Customer 360 Architecture
Unified customer data platform pattern. Stack choices depend on your scale, existing tools, and budget.
Click any node for details.
Decision Log
Architecture Decisions
- Identity resolution, Probabilistic matching for web/mobile; deterministic for authenticated users.
- Warehouse vs. CDP, Warehouse-native CDP saves cost if you have engineering; packaged CDP if speed > control.
- Feature engineering, SQL-based for simple features; Python + feature store for ML-heavy use cases.
- Model approach, Start with gradient boosting (XGBoost); add deep learning only if data volume justifies.
Integration Points
- Reverse ETL, Hightouch or Census to push segments to CRM, ad platforms, and marketing automation.
- Event streaming, Segment or RudderStack for real-time behavioral data collection.
- Consent propagation, Preference center synced to downstream systems for GDPR/CCPA compliance.
- BI layer, Semantic layer (dbt metrics, Looker) for consistent definitions across tools.
Overview
Stop flying blind on customer behavior. We build unified customer data platforms and predictive models that help marketing, sales, and CX teams act on real-time insights; not stale reports.
Stack Options
| Component | Enterprise | Lean |
|---|---|---|
| Warehouse | Snowflake | PostgreSQL + Citus |
| Ingestion | Fivetran | Airbyte (open source) |
| Transform | dbt Cloud | dbt Core + Airflow |
| Orchestration | Prefect | Dagster |
| Reverse ETL | Hightouch | Census |
| BI | Looker / Tableau | Metabase / Lightdash |
| ML | Vertex AI | MLflow + scikit-learn |
Measurable impact
Increased conversion
Targeted campaigns via propensity modeling and segment lift analysis.
Reduced churn
Early warning models identify at-risk customers before they leave.
Reporting efficiency
40%+ reduction in manual reporting through automated dashboards.
Unified view
Customer 360 with identity stitching across channels and touchpoints.
Technical scope
- •Unified data model (customer 360) with identity stitching across devices and channels
- •Reverse ETL to push insights back to CRM, marketing automation, and ad platforms
- •Propensity and churn modeling using gradient boosting and logistic regression
- •Customer LTV estimation and cohort analysis
- •LLM-powered insight summarization for executive reporting
- •Privacy-by-design architecture with consent management integration
Where this drives ROI
Customer 360 FAQs
Do you replace our existing CDP?
Not necessarily; we can integrate with your existing CDP (Segment, mParticle) or build a lean alternative using your warehouse as the source of truth.
What data sources can you connect?
Any SQL database, SaaS APIs (Salesforce, HubSpot, Stripe), event streams, and flat files. We use Fivetran, Airbyte, or custom connectors.
How long until we see results?
Basic dashboards in 2-3 weeks; predictive models in 6-8 weeks depending on data quality.