Retail & E-commerce · UrbanKart
AI personalization lifts e-commerce conversion 31%
A fashion marketplace deployed real-time recommendations and search relevance tuning across 2.3M monthly shoppers.
31%
conversion rate lift
23%
average order value increase
44%
search exit rate reduction
+$11M
annualized incremental revenue
The Problem
UrbanKart's generic, batch-updated recommendations converted poorly. Search returned irrelevant results for long-tail queries, and 71% of sessions ended without a product page view.
The Solution
We built a real-time personalization engine: session-based recommendations, vector-powered semantic search, and dynamic ranking that blends popularity, availability and margin. An experimentation framework A/B tests every ranking change against revenue per session.
Architecture
- Event streaming of clickstream into a real-time feature store
- Two-tower recommendation model with session context
- Semantic search with embeddings + traditional relevance signals
- Experimentation platform with guardrail metrics
- Edge-cached inference for sub-50ms recommendations
ROI
Incremental revenue of $11M annually against a program cost under $600k — the experimentation framework now pays for a permanent growth team.
Engagement
- Client
- UrbanKart
- Industry
- Retail & E-commerce
- Service
- AI & Software Development
- Timeline
- 14 weeks to first A/B win; personalization platform complete in 6 months
Technologies
“Every ranking change is now a measured experiment. RippleCode gave us a growth engine, not just a recommendations widget.”
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