Salesforce Data 360 Segmentation: Audience Building, Rules, Activation & Best Practices

What Is Salesforce Data 360 Segmentation?

Salesforce Data 360 segmentation is the process of using unified customer and business data to define specific audiences based on attributes, behaviors, relationships, calculated insights, and other business rules.

Instead of creating audiences from isolated CRM, website, commerce, marketing, or service datasets, Data 360 allows organizations to build segments from the unified data model.

Salesforce describes segmentation as a way to break data into useful groups for understanding, targeting, and analyzing customers. Segments can then be published to activation targets on a defined schedule or when needed.

For an enterprise, the important distinction is this:

Data unification tells you who the customer is. Segmentation determines which customers should receive a specific experience. Activation determines where that audience should be used.

A typical architecture looks like this:

Source Systems → Data 360 → Data Model → Identity Resolution → Unified Profiles → Segmentation → Activation → Engagement

This makes segmentation a critical bridge between customer data and business action.

Why Data 360 Segmentation Matters for Enterprise Organizations

Enterprise customer data rarely exists in one system.

A customer may have:

• A Salesforce CRM record
• Multiple transactions in an ERP
• Website activity
• Mobile application events
• Customer service cases
• Marketing engagement history
• Loyalty activity
• Product usage information
• Subscription data
• Store purchases
• Support interactions

Without a unified data foundation, marketers and business teams often build audiences independently in different systems.

That creates problems such as:

• Duplicate audiences
• Inconsistent customer definitions
• Outdated targeting data
• Conflicting segmentation rules
• Poor personalization
• Manual audience exports
• Difficult campaign governance
• Weak measurement
• Data privacy concerns

Data 360 segmentation addresses this by allowing organizations to build audiences against mapped data model objects and related attributes.

Salesforce's current documentation also supports standard and dynamic segmentation approaches, calculated insights, segment filtering, and publishing segments to activation targets.

How Salesforce Data 360 Segmentation Works

At a high level, segmentation follows this process:

1. Bring data into Data 360

Customer, transaction, engagement, product, service, and other data enters Data 360 through supported ingestion and integration patterns.

2. Map data to the data model

Source data is represented through Data Lake Objects and mapped to Data Model Objects.

Only appropriately mapped fields and related objects can be used for segmentation and activation.

3. Unify customer identities

Identity resolution can combine records belonging to the same individual or account.

This produces unified profiles that can be used as the foundation for audience creation.

4. Select the segmentation object

You choose the Data Model Object on which the segment is based.

Examples can include:

• Unified Individual
• Individual
• Account
• Household
• Sales Order
• Reservation
• Product
• Other supported profile or engagement objects

Salesforce documentation specifically describes segment creation using a selected DMO and provides examples including Unified Individual, Unified Household, Account, Reservations, and Sales Order.

5. Define audience rules

You then apply filters based on:

• Demographics
• Geography
• Customer status
• Purchase history
• Product ownership
• Engagement
• Service history
• Account attributes
• Calculated metrics
• Behavioral signals
• Consent and contactability

6. Preview and validate the audience

Before activation, teams can review the resulting audience and identify unexpected inclusions or exclusions.

7. Publish the segment

The segment can be published on a schedule or through supported publishing mechanisms.

8. Activate the audience

The segment can then be sent to supported activation targets such as Marketing Cloud and external advertising platforms.

Salesforce Data 360 Segmentation Architecture

A practical enterprise segmentation architecture can be represented as:

CRM

ERP

E-Commerce

Website

Mobile App

Service

Marketing

Loyalty → Data 360 Ingestion → Data Lake Objects → Data Mapping → Data Model Objects → Identity Resolution → Unified Individual / Account → Calculated Insights → Segment Rules → Audience Validation → Segment Publishing → Activation Targets → Marketing / Advertising / Commerce / Service

The quality of segmentation depends heavily on everything that comes before the segment builder.

A sophisticated segment cannot compensate for poor identity resolution, incomplete mappings, inconsistent data, or unreliable source data.

Salesforce Data 360 Segment Components

A segmentation implementation normally involves several important components.

1. Data Model Objects

DMOs provide the structured business representation used by segmentation.

For example:

• Unified Individual
• Individual
• Account
• Product
• Sales Order
• Engagement
• Household

The segment operates against the available data model rather than directly querying every underlying source system.

2. Attribute Library

The attribute library exposes attributes that can be used when defining segment rules.

These may include:

• Customer country
• Customer type
• Purchase date
• Product category
• Lifetime value
• Account industry
• Engagement status
• Email engagement
• Service history

Salesforce's segmentation interface allows users to narrow audiences using direct and related attributes.

3. Segment Canvas

The Segment Canvas provides the visual environment for constructing segmentation logic.

You can define:

• Included audiences
• Excluded audiences
• Attribute filters
• Related attributes
• Containers
• Behavioral conditions
• Aggregations

Salesforce currently documents separate Include and Exclude areas for segment rules, with up to 50 filters per area.

4. Calculated Insights

Calculated Insights can provide derived metrics that are more useful than raw customer attributes.

For example:

• Total lifetime revenue
• Average order value
• Number of purchases
• Revenue in the last 12 months
• Number of service cases
• Engagement frequency
• Product utilization

This allows segmentation to move beyond simple demographic filters.

Instead of:

Customers in India

You can create:

Customers in India with more than three purchases in the last 180 days and lifetime value above a defined threshold.

That is much more useful for personalization and revenue operations.

Common Salesforce Data 360 Segmentation Strategies

There is no single segmentation model that works for every organization.

Most mature enterprises use several segmentation dimensions together.

1. Demographic Segmentation

Examples:

• Age range
• Gender
• Geography
• Language
• Customer type
• Household attributes

Useful for broad audience planning.

2. Firmographic Segmentation

Especially valuable for B2B organizations.

Examples:

• Industry
• Company size
• Revenue
• Geography
• Account tier
• Employee count
• Business model

A B2B organization could create:

Enterprise manufacturing accounts in North America with more than 1,000 employees.

3. Behavioral Segmentation

Behavior often produces more actionable audiences than static profile data.

Examples:

• Recently purchased
• Browsed product category
• Abandoned cart
• Opened multiple campaigns
• Contacted support
• Used a product feature
• Visited pricing pages
• Attended an event

Behavioral segmentation is especially valuable for real-time or event-driven customer experiences.

4. Transactional Segmentation

Transactional data can identify customers based on actual commercial activity.

Examples:

• First-time purchasers
• Repeat customers
• High-value customers
• Customers who have not purchased recently
• Customers with increasing order frequency
• Customers with declining spend

For example:

Customers who purchased more than $1,000 in the last 90 days but have not purchased in the last 30 days.

This can become a retention or reactivation audience.

5. Lifecycle Segmentation

Customer lifecycle stages can be represented through segments such as:

• Prospect
• New customer
• Active customer
• High-value customer
• At-risk customer
• Dormant customer
• Churned customer

Lifecycle segmentation is particularly useful when connected to journey orchestration.

6. Engagement Segmentation

Marketing and digital engagement can be used to identify:

• Highly engaged customers
• Low-engagement customers
• Email clickers
• Website visitors
• App users
• Campaign responders
• Inactive contacts

These segments can then drive differentiated communication strategies.

7. Service-Based Segmentation

Customer service data creates another important dimension.

Examples:

• Customers with unresolved cases
• Customers with multiple cases
• Customers with recent complaints
• Customers using premium support
• Customers approaching renewal
• Customers with service-level risks

This can connect marketing, sales, and service strategies.

Salesforce Data 360 Segmentation Examples

Here are practical enterprise examples.

Example 1: High-Value Customers

Audience:

Customers with:

• Lifetime value above threshold
• At least three purchases
• Active customer status
• Marketing consent

Use cases:

• VIP campaigns
• Loyalty programs
• Premium offers
• Early product access

Example 2: At-Risk Customers

Audience:

Customers who:

• Previously purchased
• Have not purchased recently
• Show declining engagement
• Have an active or recent service interaction

Use cases:

• Retention campaigns
• Customer success outreach
• Personalized offers
• Service escalation

Example 3: Product Cross-Sell Audience

Audience:

Customers who own Product A but not Product B.

Use cases:

• Cross-sell campaigns
• Product education
• Sales outreach
• Personalized recommendations

Example 4: B2B Expansion Audience

Audience:

Accounts with:

• Existing product adoption
• Increasing usage
• High employee count
• Strong engagement
• No current contract for a complementary product

Use cases:

• Account expansion
• Upselling
• Sales prioritization
• Account-based marketing

Example 5: Abandoned Cart Audience

Audience:

Customers who:

• Added a product to cart
• Did not complete the purchase
• Remain contactable
• Have recent website activity

Use cases:

• Email reminders
• SMS
• Personalized recommendations
• Retargeting

Standard vs Dynamic Segmentation in Data 360

Salesforce currently supports multiple segmentation approaches.

Standard Segments

Standard segments are built against a Data Model Object and use defined segmentation criteria.

Salesforce documents configurable lookback windows for standard segments. The default is currently 90 days, with supported configurations extending further depending on organizational limits.

Standard segmentation is useful when the organization needs repeatable, governed audiences.

Examples:

• High-value customers
• Active customers
• Product owners
• Regional audiences
• Customer lifecycle segments

Dynamic Segments

Dynamic segments can evaluate criteria dynamically at execution time without relying on the same persisted segment membership model.

This can be useful when organizations need more flexible or frequently changing audience definitions.

The right choice depends on:

• Data volume
• Refresh requirements
• Activation requirements
• Governance
• Use case
• Performance
• Frequency of change

Lookback Windows in Data 360 Segmentation

Lookback windows are critical for behavioral segmentation.

For example:

Customers who purchased during the last 30 days.

The system needs to understand the relevant historical data period.

Different use cases may require:

7-day lookback

Useful for:

• Recent website behavior
• Short-term campaigns
• Flash promotions

30-day lookback

Useful for:

• Recent engagement
• Reactivation
• Subscription behavior

90-day lookback

Useful for:

• Customer activity
• Purchase behavior
• Engagement scoring

12-month lookback

Useful for:

• Annual purchasing behavior
• Customer value
• Renewal analysis

Salesforce allows segment-level lookback configuration, with specific limits depending on the organization and segmentation setup.

Include and Exclude Logic

Good segmentation is not only about defining who belongs in an audience.

It is also about explicitly defining who should not receive the experience.

For example:

Include

Customers who:

• Purchased Product A
• Live in the United States
• Have engaged within 90 days

Exclude

Customers who:

• Already purchased Product B
• Opted out of marketing
• Have an unresolved service escalation

This produces a much safer campaign audience.

Salesforce supports inclusion and exclusion rules within the segmentation interface.

Using Identity Resolution for Better Segmentation

Segmentation becomes substantially more valuable when identity resolution has already unified customer records.

Consider a customer who appears as:

John Smith

[email protected]

J. Smith

[email protected]

John A Smith

mobile: +1...

These records may originate from:

• CRM
• E-commerce
• Mobile
• Loyalty
• Service

If they represent the same person, segmentation should ideally evaluate the customer as one unified identity rather than several disconnected records.

That is where Data 360's unified profile architecture becomes important.

The segmentation layer should therefore be treated as a downstream consumer of the organization's identity and data-quality strategy.

Data 360 Segmentation and Customer 360

The relationship can be summarized as:

Data Sources → Data 360 → Data Model → Identity Resolution → Unified Customer → Segmentation → Activation → Personalized Experience

Customer 360 gives the organization a more complete representation of the customer.

Segmentation turns that representation into a usable audience.

Data 360 Segmentation and Marketing Cloud

One of the most important use cases is activating Data 360 audiences into Salesforce marketing environments.

Salesforce documents activation targets for Marketing Cloud Engagement and Marketing Cloud Personalization, among other destinations.

For example:

Data 360 → Unified Individual → High-Value Customer Segment → Marketing Cloud Engagement → VIP Customer Journey

This can support:

• Email
• SMS
• Personalized journeys
• Retention campaigns
• Cross-sell campaigns
• Loyalty campaigns

The major advantage is that the audience definition is connected to the enterprise data foundation instead of being rebuilt independently inside every campaign.

Data 360 Segmentation for Advertising

Data 360 can also support audience activation to advertising platforms.

Salesforce currently documents partner-platform activation for destinations including:

• Amazon Ads
• Google Ads
• Google DV360
• Google Ads Manager
• LinkedIn Campaign Manager
• Meta Ads Manager
• Pinterest
• Snapchat

Availability depends on the relevant Data 360 capabilities and licensing.

This enables organizations to create audiences using first-party data and activate them across advertising environments.

For example:

Unified Customers → High-Value Segment → Consent / Contactability → Audience Activation → Advertising Platform

For B2B organizations, Salesforce also documents company-level audience activation through LinkedIn Campaign Manager.

Data 360 Segmentation and Consent

Segmentation should never be treated as purely a marketing exercise.

Consent and contactability need to be incorporated into the activation design.

For example:

A customer may qualify for:

High-value customer + recent purchase

But that does not automatically mean every channel should be available.

The activation layer needs to consider:

• Email consent
• SMS consent
• Phone preferences
• WhatsApp eligibility
• Regional requirements
• Contact preferences
• Source priority
• Suppression requirements

Salesforce's activation model includes contact points and allows organizations to configure source priority and filters.

Data 360 Activation vs Segmentation

These concepts are often confused.

Segmentation answers:

Who should be included?

Activation answers:

Where should that audience go?

Engagement answers:

What should happen next?

For example:

Segmentation

Customers who purchased Product A in the last 30 days.

Activation

Publish the audience to Marketing Cloud Engagement.

Engagement

Start a post-purchase education journey.

This distinction makes the overall architecture easier to govern.

Data 360 Segmentation Governance

Large organizations should not allow every team to create uncontrolled audiences.

A segmentation governance model should define:

Naming standards

Example:

Lifecycle_Active_HighValue_90D

Ownership

Each production segment should have an accountable business owner.

Business purpose

Every important segment should answer:

Why does this audience exist?

Refresh frequency

Define whether the segment needs:

• Hourly
• Daily
• Weekly
• On-demand

refresh.

Data requirements

Document:

• Source systems
• DMOs
• Attributes
• Calculated insights
• Identity rules

Suppression rules

Document exclusions for:

• Consent
• Existing customers
• Service cases
• Regulatory restrictions
• Campaign conflicts

Lifecycle

Archive unused audiences rather than allowing the segment library to become an uncontrolled collection of outdated rules.

Segment Naming Convention

A scalable enterprise naming convention might look like:

[Business Function]_[Audience]_[Criteria]_[Time Window]_[Version]

For example:

Marketing_HighValueCustomers_LTV_90D_V1

Sales_ExpansionAccounts_Enterprise_V1

Retention_AtRiskCustomers_NoPurchase_60D_V2

Product_CrossSell_ProductA_NoProductB_V1

This makes segments easier to discover and manage.

Salesforce Data 360 Segmentation Best Practices

1. Start with business outcomes

Do not start by asking:

What filters can we build?

Start with:

What business decision will this audience support?

2. Build on unified data

Whenever possible, use unified identities and standardized data rather than rebuilding customer logic from raw source systems.

3. Keep segment logic understandable

A segment should be understandable by another person on the team.

Avoid creating unnecessarily complex rule chains when a simpler business definition can achieve the same result.

4. Use calculated insights for meaningful metrics

If the business cares about:

• Lifetime value
• Purchase frequency
• Revenue
• Engagement
• Service volume

consider whether a calculated metric is more appropriate than a collection of raw fields.

5. Define exclusions deliberately

Do not rely solely on inclusion rules.

Explicitly define who should not be targeted.

6. Separate audience definition from activation

A segment should define the audience.

The activation should determine where and how the audience is delivered.

This makes the architecture reusable.

7. Standardize contactability

Build clear rules for:

• Email
• Phone
• SMS
• WhatsApp
• Other supported channels

Salesforce activation templates can help standardize reusable activation configurations, including contact points, source priority, filters, and enrichment attributes.

8. Validate before publishing

Preview the audience.

Look for:

• Unexpected audience growth
• Missing records
• Duplicate identities
• Incorrect exclusions
• Invalid contact points
• Unexpected geographic distribution

Salesforce provides segment preview functionality for validating the audience before publication.

9. Monitor segment performance

Track:

• Audience size
• Match rate
• Activation success
• Delivery rate
• Engagement
• Conversion
• Revenue
• Unsubscribe rate

A segment should not be considered successful simply because it published successfully.

10. Treat segmentation as a reusable enterprise capability

Avoid rebuilding similar audiences for every campaign.

Create reusable audience definitions such as:

• All customers
• All prospects
• Active customers
• High-value customers
• At-risk customers
• Consentable customers
• Enterprise accounts

Then build campaign-specific segments from those foundations.

Salesforce also documents reusable activation templates to standardize activation configurations.

Common Salesforce Data 360 Segmentation Challenges

Challenge 1: Poor Data Quality

If customer data is incomplete or inconsistent, segment results will be unreliable.

Solution: Establish data-quality rules before building critical audiences.

Challenge 2: Incorrect Identity Resolution

If multiple records belonging to the same customer remain separate, the segment may underestimate customer value or engagement.

Solution: Review identity resolution and reconciliation rules.

Challenge 3: Missing Data Model Relationships

A segment may not be able to use the desired attribute if the relevant relationship has not been modeled correctly.

Solution: Review DLO-to-DMO mappings and relationships.

Salesforce notes that mapped fields and related objects are important for segmentation and activation.

Challenge 4: Overly Complex Rules

Complex segment logic can become difficult to maintain.

Solution: Break large audiences into reusable building blocks where appropriate.

Challenge 5: Stale Audiences

A segment that was useful six months ago may no longer reflect the business strategy.

Solution: Establish segment ownership and review cycles.

Challenge 6: Wrong Lookback Window

A 12-month window may be inappropriate for a campaign that needs recent behavior.

Solution: Match the lookback period to the actual business question.

Challenge 7: Activation Mismatch

A segment can be logically correct but still fail to deliver the expected business result if the activation configuration is wrong.

Solution: Validate:

• Activation target
• Membership
• Contact points
• Attributes
• Consent
• Source priority
• Schedule

Salesforce's activation process explicitly includes activation membership, contact points, attributes, target selection, and publishing schedule.

Salesforce Data 360 Segmentation Implementation Roadmap

A practical enterprise rollout can follow six phases.

Phase 1: Business Use Cases

Identify the highest-value audience use cases.

Examples:

• Retention
• Cross-sell
• Upsell
• Acquisition
• Loyalty
• Account expansion
• Personalization

Phase 2: Data Assessment

Review:

• CRM data
• Transaction data
• Web data
• Marketing data
• Service data
• Consent data
• Product data

Identify gaps before designing the segments.

Phase 3: Data Model Preparation

Validate:

• DLOs
• DMOs
• Relationships
• Identity resolution
• Calculated insights
• Data quality

Phase 4: Segment Design

Define:

• Audience
• Inclusion rules
• Exclusion rules
• Lookback windows
• Calculated metrics
• Refresh frequency

Phase 5: Activation

Configure:

• Activation targets
• Activation membership
• Contact points
• Attributes
• Source priority
• Destination
• Schedule

Phase 6: Measurement and Optimization

Monitor:

• Audience quality
• Activation performance
• Campaign engagement
• Conversion
• Revenue
• Customer retention

Then refine the audience rules.

Salesforce Data 360 Segmentation KPIs

The most useful metrics depend on the use case.

Data Quality

• Identity match rate
• Attribute completeness
• Duplicate rate
• Data freshness

Audience Quality

• Segment size
• Qualification rate
• Exclusion rate
• Contactable percentage

Activation

• Activation success rate
• Match rate
• Delivery rate
• Refresh success

Marketing

• Open rate
• Click-through rate
• Conversion rate
• Revenue per audience member

Sales

• Qualified opportunities
• Pipeline generated
• Expansion revenue
• Conversion rate

Customer Success

• Retention rate
• Churn rate
• Product adoption
• Customer health

How AI Changes Data 360 Segmentation

AI is increasingly becoming part of audience creation.

Salesforce currently documents Einstein Segment Creation and a Data 360 Marketing Agent that can create segment logic from natural-language descriptions.

Instead of manually starting with:

Select Unified Individual → add attribute → select operator → define value

a marketer could describe:

Create an audience of high-value customers who purchased in the last 90 days but have not engaged with our latest campaign.

The system can then suggest segmentation criteria.

This does not eliminate governance.

It makes governance more important.

Organizations still need to validate:

• Data availability
• Attribute meaning
• Inclusion logic
• Exclusions
• Consent
• Business intent
• Activation destination

AI can accelerate audience creation, but the organization remains responsible for the business logic.

Data 360 Segmentation for Agentforce

Segmentation can also become useful for AI-powered customer experiences.

Consider an enterprise with a segment:

High-value customers with declining engagement.

That audience could become an input to:

• Sales prioritization
• Customer success workflows
• Personalized journeys
• Service experiences
• AI-assisted recommendations

This creates a broader architecture:

Unified Customer Data → Customer Segment → Business Context → AI / Agentforce → Recommended Action → Human or Automated Execution

The important point is that AI experiences become more useful when the underlying customer context is trusted.

Data 360 Segmentation for B2B

B2B segmentation requires a different approach from consumer marketing.

Instead of focusing only on individuals, organizations may need to segment around:

• Accounts
• Contacts
• Buying groups
• Industries
• Account tiers
• Revenue
• Product adoption
• Contract status
• Pipeline
• Engagement
• Renewal dates

For example:

Enterprise accounts in manufacturing with active Salesforce usage, high product adoption, and renewal within 120 days.

That audience can support:

• Account-based marketing
• Renewal campaigns
• Expansion opportunities
• Executive outreach
• Customer success programs

Salesforce also supports company-level audience activation for B2B advertising use cases through LinkedIn Campaign Manager.

Data 360 Segmentation vs Traditional CRM Segmentation

The biggest difference is not simply the number of filters available.

It is the ability to build audiences from a broader, unified customer data foundation.

Salesforce Data 360 Segmentation Checklist

Before putting a production segment into use, verify:

Data

• Required source systems are connected
• Data quality has been assessed
• Required DLOs exist
• Required DMOs are mapped
• Relationships are configured

Identity

• Identity resolution rules have been reviewed
• Unified profiles are available where required
• Duplicate identities have been investigated

Segment

• Business purpose is documented
• Correct DMO is selected
• Lookback window is appropriate
• Inclusion rules are defined
• Exclusion rules are defined
• Calculated insights are validated
• Segment preview has been reviewed

Activation

• Correct activation target is selected
• Activation membership is correct
• Contact points are configured
• Consent rules are applied
• Required attributes are included
• Source priority is defined
• Publishing schedule is appropriate

Governance

• Segment owner is assigned
• Naming convention is followed
• Business documentation exists
• Review date is defined
• Performance metrics are tracked

How MoreYeahs Can Help With Salesforce Data 360 Segmentation

Data 360 segmentation works best when it is treated as part of a broader data and CRM architecture rather than as an isolated marketing configuration.

MoreYeahs approaches Salesforce engagements through a five-stage process:

Discovery → Configuration → Integration → Training → Optimisation

Its Salesforce services include CRM implementation, data migration and validation, custom object and workflow design, marketing automation, analytics, and ongoing Salesforce support. 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.

For a Data 360 segmentation initiative, this type of engagement can involve:

1. Use-case discovery
   • Identify high-value audience requirements.
   • Define business outcomes.
   • Prioritize segmentation use cases.

1. Data architecture
   • Review source systems.
   • Map required data.
   • Assess DLO and DMO requirements.
   • Identify data-quality gaps.

1. Customer unification
   • Review identity-resolution requirements.
   • Validate unified profiles.
   • Establish customer-level data foundations.

1. Segment design
   • Build reusable audience definitions.
   • Configure inclusion and exclusion logic.
   • Establish lookback windows.
   • Incorporate calculated insights.

1. Activation
   • Configure activation targets.
   • Define contact points.
   • Connect segments with downstream marketing, sales, advertising, or operational systems.

1. Optimization
   • Monitor audience quality.
   • Review activation performance.
   • Improve segmentation rules.
   • Expand use cases as the data foundation matures.

MoreYeahs' Salesforce case-study portfolio currently includes 20 Salesforce Implementation engagements, including Salesforce implementations for a pet sales marketplace, nonprofit transformation, and a multi-sector real estate and infrastructure enterprise.

The broader objective should not simply be to create more audiences.

It should be to create trusted, reusable audiences that can consistently drive better customer and business outcomes.

Final Takeaway

Salesforce Data 360 segmentation is where a unified customer data strategy becomes actionable.

The progression is straightforward:

Connect the data.

Model the data.

Unify the identities.

Define the audience.

Activate the audience.

Measure the outcome.

The strongest implementations do not treat segmentation as a collection of campaign filters.

They build segmentation as a governed enterprise capability that connects customer data to marketing, sales, service, commerce, advertising, and AI experiences.

For organizations already investing in Salesforce, the opportunity is to move from:

“We have customer data.”

to:

“We know exactly which customers matter for this business outcome, why they qualify, and where we should act on that insight.”

That is the real value of Salesforce Data 360 segmentation.