ecommerce_ops · ecommerce · workflow
Vape Superstore increases conversions by 35% and reduces search speed to 2ms with Algolia AI Search, Personalization, and Recommend
Vape Superstore's previous search platform was cumbersome and slow, and because the company cannot use paid advertising, it depends entirely on organic traffic and on-site search quality to drive conversions across a massive product catalog.
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 arrives on site
Customers land on the Vape Superstore website and begin their shopping journey.
Tools used
AlgoliaAI RerankingRecommendSearch APIPersonalizationKarmoonNostoBoost & Search Filter
Outcome
After deploying Algolia in 2022, Vape Superstore saw conversions increase by 35 percent over the previous search platform, average order value grow by 5 percent, and average search speed drop to 2 milliseconds.
What failed first
The previous search provider (Boost & Search Filter) was slow and lacked relevance accuracy, and the previous merchandising platform (Nosto) delivered a less smooth experience with slower load speeds.
Results
Time saved2 milliseconds
Volume35 percent
Running since2022
Grounding & classification
Source type: vendor customer story
35 fields verified against source quotes.
enterprise searchpersonalizationrecommendation systemproduct cataloghuman review describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedecommerceretailconversion increasecustomer satisfactionresponse time reductionrevenue increasevendor customer storyecommerce opsmarketing opsdata sync enrichment