Enrichment can be applied to individual attributes or across many attributes, and can be performed manually or with AI assistance. The key is choosing the right approach at the right stage of the product lifecycle.
Enriching from Existing Product Data
Many products already contain useful information when they are created or imported into SKULaunch.
This can include:
• Existing product names
• Supplier descriptions
• Product codes and identifiers
• Legacy content
SKULaunch can use this information as input to populate structured attributes. This approach is commonly used when onboarding legacy catalogues or supplier files.
It is most effective when:
• Core product information already exists
• Data needs structuring rather than discovery
• Manual rekeying would be inefficient
Enriching from External Sources
Products can also be enriched using information from external sources such as:
• Brand websites
• Product detail pages
• Public specifications
• Online documentation
External source enrichment is useful when:
• Supplier data is incomplete
• Information needs to be verified
• Products are publicly documented
This approach helps fill gaps while maintaining traceability back to the original source.
AI Assisted Data Enrichment
AI assisted data enrichment helps extract and structure product data from unstructured sources.
It is typically used to:
• Identify attribute values within text, pages, or images
• Normalise data into defined formats
• Populate multiple attributes efficiently
AI assisted data enrichment works best when:
• Attribute definitions are clear
• Lists of values are well designed
• Human review is part of the process
Manual Data Enrichment
Not all enrichment should be automated.
Manual enrichment allows users to:
• Enter or correct attribute values directly
• Resolve edge cases
• Apply judgement where automation is not appropriate
Manual enrichment is often used alongside AI enrichment to ensure data quality.
Content Enrichment
Content enrichment focuses on generating written product content using structured data.
This approach is used to:
• Create website descriptions
• Generate feature bullets
• Produce channel specific copy
Content enrichment is most effective when:
• Core data attributes are already populated
• Consistency across products is important
• Content needs to scale across large ranges
Rather than copying text between products, content enrichment creates reusable, data driven content.
Choosing the Right Approach
The best enrichment approach depends on:
• Data availability
• Product complexity
• Volume
• Publishing requirements
Most teams combine multiple enrichment methods to achieve the best results.