ecommerce_ops · ecommerce · workflow
BIG W improves search conversion by 7% and basket size by 4.7% with Algolia AI Search
After re-platforming to headless commerce, BIG W found its existing search engine was compromising the customer experience with highly irrelevant results, while a small web development team meant any replacement had to be robust and require minimal ongoing technical investment.
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 searches product catalogue
Algolia powers all of BIG W's product listing pages, search results, browse, and navigation.
Tools used
Algolia SearchAlgolia RecommendDynamic Re-RankingQuery SuggestionsRulesMerchandising StudioAdobe Experience Manager (AEM)
Outcome
After deploying Algolia Search and Recommend, BIG W reduced search exits by 10%, improved conversion from search by 7%, increased basket size by 4.7%, and improved NPS by 4 points.
What failed first
BIG W's previous search engine, Solr on a Hybris backend, delivered highly irrelevant results — a query for 'blue socks' would surface unrelated blue items and generic socks, with the correct match buried on page four or five.
Results
Volume10%
Running since2022
Grounding & classification
Source type: vendor customer story
30 fields verified against source quotes.
enterprise searchpersonalizationrecommendation systemproduct catalogfailure mode describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedecommerceretailconversion increasecustomer satisfactionemployee productivityrevenue increasevendor customer storyecommerce ops