assess accurately. Model-based similarity scores enable item matching and deduping, upsell/cross-sell tagging, and spec matching at accuracy levels far beyond rules and simple “fuzzy” matching.
One of the biggest challenges to PIM and eCommerce adoption is item selection and attribute data. Getting the data is step 1, but it has to be mapped to your systems' schema and semantics. Preparing and upserting data using models and APIs is a breeze.