Consume Customer Insights data
Typically, most end users do not access Customer Insights data directly from the Customer Insights - Data application. They consume the data inside applications they use every day. For example, a marketer might use segments created in Customer Insights - Data in a real-time marketing application like Customer Insights - Journeys. A sales organization might surface profile details from Customer Insights - Data into their CRM system to help salespeople provide better overall customer experience.
To get to this point, you need to build your Customer Insights - Data environment to support your organizational needs. At a high level, the process involves.
Ingest data - Defines the data sources that your data is coming from. Data can be ingested from a wide range of data sources through built-in connectors that connect to many different data providers.
Create customer profiles - Customer profiles are created through unifying the data that is ingested from your different data sources into a single profile.
The unification process consists of three steps:
Mapping customer data - Identifies which entities and fields from your data are used to identify the customer record, such as a customer number.
Deduplication rules – These are rules that are used identify duplicate customer records in a table and specify which ones should be kept.
Matching rules - Specifies how to combine your datasets into a unified profile through a series of rules that call out which fields are used during the matching process.
Unifying data - Completes the process and reconciles 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 interactions or purchases.
Define measures - Measures represent the analytics that best reflect the performance and health of your business. These measures might represent satisfaction levels, revenue targets, or performance levels.
Enrich data - Enrichments help you better understand who your customers are by using supplemental data that is provided by Microsoft and external sources to provide more detail, such as brand affinity and loyalties or financial details.
Build predictions - Prediction models let you use the power of AI to make predictions about your data, such as if a customer might be ready to purchase something or if they are in danger of not renewing a subscription. You can use out-of-the-box prediction models or use your own that you created by using tools such as Microsoft Azure.
Create segments - With segments, you can group your customers based on demographic, transactional, or behavioral customer attributes.
Activate the data - After you build your instance, you can use the information (measures, activities, and insights) in applications, such as other Microsoft Dynamics 365 apps, Microsoft Power Apps, LinkedIn Ads, Google Ads, and more.
Data activation
Let’s look at an example of data activation:
Mark works as a greeter in one of Contoso Coffees retail stores. As customers enter the store, Mark captures their names and phone numbers into a greeter application that Contoso uses. Then, Mark sends the customers to a store representative who can assist them. After the customer's information is captured in the greeter application, supported by data in Customer Insights, Mark provides the store representative with a complete picture of who the customer is.
This customer description includes:
All recent activities across Contoso's different touch points.
The customer's current reward points balance.
How much the customer has spent over their lifetime with Contoso.
Product recommendations based on past buying patterns.
