Dynamics 365 Customer Insights - Data Flashcards
A platform with the goal of helping create a 360-degree view of customer across disparate data systems
Customer Data Platform
Two main type of exports from Customer Insights - Data
- Data-out - export of any type of entity that’s available in Customer Insights. The entities that you select for export are exported with all data fields, metadata, schemas, and mapping details
- Segment - export segment entities from Customer Insights - Data. Segments represent a list of customer profiles. When you’re setting up the export, select the included dat afields, depending on the target system that you plan to export data to.
How does Customer Insights - Data enhance the following application
- Model-driven applications
- Power Apps
- Power Automate
- Power Bi
Model-driven applications – Customer Insights – data can ingest data from model-driven applications such as Dynamics 365 Sales or Dynamics 365 Customer Service. Additionally, insights that are captured in the application can be surfaced directly on customer records in model-driven apps.
Power Apps - Apps that are created in Power Apps can be connected to Dynamics 365 Customer Insights - Data as a data source. This capability lets you use data from the unified customer profile and data from other applications simultaneously.
Power Automate - With Power Automate, you can run actions in other line-of-business applications based on data that is surfaced from Customer Insights - Data or on actions triggered in applications that are connected to Customer Insights - Data.
Power BI - The Customer Insights - Data Power BI connector enables you to use the unified customer profile within Microsoft Power BI to further analyze and uncover insight.
How does the unification process of Dynamics 365 Customer Insights include
- Profile mapping: This is where you identify which tables and fields from your data are used to identify the customer record such as a customer number.
- Matching: When matching, you define rules to merge your data sets into a single unified profile. These rules determine which fields from each dataset are used in the matching process.
- Merge into a unified profile: Completes the process and reconciles any conflicts that might be present.
- Define activities: Activities help to consolidate your customer activities across data sources and put them into a timeline view. These activities might represent things like interactions, or purchases.
- Enrichment your data: Enrichment uses data from other sources to help you better track brand affiliation and loyalty across hundreds of different brands and several interest-categories.
- Define measures: These measures are the KPIs that best reflect the performance and health of your business. The KPIs might represent satisfaction levels, revenue targets, or performance levels.
- Create segments: With segments, you can easily group your customers based on demographic, transactional, or behavioral customer attributes.
- Activate Customer Insights – Data in other applications: Using data in other apps is the main purpose of Customer Insights – Data. You can take items like the segments that you created and consume them in other applications such as Google Ads, Customer Insights – Journeys, and more.
This refers to the digital touch points a customer has with your organization.
Digital Twin
Customer profiles are created by unifying data ingested from your organization’s different data sources. Data can be ingested from a wide range of data sources through built-in connectors that connect to many different data providers.
How can data be ingested into Customer Insights - Data?
- Microsoft Power Query: Used when you want to import data such as Microsoft Dataverse, Azure Blobs, OData sources, etc.
- Azure Synapse Analytics (preview): Used when you want to connect to Azure Synapse Analytics.
- Azure data lake storage - Used when you want to connect to an Azure Data Lake Storage Gen 2 Account.
- Microsoft Dataverse - Used when you want to connect to data sets in the Dataverse data lake.
Once you identify the customer, you need to unify the data. What are the steps for the data unification process?
- Source columns - Define which tables and columns are combined to create a unified customer profile.
- Duplicate records - Define how to handle any duplicate records in your datasets
- Matching conditions - Defines the rules that are used to help combine your datasets into a unified customer profile
- Unify customer columns - Define final information such as which items to exclude, column ranking, and other details that could impact the merge.
Element of Customer Insights - Data which refers to the transactional data that represents how your customers are interacting with your organization such as customer purchases, visits to a website, or a case opened by a customer
Activities
Element of Customer Insights - Data that defines the key business and customer KPIs such as customer lifetime value, average purchase value and frequency, and CSAT, and identifies high-value customers
Measures
Elements of Customer Insights - Data that allows you to specify what you want to group and categorize your customers based on similar demographic, transactional, or behavioral attributes.
Segments
This element of Customer Insights - Data provides access to the different AI elements that are available ot use as part of your deployment
Predictions
What are thre three types of measure of Customer Insights - Data
- Customer Attribute - These measures represent a single field for each customer. They typically reflect a score, value, or state such as a customer’s lifetime value, total sales, or average purchase value.
- Customer measure - Provides input that is related to an individual customer’s behavior with breakdown by dimensions. For example, measuring the total number of visits that each of your customers made for each channel or each customer’s total sales each day.
- Business measure - Tracks items that are related to your business’s performance and health. They might include items such as average sales per customer and monthly active users (MAU).
This measure represent a single field for each customer. They typically reflect a score, value, or state such as a customer’s lifetime value, total sales, or average purchase value
Customer attribute
This measure provides input that is related to an individual customer’s behavior with breakdown by dimensions. For example, measuring the total number of visits that each of your customer made for each channel or each customer’s total sales each day.
Customer measure
This measure tracks items related to your business’s performance and health. They might include items such as average sale sper customer and monthly active users (MAU)
Business Measure
