Case Study: Big Box Retailer achieves 91% fewer zero-results searches and 30% more search-influenced orders with Lucidworks Never Null

How one of the world’s top five retailers used Lucidworks Never Null and semantic vector search to increase AOV by 28% and decrease zero results queries by 91%

A top-five big-box retailer was facing frequent “no results” searches and poor relevance after a like-for-like Solr replacement left search driven by brittle keyword rules. With a broad product assortment but limited varieties per type, misspellings, long-tail and thematic queries, and stock/location nuances often returned nothing — creating frustrated shoppers, high bounce rates, and heavy manual rule-curation for the small search team.

Lucidworks implemented semantic vector search using its Never Null deep‑learning encoder to learn from customer behavior, broaden searchable fields, and surface purpose‑matching alternatives instead of exact keyword matches. The change automated relevance tuning and reduced the team’s rules burden; over Cyber Five it cut zero‑results queries by 91%, increased search‑influenced orders by 30% and order value by 28%, processed 680M searches with sub‑second responses and no outages.