AI-based product categorization
How to run AI categorization to reduce manual work and maintain accuracy on a large catalog.
Key principle: AI works most accurately when names, attributes, and category structures are already cleaned of duplicates and noise.
When to run AI categorization
- When bulk importing a new supplier.
- When manual categorization takes too much time.
- Before preparing the feed for advertising channels.
2. Preparation before launch
- Clean up names from technical noise and duplicate fragments.
- Check the basic attributes: brand, type, model, compatibility.
- Break down complex product groups into separate launch packages.
- Launch the first test on a sample of 100-200 products.
3. Quality control of the result
- Check not only the top level, but the final subcategory.
- Individually monitor "risky" products with similar names.
- Log frequent errors and use this as a checklist for the team.
- After stabilization, switch from sampling to the full catalog.
4. Common errors
- Vague names without product type.
- One large mixed batch without group division.
- No manual verification after the first run.