ecommerce_ops · ecommerce · workflow
Lacoste reduces bounce rates by 88% and boosts mobile conversion by 62% with Algolia search
Lacoste's Solr-based search engine provided only basic speed and relevance functionality and did not meet the new performance standards Lacoste set for themselves, as user expectations were shaped by players like Google and Amazon. The solution also needed to be both developer-friendly and usable by business stakeholders without requiring technical expertise.
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 · As-you-type search trigger
A user types in the search box, triggering direct as-you-type feedback.
Tools used
AlgoliaSolrInstantSearchQuery RulesDistributed Search NetworkAlgolia AI Recommendations
Outcome
Lacoste achieved search results in under 50ms globally, reduced bounce rates by 88%, and boosted mobile conversion rates by 62%, while enabling business teams to directly control search relevance and rankings through Query Rules and personalization.
What failed first
The previous Solr-based search engine failed to meet Lacoste's new performance standards for speed and relevance.
Results
Time savedless than 50ms
Volume88%
Running since2017
Grounding & classification
Source type: vendor customer story
29 fields verified against source quotes.
personalizationrecommendation systemproduct catalogfailure mode describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedecommerceretailconversion increasecustomer satisfactionresponse time reductionvendor customer storyecommerce opsmarketing opsextract classify route