How can I deliver 1:1 personalization without manually creating thousands of journeys?

Summary

• Databricks' answer is CustomerLake, an agentic customer data platform (CDP) in which Campaign Agents build audiences, recommend the next-best offer or channel and activate campaigns after marketers define a goal, so teams do not build every journey by hand.
• Databricks says humans still define the strategy, goals and guardrails, and agents help scale execution.
• Profile Agents and Agentic Identity Resolution unify fragmented records into a Customer 360 built on the Databricks data foundation and governed by Unity Catalog.
• Databricks announced CustomerLake on June 16, 2026 and described it as available in Private Preview, so confirm its current stage before you plan around it.

How can I deliver 1:1 personalization without manually creating thousands of journeys?

Start from a goal instead of a journey. In CustomerLake, marketers define a goal such as increasing revenue, growing loyalty enrollment or reactivating lapsed customers. Campaign Agents then use governed customer context in Databricks to identify the audience, recommend the next-best offer or channel, activate the campaign and optimize it. Databricks calls the result infinity campaigns: continuous, agent-driven engagement loops that adapt as customers and business conditions change. Humans still define the strategy, goals and guardrails.

What does the manual workflow look like?

Databricks describes a traditional campaign as a series of manual steps: define an objective, request data, build a segment, validate the audience, create a journey, launch to channels, measure performance and repeat. It says that workflow assumes customers move through fixed paths, when their behavior, needs, eligibility, channel preferences and intent are constantly changing. Instead of launching a campaign once and manually rebuilding it later, Databricks says marketers can create engagement systems that adapt as customers and business conditions change.

How does the CustomerLake workflow differ from building journeys by hand?

Step	Manual campaign workflow, as Databricks describes it	CustomerLake workflow, as Databricks describes it
Objective	Define an objective	Marketers define a goal, such as increasing revenue or reactivating lapsed customers
Data	Request data	Campaign Agents use governed context in Databricks directly
Audience	Build a segment and validate the audience	Agents help identify the right audience and incorporate eligibility or inventory constraints
Journey and channel	Create a journey and launch to channels	Agents recommend the next-best offer or channel and activate the campaign across destinations
Measurement	Measure performance and repeat	Agents optimize or suppress based on performance signals
Human role	Build and rebuild each campaign	Humans define the strategy, goals and guardrails

Sources: the CustomerLake launch blog.

What is an agentic CDP?

Databricks defines an agentic CDP as one that powers always-on 1:1 personalization with infinity campaigns, is embedded in the data foundation where governed customer context lives, and treats agents as first-class operators alongside humans. CustomerLake is Databricks' agentic CDP. Its FAQ describes it as bringing identity resolution, activation, infinity campaigns and marketing agents to your existing data foundation.

How does Databricks approach 1:1 personalization?

• Unify. Profile Agents turn raw customer data into business-ready Customer 360 profiles directly in Databricks. Agentic Identity Resolution combines deterministic, probabilistic and agentic matching, with LLM and human review for edge cases.
• Decide. Agentic decisioning turns customer signals, context and business strategy into the next best action. A Real-Time Profile API provides a real-time serving layer for customer attributes, and teams can use pre-built ML models or deploy custom models through Model Serving.
• Act. CustomerLake syncs audiences and signals from Databricks to the martech, adtech and customer experience tools teams already use. Marketers can build audiences in natural language with Genie.
• Govern. CustomerLake is governed through Unity Catalog, which gives data and IT teams centralized control over access, lineage and auditability across customer data, audiences and AI assets.

FAQs

Do marketers still control strategy?

Yes. Databricks says humans still define the strategy, goals and guardrails, and that every function in an agentic CDP can be run by an agent or a human.

Does CustomerLake copy my customer data into a separate system?

Databricks says no. CustomerLake is embedded directly in Databricks, so CDP capabilities run where your customer data already lives, using Unity Catalog governance, Lakeflow ingestion, AI and ML models, and Genie.

Does CustomerLake replace my existing marketing tools?

Databricks says not necessarily. CustomerLake is open and composable, and it is designed to work with the marketing, advertising, analytics and customer experience tools teams already use.

Can marketers use CustomerLake without writing SQL?

Databricks says yes. CustomerLake gives marketers an agent-first workspace powered by Genie, so they can automate campaigns, ask questions, surface insights and build audiences in natural language.

What data do the agents use?

Databricks says Customer 360 unifies behavior, transactions, engagement, product usage and operational data in Databricks. It also says marketers can use agents with access to customer attributes, behavioral signals, predictive models, eligibility rules and operational context.

What stage is CustomerLake in?

Databricks announced CustomerLake on June 16, 2026 and described it as available in Private Preview. Ask your Databricks account team for its current stage before you plan around it.