Data Model Objects in Customer 360 Data Model - Salesforce Data Cloud

What is the primary purpose of the Customer 360 Data Model in Data Cloud?

The primary purpose of the Customer 360 Data Model in Data Cloud is to provide a standardized framework for organizing and integrating customer data from various disparate sources. Its key functions include:

1. Data Standardization: It offers a common structure to normalize data from different systems, databases, and platforms.
2. Interoperability: The model ensures that data from various sources can work together seamlessly within Data Cloud.
3. Unified Customer View: By standardizing data structures, it enables the creation of a comprehensive, 360-degree view of each customer.
4. Data Mapping: It provides standard objects that can be mapped to various data sources, ensuring consistency across the entire data ecosystem.
5. Relationship Mapping: The model defines how different data objects relate to each other, creating a cohesive data structure.
6. Data Activation: By organizing data in a standardized way, it facilitates easier data segmentation and activation for marketing and customer engagement purposes.
7. Cross-Platform Integration: It allows for seamless integration of data from various Salesforce platforms like Sales Cloud, Service Cloud, and Marketing Cloud Engagement.
8. Data Insight Generation: The standardized structure makes it easier to draw insights from combined data sources.

Example: Consider a company using separate systems for CRM, e-commerce, and email marketing. Each system might store customer email addresses differently:

• CRM: {first_name}.{last_name}@company.com
• E-commerce: [email protected]
• Email Marketing: [email protected]

The Customer 360 Data Model would provide a standard "Contact Point Email" object where all these email addresses can be mapped, linked to a single customer profile, and used consistently across all integrated systems and analyses.

How does the Customer 360 Data Model help in standardizing data from various sources?

The Customer 360 Data Model helps standardize data from various sources by providing a common framework and structure for organizing diverse types of information. Here's how it accomplishes this:

1. Standard Objects: The model offers predefined standard objects that represent common entities in customer data, such as Individual, Contact Point Email, Product Catalog, etc. These objects serve as templates for mapping data from different sources.
2. Attribute Definitions: For each standard object, the model defines specific attributes (or fields) that describe the characteristics of that object. This ensures that data from different sources is categorized consistently.
3. Relationship Mapping: The model establishes how different objects relate to each other, creating a cohesive structure for diverse data types.
4. Data Type Standardization: It ensures that similar data types (e.g., email addresses, phone numbers) are stored and formatted consistently across all integrated systems.
5. Primary and Foreign Key Mapping: The model uses primary and foreign keys to link related data across different objects and sources, maintaining data integrity and relationships.

Example: Let's say a company has customer data in three different systems:

• CRM System: Stores basic contact info (name, email, phone)
• E-commerce Platform: Stores purchase history
• Marketing Automation Tool: Stores email engagement data

The Customer 360 Data Model would standardize this data by:

1. Mapping contact info to the "Individual" and "Contact Point" objects
2. Linking purchase history to the "Sales Order" object
3. Connecting email engagement data to the "Email Engagement" object
4. Using a common identifier (like customer ID) to link all this information to a single customer profile

This standardization allows the company to have a unified view of the customer across all touch points and systems.

Can you explain the concept of "standard objects" in the context of the Customer 360 Data Model?

Standard objects in the Customer 360 Data Model are predefined data structures that represent common entities and concepts in customer data management. They serve as templates or blueprints for organizing and categorizing different types of customer-related information. Here's a detailed explanation:

1. Definition: Standard objects are pre-built, standardized data containers that represent specific types of data or entities commonly used in customer relationship management and data analysis.
2. Structure: Each standard object has a set of predefined attributes (or fields) that describe the characteristics of that object. These attributes are carefully chosen to cover the most common and essential pieces of information for that object type.
3. Flexibility: While standard objects have a predefined structure, they are designed to be flexible enough to accommodate various business needs and data sources.
4. Consistency: By using standard objects, organizations ensure that similar types of data are structured consistently across different systems and data sources.
5. Interoperability: Standard objects facilitate easier integration and data sharing between different systems and platforms that adhere to the Customer 360 Data Model.
6. Extensibility: In many cases, standard objects can be extended with custom attributes to meet specific business requirements while maintaining the core standardized structure.

Examples of standard objects in the Customer 360 Data Model include:

• Individual: Represents a person, with attributes like first name, last name, birth date, and data preferences.
• Contact Point Email: Represents an email address associated with an individual, including attributes like the email address itself, deliverability status, and purpose.
• Product Catalog: Represents products or services, with attributes such as product name, description, SKU, and price.
• Sales Order: Represents a purchase or order, with attributes like order date, total amount, and status.

What are some common sources of data that can be integrated using the Customer 360 Data Model?

The Customer 360 Data Model is designed to integrate data from a wide variety of sources, encompassing nearly all touch points where a business interacts with or collects information about its customers. Here are some common sources of data that can be integrated:

1. Traditional Software: This includes various business applications like Enterprise Resource Planning (ERP) systems, which contain valuable customer and transaction data.
2. External Databases: Third-party data providers or partner databases that offer additional customer insights or demographic information.
3. Customer Relationship Management (CRM) Systems: Platforms like Salesforce Sales Cloud, which store detailed customer information, interaction history, and sales data.
4. E-commerce Platforms: Online shopping systems that capture purchase history, browsing behavior, and product preferences.
5. Data Lakes: Large repositories of raw data stored in its native format, often containing a wealth of unstructured or semi-structured customer data.
6. Marketing and Email Databases: Systems like Marketing Cloud Engagement, which store customer contact information, campaign data, and engagement metrics.
7. Customer Service Platforms: Help desk or support ticket systems (like Service Cloud) that capture customer issues, interactions, and resolutions.
8. Digital Engagement Data: This includes data from websites (e.g., Google Analytics) and mobile apps, capturing user behavior, preferences, and interactions in digital environments.
9. Analytics Platforms: Systems that process and analyze customer data, often providing derived insights and metrics.
10. Social Media Platforms: Data from social media interactions, mentions, and customer profiles.
11. Internet of Things (IoT) Devices: Data from connected devices that may provide insights into product usage or customer behavior.
12. Loyalty Programs: Systems that track customer rewards, points, and program engagement.

Example: A retail company might integrate data from the following sources using the Customer 360 Data Model:

• Point of Sale (POS) system: For in-store purchase data
• E-commerce platform: For online shopping behavior and purchases
• CRM system: For customer service interactions and sales pipeline
• Email marketing platform: For email engagement metrics
• Mobile app: For app usage data and in-app purchases
• Loyalty program database: For points accrual and redemption data

By mapping all this data to the appropriate standard objects in the Customer 360 Data Model (e.g., Individual, Sales Order, Email Engagement), the company can create a unified view of each customer across all these touchpoints, enabling more personalized marketing and improved customer service.

How does Data Cloud handle the different primary key values used across various systems?

Data Cloud addresses the challenge of different primary key values across various systems through a process of data harmonization and identity resolution. Here's a detailed explanation of how it handles this:

1. Party Identification Object: Data Cloud uses a special Data Model Object (DMO) called "Party Identification" to manage different identifiers for the same individual or organization across various systems.
2. Multiple Identifier Support: The Party Identification object can store multiple types of identifiers for each entity, such as: Customer IDs from different systems Email addresses Phone numbers Loyalty program numbers CRM Contact ID values (e.g., Salesforce IDs like 0033000000D8cuIQAA) Social media handles Device IDs
3. Linking Identifiers: Data Cloud creates relationships between these different identifiers, effectively linking them to represent a single entity (person or organization).
4. Identity Resolution: Using sophisticated matching algorithms, Data Cloud can determine when different identifiers likely refer to the same entity, even if they come from different systems.
5. Unified Customer Profile: By resolving these identities, Data Cloud creates a unified customer profile that brings together data from all connected systems, regardless of the original primary key used.
6. Data Mapping: When ingesting data from various sources, Data Cloud maps the source system's primary keys to the appropriate Party Identification attributes.
7. Cross-System Querying: This approach allows for cross-system querying and analysis without needing to manually join data based on different primary keys.

Example: Let's say a customer, John Doe, exists in multiple systems:

• CRM System: Customer ID = CRM12345
• E-commerce Platform: User ID = USER98765
• Marketing Cloud: Subscriber Key = [email protected]
• Loyalty Program: Member Number = LP5678

Data Cloud would handle this by:

1. Creating a Party Identification record for John Doe
2. Storing all these identifiers in the Party Identification object
3. Linking these identifiers to John's unified customer profile
4. Allowing queries and segmentation based on any of these identifiers

This way, when you query for John Doe's data, you can use any of these identifiers, and Data Cloud will return the comprehensive, unified profile, including data from all connected systems.

What is a Data Model Object (DMO) in the Customer 360 Data Model, and how does it differ from a specific instance of data?

A Data Model Object (DMO) in the Customer 360 Data Model is a standardized, abstract representation of a type of data or entity within the data model. It serves as a template or blueprint for organizing and categorizing specific types of information. Here's a detailed explanation of DMOs and how they differ from specific instances of data:

Characteristics of a Data Model Object (DMO):

1. Abstract Representation: A DMO is a conceptual structure that defines what kind of data can exist in a particular category and how it should be organized.
2. Attribute Definitions: Each DMO includes a set of predefined attributes (or fields) that describe the characteristics of that type of data.
3. Relationships: DMOs define how different types of data relate to each other within the overall data model.
4. Standardization: DMOs provide a consistent structure for organizing similar types of data across different systems and sources.
5. Reusability: The same DMO can be used to represent multiple instances of similar data.

How DMOs Differ from Specific Instances:

Abstraction vs. Concrete Data:

DMO: An abstract template or structure

Instance: Actual data that fits into this structure

Generalization vs. Specificity:

DMO: Represents a general category of data

Instance: Represents a specific entity or record

Definition vs. Values:

DMO: Defines what attributes exist and their data types

Instance: Contains actual values for these attributes

One-to-Many Relationship: One DMO can represent many instances of data

Static vs. Dynamic:

DMO: Relatively static, changing only when the data model is updated

Instance: Dynamic, changing as data is created, updated, or deleted

Example: Let's consider the "Contact Point Email" DMO:

DMO (Contact Point Email):

Attributes: Email Address (string) Is Primary (boolean) Status (picklist: Active, Inactive) Purpose (picklist: Personal, Work) Last Updated (date)

Instances (specific email records):

1. { Email Address: "[email protected]", Is Primary: true, Status: "Active", Purpose: "Work", Last Updated: "2023-09-15" }
2. { Email Address: "[email protected]", Is Primary: false, Status: "Active", Purpose: "Personal", Last Updated: "2023-08-20" }

In this example, the Contact Point Email DMO defines the structure and attributes for email data, while the instances represent specific email addresses with their corresponding details, fitting into the structure defined by the DMO.

Can you describe the relationship between subject areas, data model objects, and attributes in the Customer 360 Data Model?

The relationship between subject areas, data model objects (DMOs), and attributes in the Customer 360 Data Model forms a hierarchical structure that organizes and categorizes customer data. Let's break down each component and explain their relationships:

1. Subject Areas: Definition: Subject areas are the highest level of organization in the Customer 360 Data Model. They represent broad categories or domains of related data. Purpose: They group together related data model objects to support specific business goals or functional areas. Examples: Party (for customer identifiers), Engagement (for customer interactions), Sales (for order and revenue data), Product (for product information).
2. Data Model Objects (DMOs): Definition: DMOs are standardized representations of specific types of data or entities within a subject area. Purpose: They provide a template for organizing and structuring particular types of data. Relationship to Subject Areas: Multiple DMOs are grouped under each subject area, representing different aspects or entities within that domain.
3. Attributes: Definition: Attributes are individual pieces of information that describe characteristics of a data model object. Purpose: They define the specific data points that can be stored for each instance of a DMO. Relationship to DMOs: Each DMO contains multiple attributes that collectively describe the properties of that object type.

Hierarchical Relationship: Subject Area > Data Model Object > Attribute

Example to Illustrate the Relationship:

In this example:

• "Party" and "Engagement" are subject areas, representing broad categories of customer data.
• "Individual," "Contact Point Email," "Email Engagement," etc., are data model objects, each representing a specific type of data within its subject area.
• Elements like "First Name," "Email Address," "Open Date," etc., are attributes that define the specific pieces of information stored for each DMO.

This hierarchical structure allows for:

1. Logical organization of data
2. Scalability in adding new data types
3. Flexibility in representing complex customer information
4. Consistency in data representation across different systems and sources

By organizing data in this way, the Customer 360 Data Model enables businesses to create a comprehensive and unified view of their customers, facilitating better analysis, segmentation, and personalization of customer interactions.

What is the significance of primary keys and foreign keys in the Customer 360 Data Model?

Primary Keys:

1. Definition: A primary key is a unique identifier for a record within a data set or data model object (DMO).
2. Uniqueness: In the Customer 360 Data Model, a primary key ensures that each record in a DMO is uniquely identifiable.
3. Data Integrity: Primary keys maintain data integrity by preventing duplicate records and ensuring each piece of data has a distinct identifier.
4. Record Retrieval: They enable quick and efficient retrieval of specific records within a DMO.
5. Relationship Foundation: Primary keys serve as the basis for establishing relationships between different DMOs.

Example of a Primary Key: In the "Individual" DMO, a unique customer ID (e.g., CUST001) serves as the primary key.

Foreign Keys:

1. Definition: A foreign key is a field in one DMO that refers to the primary key in another DMO, establishing a relationship between them.
2. Data Relationships: Foreign keys create connections between different DMOs, allowing for the creation of a comprehensive, interconnected data model.
3. Data Consistency: They ensure referential integrity, maintaining consistency across related data sets.
4. Data Navigation: Foreign keys enable navigation between related pieces of information across different DMOs.
5. Data Integration: They facilitate the integration of data from various sources by providing a standardized way to link related information.

Example of a Foreign Key: In the "Contact Point Email" DMO, the customer ID (CUST001) serves as a foreign key, linking back to the "Individual" DMO.

Significance in the Customer 360 Data Model:

1. Unified Customer View: By using primary and foreign keys, the model can link various pieces of customer data (e.g., personal info, contact details, purchase history) to create a comprehensive customer profile.
2. Data Deduplication: Primary keys help identify and eliminate duplicate records across different data sources.
3. Efficient Querying: The key structure allows for efficient joining of data from multiple DMOs in complex queries.
4. Scalability: As new data sources are added, they can be easily integrated into the existing model using the established key structure.
5. Data Governance: The key structure supports better data governance by clearly defining data ownership and relationships.

Example Scenario: Consider a customer (John Doe) with the following data spread across different systems:

• CRM System (Individual DMO): Primary Key: CUST001 Name: John Doe
• Email Marketing System (Contact Point Email DMO): Foreign Key (Customer ID): CUST001 Email: [email protected]
• E-commerce Platform (Sales Order DMO): Foreign Key (Customer ID): CUST001 Order ID: ORD123 Total: $150

Using the primary key (CUST001) and foreign keys, the Customer 360 Data Model can link all this information, providing a unified view of John Doe across all systems and interactions.

Name and describe at least three common Data Model Objects used in the Customer 360 Data Model.

The Customer 360 Data Model includes several common Data Model Objects (DMOs) that represent key aspects of customer data. Here are three important ones:

1. The Individual DMO represents a person, typically a customer or prospect. Its key attributes include First Name, Last Name, Birth Date, Gender, and Data and Privacy Preferences. This DMO is fundamental to creating a customer profile and serves as the core around which other customer data is organized. For example, it can be used to store basic demographic information about a customer named John Doe, born on March 15, 1985, who has opted in for email marketing.
2. The Contact Point Email DMO represents an email address associated with an individual. Its key attributes include Email Address, Is Primary (boolean), Deliverability Status, Purpose (e.g., personal, work), Time Zone, and Communication Preferences. This DMO is crucial for email communications and helps maintain accurate contact information across various systems. For instance, it can store details about John Doe's work email ([email protected]), which is his primary email, verified as deliverable, and set to receive marketing communications.
3. The Sales Order DMO represents a purchase or sales transaction. Key attributes of this DMO include Order ID, Order Date, Total Amount, Currency, Status (e.g., pending, completed, cancelled), and Shipping Information. This DMO is essential for tracking customer purchasing behavior, order history, and revenue generation. For example, it can record a recent online purchase by John Doe, with Order ID #12345, placed on September 20, 2023, for $150.00, and a status of "Shipped" to his home address.

These DMOs work together to create a comprehensive view of the customer:

• The Individual DMO provides the core customer information.
• The Contact Point Email DMO links communication channels to the individual.
• The Sales Order DMO connects transactional data to the customer profile.

By using these and other DMOs, businesses can create a 360-degree view of their customers, enabling personalized marketing, improved customer service, and data-driven decision-making.

Why are the Individual, Party Identification, Contact Point Email, Contact Point Phone, and Contact Point Address DMOs required in all data models for Data Cloud?

These five Data Model Objects (DMOs) are required in all data models for Data Cloud because they form the essential foundation for creating a comprehensive and unified customer profile. Let's explore why each is crucial:

1. The Individual DMO represents the core identity of a person in the system. It serves as a central reference point, connecting all other customer-related data. This DMO stores basic demographic details, such as a person’s name, birth date, and gender, making it universally relevant regardless of the industry. It also includes attributes for managing data privacy preferences, which is increasingly vital in today’s regulatory environment, especially with regulations like GDPR and CCPA.
2. The Party Identification DMO is responsible for storing multiple identifiers associated with a customer across different systems. This DMO enables identity resolution by linking data from disparate sources to a single customer profile. It also plays a key role in cross-system integration, allowing data from various platforms and databases to flow seamlessly into a unified customer profile, which is essential for maintaining consistency and accuracy across touchpoints.
3. The Contact Point Email DMO captures the primary digital contact method for many customers. Email is often the go-to channel for marketing communications, account verification, and password resets. This DMO helps manage email preferences and ensures compliance with regulations regarding email communications. It’s essential for executing effective email marketing campaigns and maintaining direct communication with customers in a digital-first world.
4. The Contact Point Phone DMO is another vital object for real-time, direct communication. It supports both phone calls and SMS marketing, often serving as a critical channel for instant engagement. Additionally, phone numbers are commonly used for two-factor authentication and delivering service-related notifications, adding an extra layer of security and immediacy to customer interactions.
5. Lastly, the Contact Point Address DMO captures the physical location of the customer, which is particularly important for businesses dealing with physical products or services. It is crucial for e-commerce transactions, as it enables accurate shipping and delivery processes. Additionally, this DMO supports location-based marketing initiatives and helps companies ensure compliance with legal and tax regulations, further emphasizing its importance across industries.

Collective Importance:

1. Omnichannel View: Together, these DMOs provide a complete view of how to reach and interact with a customer across various channels.
2. Data Quality: By standardizing these core elements, Data Cloud ensures consistent, high-quality data across all integrated systems.
3. Flexibility: This set of DMOs provides a flexible foundation that can be extended to meet the specific needs of various industries and use cases.
4. Compliance: These DMOs include the necessary structures to manage consent and preferences across different contact points, supporting regulatory compliance.
5. Identity Resolution: The combination of these DMOs facilitates robust identity resolution, allowing businesses to maintain a single, unified view of each customer despite data coming from multiple sources.

Example Scenario: Consider a retail bank customer, Sarah Johnson:

• Individual DMO: Stores Sarah's name, birth date, and overall communication preferences.
• Party Identification DMO: Links Sarah's bank account number, online banking ID, and credit card numbers.
• Contact Point Email DMO: Stores Sarah's personal and work email addresses, noting her preference for receiving statements electronically.
• Contact Point Phone DMO: Includes Sarah's mobile and home phone numbers, with her mobile marked for receiving SMS alerts.
• Contact Point Address DMO: Contains Sarah's home address for mailing physical documents and her work address for the corporate credit card.

By requiring these five DMOs, Data Cloud ensures that every implementation starts with a solid foundation for customer data management, enabling businesses to create rich, multi-faceted customer profiles that support a wide range of customer engagement and analysis use cases.