Retail
Personalized Recommendation Engine
85% Accuracy Rate across a 6 months delivery with 10 engineers.
Cart Abandonment
38%
Recommendation Accuracy
+40pp
Avg Order Value
32%
Monthly Revenue
35%
The Challenge
The e-commerce platform had strong traffic but weak personalization. Product recommendations felt too generic, customers struggled to discover relevant items, and cart abandonment remained stubbornly high.
Our Solution
We built a recommendation engine that combined behavioral signals, product similarity models, and faster response times so shoppers saw suggestions that better matched their context and intent.
Business Impact
The new recommendation flow improved relevance, reduced abandonment, increased average order value, and created a measurable lift in revenue within the first few months of rollout.
Performance Timeline
Our Process
Delivery Snapshot
Tech Stack
Technologies
Project Details
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