Retail product matching: challenges & solutions

Product matching is the foundation of competitive intelligence. Without reliable matches, price comparisons are misleading and assortment analysis falls apart. But matching at scale is harder than it looks.

Common challenges

Naming inconsistency - The same product appears under different names, descriptions and categorisations across retailers.

Pack size variations - A 500ml bottle at one retailer might be listed as 0.5L at another, or bundled differently.

Private label - No shared identifier exists. Products must be matched on attributes, not codes.

Scale - Doing this for millions of products across dozens of retailers requires automation.

AI-assisted matching

Modern matching combines multiple signals:

1. Text similarity - NLP-based comparison of product names and descriptions
2. Attribute matching - Structured comparison of specifications, ingredients and dimensions
3. Image analysis - Visual similarity scoring that catches matches text alone misses
4. Human validation - Every AI suggestion is reviewed by a human before entering the dataset

Exact vs similar matches

It’s important to distinguish between:

• Exact matches - The same product (same EAN) sold across retailers
• Similar matches - Comparable products that serve the same consumer need but differ in brand, formulation or packaging

Both are valuable, but they serve different analytical purposes.

Getting started

If you’d like to see how Daltix handles matching for your categories, book a demo.