ecommerce_ops · ecommerce · workflow
ManoMano increased conversion rate by 20% in multiple markets after switching to Algolia
ManoMano's search team needed to help non-expert shoppers find the right DIY products even when using the wrong vocabulary, but their ElasticSearch-based solution was hard to configure, lacked basic ranking fine-tuning, and produced indexing bugs resulting in duplicate product listings.
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 · Shopper searches with imprecise vocabulary
Shoppers search for DIY and home improvement products, often using the wrong vocabulary to find them.
Tools used
AlgoliaInstantSearchAnalyticsQuery RulesQuery Categorization
Outcome
After a two-week test across Italy, Spain, Germany, and the UK, ManoMano saw conversion rates increase up to 20%, prompting an early decision to adopt Algolia.
What failed first
ElasticSearch required significant expertise to configure, lacked ranking fine-tuning capabilities, and produced indexing bugs that caused duplicate product listings.
Results
Time saved50 minutes
Volume20%
Running sincesince 2018
Grounding & classification
Source type: vendor customer story
25 fields verified against source quotes.
enterprise searchpersonalizationproduct catalogfailure mode describedmetric backednamed customerproduction runtime claimedsource backedtools describedworkflow describedecommerceretailconversion increasecycle time reductionemployee productivityvendor customer storyecommerce opsextract classify route