AI-Powered Recommendation System for E-commerce
Built an intelligent recommendation engine using machine learning to deliver personalized product suggestions, increasing conversion rates by 45% and average order value by 28%.
Task
Build an intelligent recommendation engine using machine learning to deliver personalized product suggestions and enhance customer shopping experience across all touchpoints.
Technologies
Python, TensorFlow, Redis, Elasticsearch, Kafka, Lambda, DynamoDB, S3, Docker, Kubernetes, Dagster, MLflow, Grafana, CloudWatch.
Result
Achieved 45% increase in conversion rates, boosted average order value by 28%, improved customer engagement by 60%, and delivered real-time recommendations in under 100ms.
A rapidly growing e-commerce platform with millions of customers and over 100,000 products was struggling to help shoppers discover relevant items in their vast catalog. Despite having rich customer data and browsing patterns, the platform relied on basic rule-based recommendations that failed to capture the nuanced preferences and behaviors of individual users.
The result was suboptimal customer experience: shoppers spent excessive time searching for products, many left without making a purchase, and those who did buy often selected lower-value items than they might have preferred. Conversion rates remained stagnant at 2.1%, well below industry benchmarks, while average order values failed to grow despite expanding product selection.
Customer engagement metrics revealed the problem's scope: users viewed an average of only 3.2 product pages per session, with 78% bouncing after viewing just one item. The platform's merchandising team struggled to manually curate relevant product displays for different customer segments, often relying on basic popularity rankings that didn't account for individual preferences or emerging trends.
Data Privacy & Security
Implement GDPR-compliant data processing with advanced encryption to protect customer information while enabling personalized experiences
Real-time Processing
Design cloud-native architecture that processes millions of user interactions and generates personalized recommendations in real-time at scale
Multi-algorithm Engine
Create adaptive recommendation models combining collaborative filtering, content-based filtering, and deep learning for optimal personalization
Automated Optimization
Eliminate manual curation through automated A/B testing, model retraining, and performance optimization based on real user behavior
Revenue Growth
Increase conversion rates and average order values through precise product recommendations and cross-selling opportunities
Cross-platform Integration
Enable seamless recommendation delivery across web, mobile, email, and in-store channels for unified customer experience
Personalized Interface
Provide intuitive, context-aware product displays that adapt to individual user preferences and shopping patterns
Dynamic Adaptation
Quickly respond to changing trends, seasonal patterns, and new product launches with intelligent recommendation adjustments
Recommendation Quality
Continuously monitor recommendation relevance, diversity, and business impact to maintain high-quality customer experiences