Definition #
Unsupervised learning identifying micro-segments based on behavior and attributes.
Key Characteristics #
- Real-time updating
- Propensity modeling
- Lookalike discovery
- Segment naming automation
Why It Matters #
Identifies 5x more actionable segments than RFM analysis (BCG).
Common Use Cases #
- Hyper-local marketing
- Product line extensions
- Churn prevention
Examples #
- Salesforce Datorama
- Adobe Analytics
- Pecan AI
FAQs #
Q: Minimum data requirements?
A: 1k+ customer profiles with 10+ attributes each.
Q: GDPR compliance?
A: Uses aggregated insights rather than individual tracking.