How to build customer 360

Summary

• A unified Customer 360 integrates customer data from all sources (transactions, behavior, product usage, support, commerce) into a single trusted view, eliminating duplicate records and data quality issues.
• Build it natively in Databricks using Profile Agents to normalize, deduplicate, and continuously improve customer data with agentic identity resolution.
• Implement Campaign Agents to move from static campaigns to always-on engagement, using trusted customer context for audience building and next-best-action recommendations.
• Maintain enterprise governance through Unity Catalog with role-based access, audit logs, and data lineage across all customer data.
• Access and activate trusted customer data where it resides using Lakehouse Federation, whether in Databricks, other cloud data warehouses, cloud storage, or operational databases.

How to build customer 360
A Customer 360 is a unified, governed, 360-degree view of each customer that consolidates disparate data into a single source of truth. Without it, customer context is incomplete, duplicated across systems, and scattered, making personalization impossible at scale. Databricks CustomerLake embeds a native agentic CDP directly in your data foundation, letting you build and activate Customer 360 profiles without moving sensitive data.

What is Customer 360?

Customer 360 integrates customer data from every source (transactions, product usage, behavior, support interactions, loyalty programs, commerce events, and enrichment data) into a golden customer record. This unified view serves as the foundation for personalization, audience segmentation, and real-time campaign optimization. Legacy approaches duplicate this data across systems and operate on incomplete snapshots; a proper Customer 360 stays current and eliminates redundancy.

Key components and capabilities

Component	Purpose	Outcome
Profile Agents	Normalize, deduplicate, and enrich customer data from disparate sources	Accurate, unified customer records with reduced duplicate records and data quality issues
Agentic Identity Resolution (AIR)	Combine deterministic, probabilistic, and agentic workflows to match and merge disconnected records	High-confidence record linkage that improves continuously through feedback loops
Campaign Agents	Automate audience building, recommend next-best actions, activate across channels	Personalized engagement at scale driven by trusted customer context
Genie-powered self-service	Democratize access to customer insights for non-technical marketers	Faster campaign creation, no SQL required
Unity Catalog governance	Centralized access control, audit logs, data lineage for all customer data	Enterprise compliance and visibility
Lakehouse Federation	Access and activate customer data from Databricks and other sources without replicating it	Reduced data movement, compliance, and operational complexity

How Databricks approaches customer 360 with CustomerLake

Databricks CustomerLake operates on three principles:
Embedded. Build a Customer 360 directly on your data foundation without martech complexity or data duplication. All customer data, identity resolution, and campaign logic live in one governed platform.
Democratized. Profile Agents turn raw customer data into business-ready profiles automatically. Campaign Agents enable marketers to build audiences and activate campaigns through Genie's natural-language interface, without needing data engineering expertise.
Autonomous. Agents continuously analyze customer signals (behavior changes, engagement patterns, lifecycle stage) and recommend optimizations. The system learns from campaign outcomes to refine predictions and personalization over time.

Implementation workflow

1. Ingest and normalize data from all sources (CRM, CDP connectors, ecommerce platforms, loyalty systems, operational databases). Profile Agents automatically detect and flag data quality issues.
2. Perform identity resolution using Agentic Identity Resolution. The system combines deterministic matching (on email, phone, ID), probabilistic scoring (on similarity), and agentic workflows (using business logic and external enrichment) to unify disconnected records.
3. Enrich profiles with third-party data and calculated attributes (lifetime value, churn propensity, segment affinity). Store all enrichments in Unity Catalog for full lineage and governance.
4. Build audiences and activate campaigns using Campaign Agents. Select a business goal (retention, cross-sell, upsell), and agents automatically segment customers and recommend channel strategies.
5. Monitor and iterate. Track campaign performance, use agent feedback to refine identity resolution and profile definitions, and re-evaluate predictions quarterly.

Governance and interoperability

Customer 360 operates within a governed environment enforced by Unity Catalog. Every data access is logged, role-based permissions prevent unauthorized views, and data lineage traces how customer attributes are calculated. This ensures compliance with privacy regulations (CCPA, GDPR) and internal data policies.
CustomerLake remains open and interoperable with existing martech and adtech ecosystems. It integrates natively with identity, activation, and engagement partners, so you can activate customer insights across your existing tools. Through Lakehouse Federation, you can also access customer data from other data platforms without replicating it to Databricks first.

FAQs

What's the difference between a CDP and CustomerLake?

Traditional CDPs are point solutions that replicate customer data, create data silos, and require custom integrations with your existing data infrastructure. CustomerLake is a native agentic CDP embedded in Databricks. It works on your existing data without movement, enforces governance through Unity Catalog, and uses agents to automate customer tasks that traditionally require manual work.

Can I build Customer 360 on data that lives outside Databricks?

Yes. Lakehouse Federation lets you access and activate trusted data where it resides, whether in other cloud data warehouses, cloud object storage, or operational databases. You don't need to move data to Databricks to use CustomerLake, though many organizations choose to consolidate sensitive customer data in a governed lakehouse.

How accurate is identity resolution?

Accuracy depends on data quality and the deterministic identifiers available (email, phone, customer ID). Agentic Identity Resolution combines multiple approaches to maximize match rates and includes built-in feedback loops so the system learns from your corrections and improves continuously.

What's included in the trial?

CustomerLake is currently in Private Preview. Interested organizations can engage with Databricks to evaluate Profile Agents and Campaign Agents, with phased rollout planned for the coming quarters.