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Design an Ad Targeting Platform

Build a real-time ad targeting system with ML-powered user profiling, bid optimization, and privacy-compliant audience segmentation at massive scale.

System DesignAdTechReal-time ML
Q: What's the expected scale of ad requests and user data processing?
A: Handle 1M+ ad requests per second with sub-100ms response times. Process 10TB+ behavioral data daily from 2B+ active users. Support 100K+ concurrent campaigns with real-time optimization.
Engineering Implications: Massive scale requires distributed architecture with edge caching, fast ML inference, and efficient data pipelines. Need to optimize for both latency (real-time serving) and throughput (batch processing).
Q: What privacy and compliance requirements must the system satisfy?
A: Full GDPR/CCPA compliance with consent management, data anonymization, right-to-deletion, and audit trails. Support cookieless targeting and privacy-safe cross-device identification.
Engineering Implications: Privacy-first architecture: differential privacy for aggregation, federated learning for model updates, secure multi-party computation for identity resolution. Balance targeting effectiveness with privacy protection.
Q: What targeting capabilities and ML models should be supported?
A: Support demographic, behavioral, lookalike, and real-time intent targeting. Real-time ML scoring for user segmentation, bid optimization, and fraud detection with continuous learning from campaign performance.
Engineering Implications: ML pipeline needs real-time inference (<50ms), batch training (hourly/daily), A/B testing infrastructure, and model performance monitoring. Multiple models running simultaneously for different use cases.
Q: How should the system handle real-time bidding and campaign optimization?
A: Integrate with major ad exchanges via RTB protocols. Dynamic bid optimization using reinforcement learning, budget pacing, frequency capping, and creative rotation with real-time performance feedback.
Engineering Implications: RTB requires ultra-low latency (<100ms end-to-end), high availability, and sophisticated auction logic. Campaign optimization needs multi-objective optimization balancing CPA, ROAS, and reach goals.
Q: What are the business metrics and attribution requirements?
A: Track CTR, conversion rates, ROAS, LTV, and incremental lift. Cross-platform attribution covering web, mobile, CTV, and offline channels with view-through and multi-touch attribution modeling.
Engineering Implications: Attribution system needs probabilistic modeling for cross-device tracking, incrementality measurement via holdout testing, and integration with measurement partners while maintaining user privacy.
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Interview Practice Questions

Practice these open-ended questions to prepare for system design interviews. Think through each scenario and discuss trade-offs.

1

Global Programmatic Advertising Platform: Design a system handling 10M+ bid requests per second with sub-100ms response times across multiple ad exchanges. Address real-time bidding, budget management, fraud prevention, and attribution tracking across global markets.

2

Privacy-First Ad Targeting System: Build a modern ad targeting platform that works without third-party cookies, using first-party data, contextual signals, and privacy-preserving techniques. Address GDPR compliance, consent management, and measurement challenges.

3

Cross-Platform Attribution System: Design an attribution platform tracking user journeys across mobile apps, web, connected TV, and offline channels. Handle viewability measurement, incrementality testing, and multi-touch attribution modeling at scale.

4

Dynamic Creative Optimization Platform: Build a system that automatically generates and optimizes ad creatives using AI. Handle image generation, text optimization, A/B testing at scale, and performance feedback loops for millions of ad variants.

5

Connected TV Advertising Platform: Design an advertising system for streaming services and connected TV platforms. Address inventory management, audience targeting without cookies, frequency capping, and integration with traditional TV measurement.

6

Retail Media Network Platform: Build an advertising platform for retailers to monetize their customer data and digital properties. Handle sponsored products, display advertising, measurement, and integration with e-commerce platforms while maintaining customer experience.