Salesforce Certified Data Cloud Consultant Practice Test - Questions Answers, Page 13

Segment Creation in Data Cloud: Salesforce Data Cloud allows the creation of segments based on specific criteria for targeted marketing campaigns.

Activation Targets: After creating a segment, it must be activated to make the data available for use. Various activation targets can be configured based on how the segment data will be used.

File Storage Activation Target: To provide a list of contacts fitting a segment, creating a file storage activation target allows the segment data to be exported as a file. This file can then be shared with NTO for their mailing campaign.

Process:

Define the segment criteria in Salesforce Data Cloud.

Create a new file storage activation target.

Activate the segment to this target, which generates a downloadable file containing the segment membership details.

Salesforce Data Cloud Documentation: Segmentation

Salesforce Data Cloud Activation

Segment Creation in Data Cloud: Salesforce Data Cloud allows the creation of segments based on specific criteria for targeted marketing campaigns.

Activation Targets: After creating a segment, it must be activated to make the data available for use. Various activation targets can be configured based on how the segment data will be used.

File Storage Activation Target: To provide a list of contacts fitting a segment, creating a file storage activation target allows the segment data to be exported as a file. This file can then be shared with NTO for their mailing campaign.

Process:

Define the segment criteria in Salesforce Data Cloud.

Create a new file storage activation target.

Activate the segment to this target, which generates a downloadable file containing the segment membership details.

Salesforce Data Cloud Documentation: Segmentation

Salesforce Data Cloud Activation

Segment Creation in Data Cloud: Salesforce Data Cloud allows the creation of segments based on specific criteria for targeted marketing campaigns.

Activation Targets: After creating a segment, it must be activated to make the data available for use. Various activation targets can be configured based on how the segment data will be used.

File Storage Activation Target: To provide a list of contacts fitting a segment, creating a file storage activation target allows the segment data to be exported as a file. This file can then be shared with NTO for their mailing campaign.

Process:

Define the segment criteria in Salesforce Data Cloud.

Create a new file storage activation target.

Activate the segment to this target, which generates a downloadable file containing the segment membership details.

Salesforce Data Cloud Documentation: Segmentation

Salesforce Data Cloud Activation

Relationship Cardinality: In Salesforce Data Cloud, defining the correct relationship cardinality between data model objects (DMOs) is crucial for accurate data representation and integration.

1-to-1 Relationship Error: The error occurs because the relationship between Account DMO and Contact Point Address DMO is set as 1-to-1, which implies that each account can only have one contact point address.

Solution:

Change Cardinality: Modify the relationship cardinality to many-to-one. This allows multiple contact point addresses to be associated with a single account, reflecting real-world scenarios more accurately.

Steps:

Go to the data model configuration in Data Cloud.

Locate the relationship between Account DMO and Contact Point Address DMO.

Change the relationship type from 1-to-1 to many-to-one.

Benefits:

Accurate Representation: Accommodates real-world data scenarios where an account may have multiple contact points.

Error Resolution: Resolves the error and ensures smooth data integration.

Salesforce Data Cloud Documentation: Relationships

Salesforce Help: Data Modeling in Data Cloud

Consolidation Rate: The consolidation rate in Salesforce Data Cloud refers to the effectiveness of unifying records into a single profile. A low consolidation rate indicates that many records are not being successfully unified.

Matching Rules: Matching rules are critical in the identity resolution process. They define the criteria for identifying and merging duplicate records.

Solution:

Increase Matching Rules: Adding more matching rules improves the system's ability to identify duplicate records. This includes matching on additional fields or using more sophisticated matching algorithms.

Steps:

Access the Identity Resolution settings in Data Cloud.

Review the current matching rules.

Add new rules that consider more fields such as phone number, address, or other unique identifiers.

Benefits:

Improved Unification: Higher accuracy in matching and merging records, leading to a higher consolidation rate.

Comprehensive Profiles: Enhanced customer profiles with consolidated data from multiple sources.

Salesforce Data Cloud Identity Resolution

Salesforce Help: Matching Rules

Initial Data Load: For loading large volumes of historical data, such as the last 90 days of runner profiles and activity logs, bulk ingestion is the most efficient method. It allows for high-throughput data transfer.

Bulk Ingestion: Use Salesforce Data Cloud's bulk ingestion tools to load the historical data quickly and efficiently.

Ongoing Data Synchronization: To keep the Data Cloud updated with new and modified records as they become available in the Track My Run mobile app, streaming ingestion is appropriate. It ensures near-real-time data updates.

Streaming Ingestion: Configure streaming ingestion to continuously update the Data Cloud with new and updated data from the mobile app.

Sequence of Actions:

Step 1: Perform bulk ingestion to import the last 90 days of historical data into Data Cloud.

Step 2: Set up streaming ingestion to handle ongoing updates and new data as it becomes available.

Best Practice: This approach ensures that the initial large data load is handled efficiently, and ongoing updates are processed in near real-time, providing the marketing department with the most up-to-date data.

Salesforce Data Cloud Ingestion Methods

Salesforce Bulk Data Ingestion

Salesforce Streaming Data Ingestion

Segment Population vs. Data Extension Count: Minimizing the difference between Data Cloud segment populations and Marketing Cloud data extensions ensures accurate segment sizes for campaign planning.

Filtering by User Preferences: By filtering segments based on user preferences for marketing outreach, you ensure that only those contacts who have opted in or are eligible for marketing campaigns are included. This aligns the segment population in Data Cloud with the counts in Marketing Cloud.

Process:

Define Preferences: Ensure that user preferences for marketing outreach are clearly defined and captured in the system.

Filter Segments: Use these preferences to filter segments in Data Cloud, ensuring only the relevant contacts are included.

Benefits:

Accuracy: Increases the accuracy of segment sizes by including only those who have opted in for marketing.

Compliance: Helps in complying with regulatory requirements for marketing communications.

Salesforce Data Cloud Segmentation

Marketing Cloud Data Extensions

Field Data Types: Salesforce CRM's Contact object fields can store data in various formats. When ingesting data into Salesforce Data Cloud, the target field's data type determines how the data is processed and stored.

Date Data Type: If the target field in Data Cloud is set to Date data type, it is designed to store date values without time information.

Mixed Format Values: When ingesting a field containing both date (yyyy-mm-dd) and datetime (yyyy-mm-dd hh:mm:ss) values into a Date data type field:

The Date field will extract and store only the date part (yyyy-mm-dd), ignoring the time part (hh:mm:ss).

Result:

Date Values: yyyy-mm-dd values are stored as-is.

Datetime Values: yyyy-mm-dd hh:mm:ss values are truncated to yyyy-mm-dd, and the time component is ignored.

Salesforce Data Cloud Field Mapping

Salesforce Data Types

Unified Profile: Creating a unified customer profile in Salesforce Data Cloud involves consolidating data from various sources.

Reconciliation Rules: These rules determine which data source is considered the 'best' when conflicting data is encountered. Changing reconciliation rules allows prioritizing specific sources.

Source Priority: Setting source priority involves defining which data source should be preferred over others for specific attributes.

Process:

Step 1: Access the Data Cloud settings for reconciliation rules.

Step 2: Select the Contact Point Address details.

Step 3: Change the reconciliation rules for address attributes to 'Source Priority.'

Step 4: Move the Order DMO to the top of the priority list. This ensures that address details from customer orders are prioritized and selected as the best data to save to the unified profile.

Benefits:

Accuracy: Ensures the most accurate and reliable address data is used in the unified profile.

Relevance: Gives priority to the most relevant and frequently updated source (customer orders).

Salesforce Data Cloud Reconciliation Rules

Salesforce Unified Customer Profile