ecommerce_ops · ecommerce · workflow
Hubo grows site search conversions 7% with SearchNode AI automation and NLP
Hubo's site search suffered from poor relevance, an inability to handle Dutch and French language nuances, and required extensive manual hardcoding that created search debt without improving core performance.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User submits search query
Users type search queries on the Hubo.be site, including rare and complex long-tail terms.
Tools used
SearchNodeNLP
Outcome
After SearchNode was implemented, Hubo's site search conversions increased by 7% immediately, grew a further 4% one year later, site search usage grew by almost 10%, and CTR grew by 28%. The search team transitioned from manual hardcoding to strategic planning.
What failed first
The previous Adobe Search & Promote tool could not comprehend complex grammar rules in Dutch and French, and the manual fix approach only addressed the most frequent queries while leaving long-tail queries unoptimized, accumulating search debt.
Results
Volume7%
Grounding & classification
Source type: vendor customer story
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enterprise searchpersonalizationproduct catalogfailure mode describedhuman review describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedretailconversion increaseemployee productivitythroughput increasetime savedvendor customer storyecommerce opsautonomous resolution