Behavioral Event Streaming & Recommendation System Overhaul
As Product Owner, I led a comprehensive redesign of n11.com's behavioral event streaming infrastructure to enhance user understanding and drive personalized experiences. This strategic initiative evolved into implementing smart recommendation banners that generated measurable business impact.
Leadership & Team Management
Team Composition
- •Led a cross-functional team of 11 developers including contractors
- •Mobile app developers, full-stack engineers, and data engineers
Delivery Management
- •Managed end-to-end data pipeline development and implementation
- •Balanced technical requirements with business objectives through effective stakeholder management
- •Established prioritization framework to ensure business value delivery
Strategic Vision & Company-wide Adoption
Comprehensive Strategy
Developed and presented a comprehensive behavioral data strategy to the entire organization
Documentation & Standards
Created documentation and evangelized the new approach to ensure adoption across teams
Cross-team Guidance
Guided multiple departments on leveraging the new behavioral data infrastructure
Cross-functional Collaboration
Business Alignment
- •Coordinated with design, commercial, and marketing teams to align technical capabilities with business needs
- •Partnered with UX researchers to translate user insights into actionable data requirements
Partner Integration
- •Managed relationship with third-party implementation partner to ensure seamless integration
- •Established clear requirements and success criteria for external collaborators
Technical Implementation
Event Architecture Redesign
Redesigned end-to-end event streaming architecture to capture more meaningful user behavior metrics
Algorithm Development
Oversaw development of recommendation algorithms leveraging the new behavioral data
Recommendation Diversity
Implemented various recommendation types (buy-to-buy, cart recommendations) to enhance user experience
Performance Optimization
Fine-tuned systems to deliver real-time recommendations without impacting page load times
Results & Business Impact
7%
Increase in revenue within first month of recommendation banner implementation
Enhanced UX
Simplified user shopping journey through contextually relevant product suggestions
Data Foundation
Established foundation for data-driven decision making across multiple business units
Technologies & Skills
Kafka
Event streaming
Machine Learning
Recommendation algorithms
Product Management
Strategic leadership
Data Engineering
Pipeline development