E-commerce Personalization Engine
Hyper-personalized shopping experiences that boosted conversion rates by 30%.
The Challenge
Customers were abandoning carts due to generic product recommendations. The client needed to show the right product to the right user at the right time.
The 'Vibe'
"A 'personal shopper' for every visitor, anticipating their needs before they even know them."
My Solution
Developed a real-time recommendation engine using collaborative filtering and content-based models. The system analyzes browsing history, purchase data, and user demographics to provide highly relevant product suggestions via carousels, email, and checkout upsells.
Core Tech Stack
Python, n8n, SQL Database, REST API, A/B Testing Framework
The Outcome
"Increased average order value by 15% and boosted conversion rates by 30% within the first quarter. Customer satisfaction scores for 'site personalization' improved significantly."