Dynamics 365 Customer Insights is a customer data platform (CDP) businesses use to provide customers with more personalized experiences. It enhances the functionality of Microsoft products like Dynamics 365 CRM and ERP by offering holistic insights into what’s happening with customers. That’s thanks to Customer Insight’s ability to combine customer data with information from Internet of Things (IoT) and operational tools. In addition, you can build upon the capabilities of Dynamics 365 Customer Insights by integrating AI capabilities.
Customer Insights helps customers get started with data predictions by providing predefined or out-of-the-box models. The following options depend on whether you want to target consumers (B2C) or other businesses (B2C).
If you’ve already developed machine learning scenarios with Azure Machine Learning experiments, you can use the custom models feature in Customer Insights to set up new connections. For example, you can select data from which to generate insights, then map the results to customer profiles.
You’ll need to have web services already published through Azure Machine Learning batch pipelines published under a pipeline endpoint. In addition, you need to have an Azure Data Lake Gen2 storage account. If your Azure Machine Learning workspace has pipelines, you must have owner or user access administrator permissions. Remember that you can’t use data sources updated with incremental refreshes in custom models within Customer Insights.
Refreshing Predictions
Go to the Insights -> Predictions page, then select the My Predictions tab. There, you can view essential information like:
Users can also perform any of the following actions:
Before you transfer data to an Azure service, make sure you’ve configured the service to process data in a way that complies with any legal or regulatory requirements for your company.
You can edit, run, and delete workflows when navigating to Insights -> Custom models to view existing workflows.
The following actions will delete a workflow. However, you can still view the table created for it when you go to the Data -> Tables page.
If you have configuration issues when establishing a custom model workflow, there might be a configuration issue in your pipeline. First, make sure you’ve configured your input parameter correctly, along with your output datastore and path parameters.
Some users might see an error message telling them they couldn’t save the workflow. That typically means they lack proper access to the workspace. Contact the administrator to obtain a higher permission level that lets you process the workflow as a service.
While AI benefits companies through improved customer experiences, more effective business capabilities, and increased revenue streams, you should still take steps to use the technology responsibly. For example, try to balance your prediction’s value and any inherent biases when making your predictions.
Artificial intelligence can be challenging to work with if you are unfamiliar with the technology. Internet eBusiness Solutions (IES) helps organizations bridge that knowledge gap and learn to integrate AI effectively and ethically. Learn more about our services by contacting an IES representative.