Course Description
Dynamics 365 Customer Insights - Data specialists implement solutions that provide insight into customer profiles and that track engagement activities to help improve customer experiences and increase customer retention. In this course, students will learn about the Dynamics 365 Customer Insights - Data solution, including how to unify customer data with prebuilt connectors, predict customer intent with rich segmentation, and maintain control of customer data. This course begins with importing and transforming your customer data and culminates with extending your customer data platform solution into the Power Platform and Dynamics 365 applications.
Audience Profile
Candidates should be familiar with Dynamics 365 Customer Insights - Data and have firsthand experience with one or more additional Dynamics 365 apps, Power Query, Microsoft Dataverse, Common Data Model, and Microsoft Power Platform. They should also have working knowledge of practices related to privacy, compliance, consent, security, responsible AI, and data retention policy.
About This Course
Skills at a glance
-
Describe Dynamics 365 Customer Insights – Data (5–10%)
-
Ingest data (10–15%)
-
Create customer profiles through data unification (35–40%)
-
Implement AI predictions (5–10%)
-
Configure measures and segments (15–20%)
-
Configure third-party connections (5–10%)
-
Administer Customer Insights – Data (5–10%)
-
Describe Customer Insights - Data components
-
Describe support for near real-time updates
-
Describe the differences between individual consumer and business account profiles
-
Describe support for Microsoft Fabric
-
Describe the tables and relationships in Customer Insights - Data
-
Describe real-time ingestion capabilities and limitations
-
Describe benefits of pre-unification data enrichment
-
Identify when to use the managed data lake or an organization’s own data lake
-
Describe use cases for Customer Insights - Data
-
Describe use cases for Customer Insights - Data APIs
-
Describe the integration between Customers Insights - Data and Customer Insights - Journeys
-
Describe use cases for machine learning
-
Attach to Microsoft Dataverse
-
Attach to Azure Data Lake Storage
-
Ingest and transform data by using Power Query
-
Attach to Azure Synapse Analytics
-
Update Unified Customer Profile fields in near real-time
-
Troubleshoot common ingestion errors
-
Attach to data stored in Delta Lake format
-
Configure incremental refresh
-
Select tables and columns
-
Resolve data inconsistencies, unexpected or null values, and data quality issues
-
Evaluate and transform column data types
-
Transform data from Dataverse
-
Select Customer Insights tables and attributes for unification
-
Describe attribute types
-
Describe the requirements for a primary key
-
Deduplicate enriched tables
-
Define deduplication rules, including exceptions, winner, and alternate records
-
Manage merged preferences
-
Specify a match order for tables
-
Define match rules
-
Define exceptions
-
Include enriched tables in matching
-
Configure normalization options
-
Differentiate between basic and custom precision methods
-
Configure custom match conditions
-
Specify the order of fields for merged tables
-
Combine fields into a merged field
-
Combine a group of fields
-
Separate fields from a merged field
-
Exclude fields from a merge
-
Change the order of fields
-
Rename fields
-
Group profiles into Clusters
-
Configure customer ID generation
-
Describe B2B unification
-
Describe business unit separation prerequisites
-
Access business data in Dataverse
-
Implement Customer Insights - Data business unit integrations
-
Review and create customer profiles
-
View the results of data unification
-
Verify output tables from data unification
-
Update the unification settings
-
Create and manage relationships
-
Create and manage activities
-
Combine customer profiles with activity data from unknown users
-
Describe how to use customer consent
-
Describe how to use web data for personalization
-
Describe relationship paths
-
Set the B2B account relationship with contacts
-
Define which fields should be searchable
-
Define filter options for fields
-
Define indexed fields
-
Configure and evaluate the customer churn models, including the transactional churn and subscription churn models
-
Configure and evaluate the product recommendation model
-
Configure and evaluate the customer lifetime value model
-
Configure and manage sentiment analysis
-
Describe prerequisites for using custom Azure Machine Learning models in Customer Insights - Data
-
Create and manage workflows that consume machine learning models
-
Describe prerequisites for using custom models from Azure Synapse Analytics in Customer Insights - Data
-
Create and manage tags
-
Describe the different types of measures
-
Create a measure
-
Configure measure calculations
-
Modify dimensions
-
Schedule measures
-
Describe methods for creating segments, including segment builder and quick segments
-
Create a segment from customer profiles or measures
-
Create a segment based on a prediction model
-
Describe projected attributes
-
Schedule segments
-
Describe how the system suggests segments for use
-
Create a suggested segment based on a measure
-
Create a suggested segment based on activity
-
Configure overlap segments
-
Configure differentiated segments
-
Review the overlap or differentiator analysis
-
Find similar customers by using AI
-
Configure a connection for exporting data
-
Create a data export
-
Define types of exports
-
Configure on demand and scheduled data exports
-
Define the limitations of segment exports
-
Enrich customer profiles
-
Configure and manage enrichments
-
Enrich data sources before unification
-
Identify who can create environments
-
Differentiate between trial, sandbox, and production environments
-
Connect Customer Insights - Data to Dataverse
-
Connect Customer Insights - Data with Azure Data Lake Storage Account
-
Manage environments
-
Assign user permissions
-
Create an environment in Customer Insights - Data
-
Manage keys in Azure key vault
-
Differentiate between system refreshes and data source refreshes
-
Describe the system refresh process
-
Configure a system refresh schedule
-
Monitor and troubleshoot refreshes
4 Days