ecommerce_ops · ecommerce · workflow
LuisaViaRoma increases ARPU by up to 15% with Dynamic Yield ML-powered personalization
LVR needed to optimize the luxury buying experience — helping shoppers find the right items quickly, increasing revenue per visitor, and obtaining flexible ML-powered recommendations — after repeated failures with previous personalization providers.
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 · Personalized add-to-cart recommendations
LVR created unique recommendation experiences across countries and audience segments to upsell and cross-sell at the point of purchase.
Tools used
Dynamic Yield
Outcome
LVR achieved significant increases in average revenue per user across multiple use cases, adding millions of dollars in top-line revenue, with a 15% ARPU increase from add-to-cart recommendations, 14% from thank you page optimization, and 6% from urgency messaging.
What failed first
LVR had a history of bad experiences with earlier personalization providers before switching to Dynamic Yield.
Results
Volume15%
Cost replacedmillions of dollars in top-line revenue
Grounding & classification
Source type: vendor customer story
20 fields verified against source quotes.
personalizationrecommendation systemproduct catalogfailure mode describedmetric backednamed customertools describedvendor confirmedworkflow describedecommerceretailconversion increaserevenue increasevendor customer storyecommerce opsmarketing ops