Integration with CDP, Campaign Systems, and MaxAI

CDM as the Enterprise Backbone

CDM acts as the single integration spine across:

• Campaign systems (Unica / other MA tools)
• Customer Data Platform (CDP)
• MaxAI (analytics, insights, recommendations)

Everything plugs into 360 views, Customer 360, Campaign 360, and Flowchart 360, and the metadata-driven core.

Campaign AI Integration

High-level data flow:

1. Source systems – campaign managers, marketing platforms, etc.
2. LDZ – raw campaign data staged as ldz_campaign_*.
3. RDV – Data Vault entities h_campaign for core campaign, l_campaign_* for relationships (campaign–customer, campaign–offer, campaign–channel), and s_campaign_* for campaign attributes and performance metrics
4. Campaign 360 – curated view with standardized KPIs and attributes.
5. Flowchart 360 – generated in parallel with Campaign 360, providing flowchart-level execution insights derived from the same RDV campaign entities.

MaxAI Integration: Customer / Campaign Analytics

MaxAI leverages Customer 360 and Campaign 360 views to deliver AI-powered insights, advanced analytics, and intelligent recommendations. The integration transforms customer and campaign data into actionable intelligence that helps marketers understand customer behavior, evaluate campaign effectiveness, and improve marketing outcomes.

For more information, refer to the MaxAI CDM Redbook.

• Leverages unified Customer 360 and Campaign 360 views to generate high-quality analytical insights and recommendations.
• Provides descriptive and diagnostic analytics across customer behavior, campaign performance, channel effectiveness, and campaign-level outcomes.
• Highlights response patterns based on customer attributes, segmentation criteria, and historical campaign interactions.
• Enables cross-analysis of customer behavior and campaign execution data to identify trends, correlations, and opportunities for optimization.
• Delivers natural-language responses to analytical questions through MaxAI Insights.
• Provides data quality and data volume insights by identifying anomalies and trends within Customer 360 and Campaign 360 datasets.
• Supports AI-driven campaign optimization by learning from historical customer engagement and campaign performance data to recommend improved targeting strategies and marketing actions.

CDP Integration

• Customer 360 → CDP: Customer resolution, unified demographic and behavioral attributes, consent, risk indicators, and product holdings.
• Campaign 360 → CDP: Performance feedback, segment lift, audience insights including Flowchart 360 execution-level engagement signals, which are surfaced as sub-component of Campaign 360.
• CDP uses these to power:
  • Segment definitions
  • Journey orchestration
  • Real-time activation

CDM-ML Model Integration

Introducing bidirectional CDM-ML Model Integration framework. ML model training and prediction feature views that are constructed using Customer 360 and Contact & Response History (CHRH) data from BDV, and exposed through cdm_publish_db as point-in-time–consistent inputs for model execution.

How It Works

• CDM as feature provider: cdm_publish_db exposes point-in-time–consistent ML feature views derived from Customer 360 and CHRH data for model training and scoring.
• ML model execution: Models consume Customer 360 features independently and generate predictions NBC and STO.
• ML output ingestion: Model outputs are written back into CDM via a dedicated ingestion schema (cdm_ingest_db), stored in a structured, query-optimized format.
• Customer 360 enrichment: An enrichment pipeline integrates ML predictions back into the Customer 360 layer, updating attributes with the latest NBC and STO outputs.
• Downstream activation: ML-enriched Customer 360 attributes are then accessible to AI agents, Unica Campaign, Unica Journey, and real-time interaction systems (Unica Interact).

What This Means for Users

• Customer 360 attributes may include ML-derived fields (NBC channel recommendations, optimal send-time classification) alongside standard behavioral and demographic attributes.
• These ML-enriched attributes can be used in CDP segmentation rules and MaxAI analytics just like any standard Customer 360 field.
• The ML write-back creates a continuous improvement loop and AI recommendations improve as new campaign interaction data (including Contact & Response History) flows through CDM, enriching the next round of feature views.

End-to-End Flow: CDM → 360 → AI

• Source data entered via LDZ.
• RDV canonicalizes entities and relationships.
• 360 layer builds Customer 360, Campaign 360 and Flowchart 360.
• ML models consume feature views from cdm_publish_db (derived from Customer 360 and CHRH), generate NBC/STO predictions, and write outputs back into the Customer 360 enrichment layer.
• CDP and MaxAI consume these unified views.
• Insights and performance feed back into CDM for continuous improvement.