The challenge
Abbreviations, conflicting labels and invalid hierarchies made product classification repetitive and inconsistent. Broader categories and complete hierarchies required different evaluation standards.
The intervention
Data scientists developed normalization models, a hierarchical classifier and semantic PLU matching. Confidence thresholds routed uncertain or unfamiliar items to catalog specialists.
How the layers connect
Simplified architecture based on the published implementation.
- 01Product descriptions
- 02Normalize + validate
- 03Hierarchical classifier
- 04Confidence routing
- 05Catalog specialist
What changed
- Department-level validation accuracy reached 97%; full valid-hierarchy accuracy was 60%.
- 135,683 product records were evaluated. The published effort-reduction figures are planning estimates, not measured production savings.
A high score at one classification level does not prove end-to-end accuracy. The interface and review workflow must expose uncertainty at the level where people make decisions.
Source & context
Validation results, not a universal production accuracy claim. Operational effort reductions are intentionally excluded as outcomes.
Read the published source: Fast Forward Deploy: Product Intelligence