Leroy Merlin Brasil achieves 31% CTR increase and $28M annual revenue boost with Algolia AI Search
Leroy Merlin Brasil's existing Elasticsearch search engine was too inflexible to scale, requiring significant developer involvement for even simple changes, while poor speed and irrelevant results degraded the customer experience across its large, diverse product catalog spanning multiple complex categories.
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 · Customer initiates product search
Customers search for products across Leroy Merlin Brasil's desktop, mobile, and in-app experiences.
Leroy Merlin Brasil achieved a 31% increase in click-through rate, a 15% increase in add-to-cart from search, and an estimated annual revenue increase of more than $28 million, while business teams can now independently manage and optimize search without large dedicated technical staff.
What failed first
The previous Elasticsearch search engine required developers to constantly debug and manually analyze search result rankings, and even simple changes demanded full team involvement and significant coding.