With Copilot, you can easily access rich insights like predicted lifetime value, propensity to buy, recent transactions, and interactions. Agents and sellers can ask Copilot for information about specific customers directly in the flow of their work and get relevant data right away. Even if they're unsure about what data is available or how to find it, Copilot provides immediate, seamless access. It empowers teams to engage more effectively while securing access controls and boundaries.

Scenario:

Sarah, a sales representative at a large retail company, prepares for an important meeting with a high-value lead named Mark. Before the meeting, Sarah wants to understand Mark's purchasing behavior and level of engagement, but she's short on time and unsure about what specific data she can access.

Sarah asks Copilot, "What's Mark's predicted lifetime value and propensity to buy?" Copilot returns insights from Customer Insights - Data. It gives Sarah Mark's predicted lifetime value, recent purchases, and interactions with the company. Copilot also shows that Mark has a high propensity to buy but has a medium risk of churn because of recent inactivity.

With these insights, Sarah tailors her meeting approach. She focuses on reengaging Mark with a personalized offer to prevent churn. She engages him more effectively by showing that she understands his needs. This approach leads to a successful meeting and a stronger customer relationship.

Autonomous agents that understand your unified customer data are powerful assets. With these agents, you can build organizational efficiencies that you couldn't achieve before.

In Microsoft Copilot Studio, you can add customer profiles, segments, measures, and other insights from Dataverse as knowledge sources for any custom or customizable autonomous agents.

• When you launch or update the agent, all users instantly get access to your key customer insights.
• Create agents that perform autonomous actions or respond to changes in your unified customer insights.

Scenario:

Sally is a seller who evaluates a promising new lead named Joe. Joe recently attended a new product launch event and has an existing relationship with the company. Using data from Customer Insights, agents in Dynamics 365 Sales (such as the sales qualification agent) access information about Joe's total spending, his VIP status, and his recent interactions across other touchpoints.

With these insights, the agent can make a more informed decision about Joe's likelihood of purchasing than would have been possible without this data. This way, autonomous agents instantly democratize the key customer insights Sally needs to determine how important Joe is for her prospecting efforts.

Delta Lake format lets you track changes to your data over time. When you use Customer Insights with native support for the Delta Lake table format, you can use Delta Lake incremental data updates to speed up processing in Customer Insights - Data.

Customer profile data usually changes by only a small percentage each day. Instead of reprocessing the entire data set every day, Customer Insights - Data processes only the changed source data for unification in the Delta format. This approach saves time and delivers results faster. This improvement happens automatically when all tables use the Delta format. You don't need to set up or change anything.

We plan to add incremental processing for segments, measures, activities, and predictions in future updates.

Virtual Network support uses Azure subnet delegation to manage outbound traffic from Power Platform at runtime. When Power Platform uses Azure subnet delegation, you don't need to make protected resources available over the internet to integrate with Customer Insights - Data.

Customer Insights - Data uses the Virtual Network support in Power Platform to securely configure Private Link connections to your Azure storage behind a firewall. Customer Insights - Data uses private links for three scenarios:

• Data input: Use data sources to bring data from your Azure storage into Customer Insights - Data for processing.
• Data output: Set up Customer Insights - Data to write all output to your Azure storage.
• Exports: Configure exports to send specific tables to your Azure storage.

All new private links use Virtual Network support. You need to update existing private links.

For more information about platform support, see Virtual Network support overview.

A common challenge when working with customer source data is dealing with old and obsolete source data that you ingest with the data you want. But preprocessing your source data takes extra time and effort.

With Customer Insights - Data, you can apply row filters directly to any source table you ingest. This filter feature reduces the data scope to just what you need. You apply filters one time on the Tables page. The Tables page displays source tables on their own tab. This tab separates source tables from the output tables that Customer Insights creates. Select the row filter link next to any table to set the filters you want.

To optimize processing speed, Spark filters data during processing for unification, segments, measures, activities, and models. When you download data from the Tables page, you get the full dataset without filters.

This feature offers key benefits:

• Faster processing: By removing unwanted rows, the system skips unnecessary data and cuts down processing time.
• Better output data: When you remove unnecessary data from the output, you make it easier for consumers to use the data without needing to exclude unwanted data.

Preview limitation

The condition Is any of {list of values} works only for string data.

Easily explore and select customer data stored in your OneLake for processing in Customer Insights - Data. The OneLake data connector connects directly to your OneLake data, so you don't need to copy data.

• Use Fabric shortcuts and mirroring to quickly integrate data from different external sources and bring that data into Customer Insights - Data for processing.
• For OneLake data in the Delta format (the default format for OneLake), take advantage of incremental processing of Delta data, which releases in 2025. This feature processes only recently changed data and dramatically reduces processing times.

Scenario: Contoso centralizes its customer data in Microsoft Fabric's OneLake. Customer Insights - Data connects to OneLake to read source data without copying, staging, or transforming data for the product. By creating shortcuts and mirrors to external sources like Snowflake within OneLake, Contoso accesses enterprise data seamlessly without extra data movement or duplication. OneLake stores data in Delta format, so Customer Insights - Data processes only the changes since the last refresh. This setup cuts processing time and speeds up access to insights.

With Fabric and Customer Insights - Data, Contoso replaces slow, costly ETL pipelines with a real-time, unified data foundation. This solution streamlines operations, lowers overhead, and enables faster, more responsive customer engagement.

Unified profiles and insights are only useful when you can act on the data. If you use Microsoft Fabric, Customer Insights - Data makes it easier to share valuable unified profiles and insights directly in OneLake. You can add those insights to your core analytics and downstream processes.

Scenario:

Contoso has multiple departments with distinct systems and data requirements. After creating shortcuts in Fabric OneLake to all their Customer Insights - Data profiles and insights, the entire Contoso organization can leverage the unique advantage of customer 360-degree profiles across all the workloads of the organization.

The enhanced visibility and collaboration from sharing unified customer profiles and insights in Fabric OneLake creates a cycle where several departments take steps to integrate their unique customer data into unified profiles. This process improves the fidelity and value of the profiles.

In this release, you can use Azure Data Lake Storage (ADLS) exports with general availability.

Scenario Sarah unifies her customer data and creates a segment based on requirements from across the organization. Multiple teams at her company use these insights. Each team needs a consistent and reliable mechanism to get updates every day. Sarah configures Customer Insights - Data to automatically export all the insights her teams need each day. Every day, millions of customer data points get unified, activity data is processed, measures are calculated, and customer segments are created. The solution automatically sends the resulting insights to the Azure Data Lake storage that all her teams read from.

You can now automatically access behavioral data from Customer Insights - Journeys, such as email opens and link clicks, in Customer Insights - Data without any setup or data movement. With this feature, you can:

• Use interactions from Customer Insights - Journeys to build precise segments and personalize engagement in Customer Insights - Data.
• Combine Customer Insights - Journeys interaction data with other external data sources to create powerful customer measures for scoring and targeting customers with greater precision.
• Identify customers who don't respond to your campaigns and retarget them by using external systems like Google and Meta.
• Use unification to identify customers with multiple emails and see how they interact with your brand across different contact points, so you can see the customer and not just the email address.

Scenario: As a marketing manager at an online retail store, you send a monthly campaign that offers a 5% discount to customers. When you review the results, you see a high email open rate but a low click-through rate on the coupon. With Customer Insights - Data, you can see which contacts opened the email and interacted with your product. You create a new segment of customers who opened the email but didn't click the coupon, and you retarget them with a more compelling 10% offer. When you act on real engagement signals, you refine your audience, increase relevance, and boost sales.

The following export connectors will be available in this release:

• Facebook Ads
• Google Ads
• LinkedIn
• Secure FTP (SFTP)
• Snapchat

Scenarios:

• Sarah has unified the customer data and created segments to target customers with a high lifetime value. Sarah wants to target this audience with ads on Google and Facebook, so she exports the audience to Google and Facebook where she configures her online campaign.

• Mark is an admin managing the Customer Insights - Data environment. Mark's team wants to create Power BI reports based on changes in segment membership for key segments. To do this, they can now export segment data to their Azure Data Lake Storage account. Once the data is in Azure Data Lake, they can easily create Power BI reports and perform other business-critical operations. Exporting to their own Azure Data Lake also provides the flexibility to reshape the data to meet specific business needs and integrate it into other workflows powered by their Azure data estate.