Replacing a manual vendor with AI competitive matching: 19 competitors covered at 88% accuracy, with 130 matches the previous process missed entirely.
This retailer needed to move from a manual, vendor-reliant competitive pricing process to an automated solution capable of matching private label and branded products across 19 websites. The existing approach could not scale to the full merchandise hierarchy and was producing coverage gaps that left significant portions of the assortment without competitive benchmarking. The replacement needed to handle both exact EAN matching and AI-assisted probabilistic matching for private label products with no direct identifier.
Two proof-of-concept phases were conducted. Phase 1 matched 500 private label products and achieved 90% match rate. Phase 2 processed 38,000 product rows using a dual-method approach: exact EAN matching as the primary method, with AI probabilistic matching as secondary for private label items without direct identifiers. The system included automated web scraping with per-competitor configuration, confidence scoring on probabilistic matches, and a monthly feedback loop in which subject matter experts reviewed low-confidence results to retrain the models.
The system delivered 88% total SKU coverage at 100% match confidence across all 19 tracked competitors, with per-competitor coverage ranging from 90% to 99%. AI matching achieved 70% recall and precision on probabilistic matches. Critically, the system discovered 130 new matches that were entirely absent from the retailer's existing manual file, competitive intelligence gaps that the previous approach had no mechanism to detect.