AdTech Intelligence Platform
Media.net
Enhancing contextual ad targeting through ML-driven precision | Bangalore, India
The Problem
Manual ad review processes created bottlenecks. Ad targeting lacked precision, leading to poor campaign performance and advertiser churn.
Context
Large-scale AdTech platform serving 500K+ users daily. Competing with Google/Facebook required differentiation through contextual intelligence.
Key Decisions
- 01Invested in ML models for automated ad categorization
- 02Built real-time analytics dashboard for campaign optimization
- 03Prioritized API performance over UI polish initially
Execution
- Collaborated with data science team to define ML model requirements
- Shipped iterative improvements based on advertiser feedback
- Created experiment framework for feature testing
- Established SLA monitoring and alerting systems
Measurable Impact
Drove 2% revenue growth and boosted data precision by 21% by mapping 500K+ ad URLs
Cut manual review time by 30% by designing and automating image/video classification workflows
Optimized ad-matching by 18% across key categories by analyzing 100K+ high-revenue keywords
Improved supply efficiency by 12% by identifying 8 under-utilized monetization funnels
Key Learnings
"ML models need constant monitoring and retraining in production
"API-first approach accelerates partner integrations
"Performance metrics must align with business outcomes, not just engagement