Ecommerce ops · Production
PetSmart improves search relevance and revenue with Algolia AI-powered search
The problem
PetSmart's existing search solution failed to deliver hyper-relevant results, preventing customers from quickly finding the right products across its digital channels.
Workflow diagram · grounded in source
1
Customer searches digital channel
trigger
“being able to provide search capabilities across channels (website, mobile app, in-store, etc.)”
2
AI-powered search processes query
ai_action
“AI-powered search improved both speed and relevance”
3
Relevant results delivered to customer
output
“pet parents could now easily find exactly what they were looking for, improving their overall shopping experience”
Reported outcome
After implementing Algolia, PetSmart saw search revenue increase and browse revenue lift, with pet parents able to easily find exactly what they were looking for, improving their overall shopping experience.
Reported metrics
Search revenuesearch revenue increase
Browse revenuebrowse revenue also saw a lift
Search speed and relevanceimproved both speed and relevance
Reported stack
Algolia
Frequently asked questions
What did this team achieve with this AI workflow?
After implementing Algolia, PetSmart saw search revenue increase and browse revenue lift, with pet parents able to easily find exactly what they were looking for, improving their overall shopping experience.
What tools did this team use?
Algolia.
What results were reported?
Search revenue: search revenue increase; Browse revenue: browse revenue also saw a lift; Search speed and relevance: improved both speed and relevance (source-reported, not independently verified).
How is this ecommerce ops AI workflow structured?
Customer searches digital channel → AI-powered search processes query → Relevant results delivered to customer.