Case Study: a multi-brand US auto parts retailer reduces wrong-fit returns with WiserBrand's AI search and fitment intelligence

Multi-brand US auto parts retailer cuts wrong-fitment returns by 35% with WiserBrand

A multi-brand US auto parts retailer faced significant challenges with wrong-fitment returns, which increased costs and hurt margins. Their standard Year/Make/Model filters often missed critical compatibility details, leading to customer returns and a heavy manual workload for their merchandising team. To address this, they partnered with WiserBrand to implement an AI integration solution focused on fitment intelligence.

WiserBrand built a dedicated fitment intelligence layer that moved logic away from the main site into a Solr-backed pipeline. This solution provided configuration-level matching, smarter search interpretation, and a return feedback loop. As a result, the retailer reduced wrong-fitment returns by 35% and decreased manual merchandising and fitment QA work by about 60%. The solution also enabled over 40% catalog growth and lowered zero-result rates for new vehicle searches.