Unifying Online and Offline Worlds
summary
A machine learning pipeline that merges website, social, email and point‑of‑sale data into a centralized store and uses probabilistic matching plus deterministic IDs, graph‑based entity resolution and privacy‑preserving hashing to link customers across devices. Models combine gradient boosting, neural nets and survival analysis to measure incrementality; results report social amplifying email by 34% and instore visits after browsing converting at 2.8x. Outputs feed automated budget optimization, AB tests and campaign activation; privacy via federated learning and differential privacy and a recommended MVP starting with two channels.
AI-written summary · about
chapters (11)
- 0:00Intro: Meet Ash & the Goal—Unifying Online + Offline Attribution
- 0:16The Modern Consumer Journey (Why Paths to Purchase Are Non‑Linear)
- 0:46Why Traditional Attribution Fails: Fragmentation, Linear Models, Offline Gaps
- 1:25The Unified ML Attribution Framework: Data, Models, Identity, Activation
- 2:24Cross‑Channel Identity Resolution: Linking People Across Devices & Stores
- 3:10Feature Engineering + ML Models: Capturing Interactions & Incrementality
- 3:52What You Discover: Cross‑Channel Synergies You’d Otherwise Miss
- 4:23Activation: Turning Attribution Insights into Budget & Campaign Decisions
- 4:51Future‑Proofing: Privacy‑First, Adaptive Retraining, and AI/LLM Integration
- 5:26Measuring Success: ROI, Budget Efficiency, and Accuracy Gains
- 5:51Roadmap + Wrap‑Up: Start Small, Build Identity, Iterate Fast
related talks
11mMaximizing personalization and AI models with quality customer data
29mUnified Customer Data Management
27mTransform online shopping experiences with personalized recommendations
23mCustom-made games with machine learning and big data
25mRevolutionizing Customer Experience Through AI & Data
21mDemystifying Machine Learning for Programmatic UA & Retargeting
32mHow to Machine Learn-ify any Product
27mUnlocking Product Growth With Big Data, Data Science And Machine Learning