Learning objectives
- Understand Customer AI, Attribution AI and Anomaly Detection at a conceptual level
- Identify which datasets and schemas each service needs as input
- Map an intelligent service output back into a reusable audience
What this module covers
- Where ML-derived attributes live on the profile
- Feeding model outputs back into Real-Time CDP segmentation
- Governance considerations for AI-derived attributes
Common mistakes to avoid
- Treating a model score as ground truth
- Skipping the documented input requirements of each service
- Using derived attributes in activation without governance review
Time and prerequisites
Time investment: 2 hours. Prerequisites: Modules 2-4 complete.
Lab screenshots, sandbox names and configuration IDs are shared with enrolled learners inside the Academy LMS, not on this public page.
Move through the track
← Module 03: Real-Time Customer ProfileModule 06: Real-Time CDP: Build a Segment →All modules
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