Salesforce Data 360 vs CDP: Differences, Architecture, Features, Use Cases & When to Choose Data 360

Customer Data Platforms have traditionally been designed to solve a specific problem: customer data exists across multiple systems, but marketing and customer-facing teams need a unified view of the customer.

That problem has not disappeared.

What has changed is the scale of the problem.

Enterprise organizations now need customer data to support marketing personalization, sales intelligence, customer service, commerce, analytics, automation, and AI agents. A platform that only creates marketing audiences may no longer be enough.

This is where Salesforce Data 360 enters the conversation.

Salesforce Data 360 is an evolution of Salesforce's Customer Data Platform capabilities. Salesforce describes Data 360 as extending beyond traditional CDP use cases into broader enterprise data, real-time processing, AI, and use cases across sales, service, commerce, marketing, and Agentforce.

There is also an important terminology update. Salesforce rebranded Data Cloud as Data 360 on October 14, 2025. Salesforce states that the functionality and content remained unchanged during the rebrand, although documentation may still contain references to Data Cloud.

So, is Data 360 a CDP?

Yes. But it is better understood as an enterprise data and activation platform that includes CDP capabilities rather than treating it as only a traditional CDP.

This guide explains the differences between Salesforce Data 360 and the broader CDP category, how their architectures compare, where each approach fits, and how enterprises can decide which option makes sense.

What Is a CDP?

A Customer Data Platform (CDP) is a technology platform designed to collect customer data from multiple sources, unify that information around customer identities, create usable customer profiles, and make those profiles available for segmentation, analytics, personalization, and activation.

A typical CDP brings together information such as:

• CRM records
• Website activity
• Mobile application behavior
• Purchase history
• Email engagement
• Advertising interactions
• Customer service interactions
• Loyalty activity
• Product usage
• Consent and preference data

The basic objective is simple:

Turn fragmented customer data into an actionable customer profile.

For example, an enterprise might have:

• Customer details in a CRM
• Orders in an ERP
• Website behavior in an analytics platform
• Email engagement in a marketing platform
• Service history in a support system
• Loyalty information in another application

A CDP can connect these sources and help identify that several records actually belong to the same customer.

Instead of treating them as separate records:

CRM customer + website visitor + purchaser + service customer

the organization can work toward a unified identity:

One customer with a connected history across multiple touchpoints.

Salesforce itself describes a CDP as a platform that collects and stores customer information, while positioning Data 360 as something that goes beyond traditional CDP capabilities.

What Is Salesforce Data 360?

Salesforce Data 360 is Salesforce's data platform for connecting, preparing, harmonizing, unifying, analyzing, and activating data across enterprise systems.

It supports traditional CDP capabilities such as:

• Identity resolution
• Unified customer profiles
• Segmentation
• Audience activation
• Customer data unification
• Cross-channel personalization

But Salesforce also positions Data 360 as a broader data foundation for:

• Sales
• Service
• Marketing
• Commerce
• Analytics
• Automation
• AI
• Agentforce

Salesforce says Data 360 can connect different types of data through batch, streaming, or real-time approaches, prepare and harmonize data, apply identity resolution, create insights, and provide data context for Agentforce.

It can also work with external data sources and support zero-copy approaches, allowing organizations to use data from external platforms without necessarily creating another copy of the underlying data.

That changes the architectural conversation.

A traditional CDP discussion often starts with:

How do we build better customer profiles and audiences?

A Data 360 discussion can start with:

How do we create a trusted enterprise data foundation that can power customer experiences, analytics, automation, and AI?

Is Salesforce Data 360 a CDP?

Yes. Salesforce Data 360 includes CDP capabilities, but it extends beyond the traditional CDP category.

Salesforce explicitly describes Data 360 as a CDP while also positioning it as a broader platform for enterprise data and AI use cases.

The distinction is important.

A traditional CDP typically focuses on:

1. Data collection
2. Identity resolution
3. Customer profiles
4. Segmentation
5. Activation

Data 360 includes those capabilities while adding broader capabilities around:

1. Enterprise data integration
2. Customer 360 data modeling
3. Real-time data
4. Data graphs and insights
5. Zero-copy data access
6. Cross-cloud Salesforce activation
7. AI and Agentforce context
8. Sales, service, commerce, and marketing use cases

So the better comparison is not:

Data 360 vs CDP

as if they were two completely separate product categories.

The more accurate comparison is:

Salesforce Data 360 vs traditional or standalone CDP platforms.

Salesforce Data 360 vs CDP: Quick Comparison

The exact capabilities of a CDP depend heavily on the vendor and edition. "CDP" is a category, not a single standardized product.

The Biggest Difference: Scope

The biggest difference between Data 360 and a traditional CDP is scope.

A CDP can be implemented primarily to solve marketing problems.

For example:

• Create unified customer profiles
• Build marketing audiences
• Personalize campaigns
• Suppress customers from advertising
• Trigger customer journeys

Data 360 can support those same requirements.

But an enterprise may also want to use the same data foundation for:

• Sales recommendations
• Service personalization
• Commerce experiences
• Customer lifetime value analysis
• Real-time decisioning
• AI agents
• Operational automation
• Customer service context
• Revenue intelligence

That broader scope is one of the main reasons Salesforce positions Data 360 beyond a traditional CDP.

Data Architecture: Data 360 vs Traditional CDP

Architecture is where the difference becomes more significant.

A simplified traditional CDP architecture might look like:

Data Sources → Ingestion → Identity Resolution → Customer Profiles → Segmentation → Activation

A Data 360 architecture can extend that model:

Enterprise Data Sources → Ingestion / Zero Copy → Data Preparation → DLOs → Data Model Objects → Identity Resolution → Unified Profiles → Insights → Segmentation → Activation → Salesforce / External Channels / AI

Salesforce's Data 360 architecture uses data lake objects, data model objects, identity resolution, unified profiles, data graphs, insights, and activation capabilities.

This makes Data 360 useful not only for creating audiences but also for creating a reusable enterprise data foundation.

Data Ingestion

Both CDPs and Data 360 need to solve the same fundamental problem:

How do we bring data from different systems together?

Common sources include:

• CRM
• ERP
• Data warehouse
• Data lake
• Website
• Mobile application
• Marketing systems
• Service platforms
• Commerce platforms
• Advertising platforms
• Loyalty systems
• Custom applications

Data 360 supports batch, streaming, and real-time data patterns. Salesforce also supports zero-copy approaches for certain external data environments.

This is particularly useful for enterprises that already have significant investments in data warehouses and lakehouses.

Instead of assuming that all enterprise data must be copied into a new platform, architecture teams can evaluate where data should remain and how Data 360 should access or use it.

Identity Resolution

Identity resolution is one of the defining capabilities of a CDP.

Imagine these records:

CRM

John Smith
[email protected]

Website

J. Smith
[email protected]

Commerce

John Smith
Phone: +1-555-0100

Service

John Smith
Customer ID: 100245

Without identity resolution, the business may treat these as separate records.

With identity resolution, the systems can establish relationships between the records and create a more complete customer view.

Data 360 supports identity resolution and identity graphs using matching approaches that can include exact and fuzzy matching.

The result is not simply a larger database.

The objective is a more actionable understanding of the customer.

Unified Customer Profiles

A CDP generally aims to create a unified customer profile.

Data 360 does this through its data model and identity capabilities.

Salesforce describes Data 360 as creating unified profiles across touchpoints by connecting identities, engagement data, orders, loyalty information, and marketing journeys.

A profile could combine:

• Customer information
• Purchases
• Website activity
• Email engagement
• Service cases
• Product usage
• Loyalty status
• Consent
• Calculated metrics
• Behavioral signals

This profile can then support multiple business functions.

For example:

Marketing

"Which customers should receive this offer?"

Sales

"Which accounts show strong buying intent?"

Service

"What has this customer already experienced?"

Commerce

"What should we recommend next?"

AI

"What context should an agent have before taking action?"

This cross-functional capability is where Data 360 becomes significantly different from a marketing-only customer data implementation.

Customer 360 Data Model

Another major difference is Salesforce's Customer 360 Data Model.

Data 360 allows source data to be mapped from its original structure into a standardized and extensible data model. Salesforce describes this process through data lake objects, data model objects, and relationships.

This creates a common semantic layer.

Instead of every application understanding customer data differently, the organization can establish common concepts such as:

• Individual
• Account
• Contact
• Product
• Order
• Engagement
• Campaign
• Loyalty
• Service interaction

This can make downstream segmentation, activation, analytics, and application integration easier to govern.

Data 360 vs CDP for Real-Time Data

Traditional CDP implementations have historically been associated with batch-oriented customer data workflows.

Modern CDPs increasingly support real-time processing, so this is no longer a simple category-level distinction.

The important question is:

How much real-time capability does the specific platform provide, and which use cases can actually use it?

Data 360 supports batch, streaming, and real-time capabilities. Salesforce documentation describes real-time processing capabilities that can support sub-second processing across supported customer interactions.

This can support scenarios such as:

1. Customer performs an action
2. Data is captured
3. Customer profile or context is updated
4. Segment or decision logic evaluates the signal
5. A downstream action is triggered

For enterprises, real-time architecture becomes especially important for:

• Fraud-related experiences
• Customer retention
• Product recommendations
• Digital commerce
• Service escalation
• Journey orchestration
• Real-time personalization
• AI agent context

Zero Copy: A Major Enterprise Differentiator

One of the more important Data 360 capabilities for enterprise architecture teams is zero-copy integration.

Traditional data architecture often involves:

System A → ETL → Data Warehouse → ETL → CDP → Activation Platform

Every copy introduces:

• Storage requirements
• Data movement
• Pipeline maintenance
• Latency
• Governance concerns
• Duplication

Zero-copy approaches can reduce the need to physically duplicate certain data.

Salesforce positions Data 360 as capable of working with data from external data platforms through zero-copy architecture. Salesforce specifically references environments including Snowflake, Databricks, Google BigQuery, and Amazon Redshift.

For enterprises with mature data platforms, this can materially change the implementation architecture.

Salesforce Data 360 vs CDP for Marketing

Marketing is still one of the strongest CDP use cases.

Both traditional CDPs and Data 360 can support:

• Customer segmentation
• Audience creation
• Campaign personalization
• Journey orchestration
• Advertising activation
• Suppression audiences
• Lifecycle marketing
• Behavioral targeting
• Customer retention

Data 360 adds deeper integration into the Salesforce ecosystem.

This becomes particularly useful when marketing needs to combine:

Marketing data + CRM data + service data + commerce data + external data

For example:

A customer:

1. Purchased a product.
2. Opened a service case.
3. Has not renewed.
4. Visited the pricing page.
5. Engaged with an email.
6. Has a high customer value.

A unified data foundation can use those signals to create a more relevant audience or journey.

Salesforce Data 360 vs CDP for Sales

Traditional CDPs may support sales use cases, but this varies significantly by platform.

Data 360 is designed to work within the broader Salesforce ecosystem.

Sales teams can potentially benefit from unified information such as:

• Marketing engagement
• Purchase history
• Service history
• Website activity
• Product usage
• Account relationships
• Customer lifetime value
• Behavioral signals

This can help sales teams move from:

CRM record

to:

Customer context.

For example, a salesperson preparing for a renewal conversation could have access to:

• Previous purchases
• Open service cases
• Product adoption
• Marketing engagement
• Contract information
• Customer sentiment indicators

The value is not simply having more data.

The value is making relevant data available at the point of decision.

Salesforce Data 360 vs CDP for Customer Service

Customer service is another area where broader enterprise data can matter.

A service representative may need to understand:

• Customer identity
• Previous purchases
• Service history
• Product usage
• Loyalty status
• Marketing interactions
• Previous complaints
• Account value
• Open opportunities

A traditional CDP can potentially provide some of this information.

Data 360's Salesforce-native architecture can make the same customer context available across Salesforce applications.

That creates an opportunity to connect:

Data → Customer Profile → Service → Automation → AI

This becomes especially relevant for Agentforce.

Data 360 and Agentforce

One of the biggest reasons enterprises may consider Data 360 today is AI.

AI systems need context.

A customer service agent cannot reliably answer a question if it only sees a fragmented service record.

An AI agent may need access to:

• Customer identity
• Account information
• Orders
• Service cases
• Product information
• Usage signals
• Preferences
• Consent
• Previous interactions
• Calculated insights

Salesforce positions Data 360 as a foundation for providing data context to Agentforce.

This creates a different strategic model:

CDP

Customer data → Customer experiences

Data 360

Enterprise data → Customer experiences + automation + analytics + AI agents

That distinction can be important when an organization is building an AI roadmap.

Data 360 vs CDP: Segmentation and Activation

Segmentation remains a core CDP capability.

A business may want to create a segment such as:

Customers who purchased Product A in the last 90 days, have not opened a support case, and have engaged with at least one email campaign.

The resulting audience can then be activated.

Data 360 supports segmentation and activation across Salesforce and external destinations.

Salesforce documentation describes Data 360 segmentation as a process of creating useful groups from data and publishing those segments to activation targets.

Activation can include destinations such as:

• Marketing Cloud
• Personalization
• Commerce
• Data 360
• External platforms
• File storage
• Advertising platforms

The important architectural point is that segmentation and activation are connected to the underlying unified data foundation.

Data Governance: Data 360 vs CDP

Enterprise data platforms need more than data ingestion.

They need governance.

Important governance areas include:

• Data ownership
• Data quality
• Consent
• Access control
• Identity matching
• Data retention
• Data lineage
• Data residency
• Data classification
• Security
• Activation permissions

Salesforce recommends organizations evaluate data strategy, architecture, existing data sources, unified profiles, users, permissions, and goals before implementing Data 360.

This is important because implementing a CDP or Data 360 without a clear governance model can simply create a better-organized version of the same data problems.

Implementation Complexity

A common mistake is assuming that a CDP implementation is simply:

Connect sources → create profiles → launch campaigns

Enterprise implementations are rarely that simple.

The actual work can include:

1. Data discovery
2. Source inventory
3. Data quality assessment
4. Identity strategy
5. Data mapping
6. Data model design
7. Integration architecture
8. Security design
9. Consent management
10. Segmentation strategy
11. Activation design
12. Testing
13. Migration
14. User adoption
15. Governance
16. Optimization

Data 360 implementations can become even broader when organizations use:

• Multiple Salesforce clouds
• ERP systems
• Data warehouses
• Legacy CRM platforms
• Custom applications
• External data
• Real-time data
• AI use cases

Therefore, platform selection should happen alongside architecture planning.

Salesforce Data 360 vs CDP: Cost Considerations

Cost should not be evaluated solely on the license price.

A better model is:

Platform Cost + Implementation Cost + Integration Cost + Data Engineering Cost + Governance Cost + Operations Cost

For Data 360 specifically, Salesforce has multiple licensing and consumption models. Salesforce's current pricing documentation describes profile-based options alongside consumption-based Data Services/Flex Credit approaches, with pricing and availability subject to change.

Salesforce currently lists Profiles at $240 per 1,000 profiles per year and Enterprise Profiles at $420 per 1,000 profiles per year on its public pricing page, while additional consumption-based services can apply depending on the implementation.

However, comparing this directly against another CDP's headline license price can be misleading.

The real question is:

What will the organization spend to achieve the required business outcomes?

For example, a cheaper CDP may require additional integration work to connect it with Salesforce.

A more expensive platform may reduce duplication if it replaces several separate data workflows.

The right comparison is therefore total cost of ownership, not license price alone.

When a Traditional CDP May Be the Better Choice

Salesforce Data 360 is not automatically the right answer for every organization.

A standalone CDP may be appropriate when:

1. Marketing is the primary requirement

If the organization primarily needs:

• Audience segmentation
• Marketing personalization
• Campaign activation
• Customer profiles

a focused CDP may be sufficient.

2. Salesforce is not the strategic CRM

If the organization does not have Salesforce as a major part of its customer technology stack, the advantages of native Salesforce integration may be less significant.

3. The use case is relatively narrow

A smaller implementation may not need the broader capabilities of an enterprise data platform.

4. Existing architecture already solves enterprise data requirements

If a company has a mature data platform and only needs a specialized activation layer, adding a broad data platform may create unnecessary complexity.

When Salesforce Data 360 Makes More Sense

Data 360 becomes more compelling when the organization needs a shared data foundation across multiple business functions.

It can be particularly relevant when:

1. Salesforce is central to the enterprise architecture

If Salesforce already powers:

• Sales
• Service
• Marketing
• Commerce
• Customer engagement

Data 360 can fit naturally into the ecosystem.

2. Multiple customer data sources need to be unified

For example:

CRM + ERP + Website + Commerce + Service + Marketing + Data Warehouse

3. Real-time customer context matters

Organizations building real-time experiences may need more than batch-oriented customer profiles.

4. AI is a strategic priority

If Agentforce and other AI use cases are part of the roadmap, having governed and unified customer context becomes increasingly important.

5. Data warehouses and lakehouses are already part of the architecture

Zero-copy capabilities can help enterprises evaluate how existing data infrastructure can work with Data 360 rather than automatically creating another data silo.

6. Sales, service, marketing, and commerce need the same customer truth

This is one of the strongest arguments for a broader data foundation.

Data 360 vs CDP Decision Framework

Use this framework when evaluating the platforms.

Data 360 vs CDP: A Practical Enterprise Example

Consider a global retail organization.

Its architecture includes:

• Salesforce CRM
• SAP ERP
• E-commerce platform
• Mobile application
• Customer service platform
• Snowflake
• Marketing automation
• Advertising platforms

The organization wants to:

1. Build unified customer profiles.
2. Identify high-value customers.
3. Personalize marketing.
4. Improve service experiences.
5. Give sales representatives more customer context.
6. Activate audiences across advertising channels.
7. Provide AI agents with trusted customer information.

A marketing-focused CDP could solve parts of this problem.

But the organization is no longer solving only a marketing problem.

It is solving an enterprise customer data problem.

That is where Data 360 can become strategically attractive.

The architecture can be designed around:

SAP + CRM + Commerce + Service + Web + Mobile + Snowflake → Data 360 → Identity Resolution + Unified Customer Data → Insights + Segments + Real-Time Signals → Marketing + Sales + Service + Commerce + Agentforce

This is much broader than the original CDP model.

Common Mistakes When Comparing Data 360 and CDPs

Mistake 1: Comparing Only License Prices

A lower subscription price does not automatically mean a lower implementation cost.

Always evaluate TCO.

Mistake 2: Assuming Every CDP Is the Same

CDPs differ significantly in:

• Data ingestion
• Identity resolution
• Real-time capabilities
• Data modeling
• Activation
• APIs
• AI
• Governance
• Integrations

Compare actual capabilities, not category labels.

Mistake 3: Treating Data 360 as Only a Marketing Tool

This can limit the business case.

Data 360 can support use cases across multiple Salesforce functions and AI scenarios.

Mistake 4: Ignoring Existing Data Architecture

Before choosing a platform, map:

• CRM
• ERP
• Data warehouse
• Data lake
• Marketing systems
• Service platforms
• Commerce
• Customer identity
• Analytics
• AI systems

Then determine what should move, what should integrate, and what should remain where it is.

Mistake 5: Starting With Technology Instead of Use Cases

Start with business outcomes.

For example:

Bad starting point:

"We need a CDP."

Better starting point:

"We need to reduce customer identity duplication, improve personalization, and give sales and service teams a unified customer view."

The second statement gives the architecture team something measurable to design around.

How to Choose Between Data 360 and a CDP

A practical selection process can follow eight steps.

Step 1: Define Business Outcomes

Identify what the platform must accomplish.

Examples:

• Increase conversion
• Improve retention
• Reduce service effort
• Improve customer lifetime value
• Improve sales productivity
• Enable AI agents

Step 2: Map Customer Data

Create a source inventory.

This exposes the actual complexity.

Step 3: Define Identity Requirements

Determine:

• What identifies a customer?
• What identifies an account?
• Can one person have multiple accounts?
• How are duplicates handled?
• Which identifiers are trusted?
• How are anonymous users handled?

Step 4: Define Activation Requirements

Identify where data needs to go.

For example:

• Marketing Cloud
• Advertising
• CRM
• Service
• Commerce
• Data warehouse
• External applications
• AI agents

Step 5: Evaluate Real-Time Requirements

Separate requirements into:

• Batch
• Near real-time
• Streaming
• Real-time

Not every use case requires real-time processing.

Step 6: Evaluate Existing Salesforce Investment

If Salesforce already represents a major part of the architecture, evaluate the benefits of native integration.

Step 7: Model Total Cost

Calculate:

License + Implementation + Integration + Data Engineering + Governance + Operations

Do not compare subscription prices alone.

Step 8: Run a Proof of Concept

Choose one high-value use case.

For example:

Unify CRM + service + purchase data and activate a high-value retention audience.

Measure:

• Data quality
• Match rate
• Profile completeness
• Activation speed
• Implementation effort
• Business impact

Then expand.

Salesforce Data 360 vs CDP: Recommended Architecture

For an enterprise already invested in Salesforce, a practical architecture can look like this:

Data Sources

• Salesforce CRM
• SAP
• NetSuite
• Dynamics 365
• E-commerce
• Website
• Mobile
• Service platforms
• Data warehouse

Data Integration

• APIs
• Connectors
• Middleware
• Batch pipelines
• Streaming
• Zero-copy integrations

Data 360

• Data Lake Objects
• Data Model Objects
• Customer 360 Data Model
• Data transformations
• Identity resolution
• Unified profiles
• Data graphs
• Calculated insights

Decision Layer

• Segmentation
• Personalization
• Analytics
• Automation
• AI

Activation

• Sales
• Service
• Marketing
• Commerce
• Advertising
• Agentforce
• External systems

This architecture makes Data 360 a shared customer data layer rather than another isolated marketing database.

Data 360 vs CDP: Migration Considerations

Organizations that already have a CDP do not necessarily need to replace it immediately.

A better approach may be to evaluate:

1. Existing CDP capabilities
2. Current integrations
3. Identity model
4. Customer profile model
5. Audience definitions
6. Activation destinations
7. Historical data
8. Data quality
9. Existing campaigns
10. Business dependencies

Then identify which capabilities need to migrate.

A phased approach may include:

Phase 1

Data discovery and architecture assessment.

Phase 2

Priority data sources.

Phase 3

Identity resolution.

Phase 4

Unified profiles.

Phase 5

Priority segments.

Phase 6

Activation.

Phase 7

Sales/service use cases.

Phase 8

AI and Agentforce use cases.

This reduces the risk of attempting a massive platform migration before the organization has proven business value.

How MoreYeahs Can Help With Salesforce Data 360

For enterprises evaluating Data 360, the difficult part is often not turning the platform on.

The difficult part is designing the surrounding architecture.

MoreYeahs' Salesforce services include Salesforce implementation, data migration and validation, custom object and workflow design, marketing automation, analytics, training and adoption, along with Salesforce support and managed services. MoreYeahs also states that it has built Salesforce integrations with SAP, NetSuite, Dynamics 365, and custom systems using real-time API, near-real-time middleware, and batch patterns.

That makes the implementation conversation broader than simply configuring a Salesforce product.

A Data 360 program can require coordination across:

• Salesforce architecture
• Data engineering
• CRM
• ERP
• Marketing
• Service
• Data quality
• Identity resolution
• Integration
• Analytics
• AI

A practical implementation approach should therefore start with the business use case, data landscape, architecture, and governance model before expanding into activation and AI.

MoreYeahs can support organizations across that Salesforce implementation lifecycle, from discovery and configuration through integration, training, and optimization.

Salesforce Data 360 vs CDP: Final Verdict

There is no universal winner.

A traditional CDP can be the right choice when the organization needs a focused customer data platform primarily for marketing, segmentation, personalization, and activation.

Salesforce Data 360 becomes more compelling when the organization needs a broader enterprise customer data foundation connected to Salesforce, real-time experiences, cross-cloud processes, analytics, automation, and AI.

The most important distinction is therefore not:

Data 360 vs CDP

It is:

Focused customer data platform vs broader enterprise data and activation foundation.

For a Salesforce-centric enterprise, Data 360 can provide a strong architectural foundation because customer data can be unified and used across marketing, sales, service, commerce, and AI.

For an organization with a narrow marketing requirement or limited Salesforce footprint, a standalone CDP may still be the more appropriate option.

The right decision should come from the data architecture and business outcomes, not the product label.

Salesforce Data 360 vs CDP: Enterprise Evaluation Checklist

Before selecting a platform, answer these questions:

Business

• What business outcomes are we targeting?
• Which teams will use the platform?
• Which customer journeys matter most?

Data

• Where does customer data currently live?
• How many systems contain customer identities?
• How much duplicate data exists?
• What data needs to be unified?

Architecture

• Is Salesforce the strategic CRM?
• Do we use Snowflake, Databricks, BigQuery, Redshift, or another data platform?
• Do we need batch, streaming, or real-time processing?
• Can zero-copy architecture reduce unnecessary data movement?

Identity

• What identifiers are available?
• What matching rules are required?
• How should anonymous identities be handled?

Activation

• Which marketing platforms need audiences?
• Do sales and service need unified customer context?
• Which external systems need customer data?

AI

• Is Agentforce part of the roadmap?
• What customer context will AI agents need?
• What data must be governed before AI can use it?

Financial

• What is the expected license cost?
• What is the implementation cost?
• What integration work is required?
• What will ongoing operations cost?

If these questions are answered before platform selection, the organization is far more likely to choose an architecture that can scale.

Related Salesforce Data 360 Resources

Final Takeaway

A CDP answers an important question:

How do we unify customer data and make it actionable?

Salesforce Data 360 takes that question further:

How do we create a trusted data foundation that can power customer experiences, business processes, analytics, automation and AI across the enterprise?

That distinction is the real reason organizations should evaluate Data 360 against the broader CDP market.

For Salesforce-centric enterprises, the decision should not be based on the label "CDP." It should be based on how effectively the platform can connect the organization's data, business processes, customer experiences and AI strategy into one scalable architecture.