Measurement in a Privacy First World
summary
Argues that advertising measurement now relies on first-party and aggregated data and server-side matching instead of device IDs, and that building on-platform experiment frameworks (A/B experiments and lift measurement) plus semi-automated ML-supported granularity produces actionable, single-digit to mid-teen percent uplifts in pilots. Recommends investing in data pipelines, telco-aggregated tests (examples include Reliance/Jio experiments), on‑house matching and experiment tooling; reports these approaches work in early deployments.
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chapters (10)
- 0:00Introduction to privacy measurement
- 3:15Privacy regulations impact
- 6:36Planning for privacy first
- 9:25Measurement tools and data paths
- 13:11Business volume and metrics
- 17:14Hybrid data-driven analysis
- 21:17First-party data strategies
- 23:55Selecting the right tools
- 26:49Developing new business models
- 29:00Conclusion and Q&A
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