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.
The previous Solr-based search engine failed to meet Lacoste's new performance standards for speed and relevance.
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.
Frequently asked questions
What did this team achieve with this AI workflow?
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…
What tools did this team use?
Algolia, Solr, InstantSearch, Query Rules, Distributed Search Network, Algolia AI Recommendations, Altima - Accenture, Telios, Early Birds.
What results were reported?
Search response time: less than 50ms; Bounce rate reduction: 88%; Mobile conversion rate increase: 62%; Conversion rates: increase in conversion rates (source-reported, not independently verified).
What failed first in this deployment?
The previous Solr-based search engine failed to meet Lacoste's new performance standards for speed and relevance.
How is this ecommerce ops AI workflow structured?
As-you-type search trigger → Route to nearest data center → NLP relevance processing → Business rules and personalization → Results delivered under 50ms.