A common question that comes up when you begin to discuss Customer Insights and its ability to unify and report on your customer data is: “Why can’t I just use Power BI for that?” I thought it might be helpful to provide some bullet points to help clarify how Customer Insights is differentiated from Power BI.
- Artificial Intelligence/Machine Learning: Customer Insights leverages sophisticated algorithms to help match customer records from disparate systems that don’t have IDs or keys in common.
- Common Data Service (CDS): Data that you ingest into Customer Insights can be mapped to CDS entities. For example, if you connect a contact from your order processing system, you can map it to the CDS “Contact” entity. Or if you bring in lead data from salesforce.com, you can map it to the CDS “Lead” entity. This is an optional step, but it allows you to begin to normalize your data so it can be used in other applications.
- Extensible Machine Learning Models: Once you have unified your data in Customer Insights you can leverage the UI to create measures and KPIs. But you can also extend this functionality by incorporating custom machine learning models by consuming them from Azure Machine Learning Studio.
- Third-party Data Enrichment: In addition to unifying the data you own internally, you can overlay third-party data to enrich your customer profiles. Microsoft makes this easy with built-in enrichment functionality from the Microsoft Graph to help you understand your customers’ potential interests and brand affinities.
- Segmentation: Customer Insights allows you to create segments (both static and dynamic) that span attributes across your unified and enriched data sources. These segments can be leveraged in your marketing automation system, with built-in connectors to Dynamics or by exporting the segment for use in another application. (In upcoming releases, Microsoft will also have “recommended” segments generated by algorithms that spot patterns and opportunities in your customer data that you might not otherwise look for.
- Integration with Power Platform/Dynamics: This may not strictly be a differentiator from Power BI, as of course Power BI also integrates nicely with its family of products. But Customer Insights offers a pre-built solution that can be installed in Dynamics 365 Sales to add customer cards/measures to the Contact forms so you can quickly leverage your unified data. There are also connectors so you can build Power Automate Flows/Automations (do we still call them Flows?), Power Virtual Agents, Power Apps, etc., that leverage your unified customer data.
- Time-to-Value: Another huge differentiator between Customer Insights and Power BI (and most other BI tools) is the time-to-value. Connecting to multiple data sources, normalizing them, matching them, and extracting meaningful, actionable insights can be a huge undertaking. Not only does Customer Insights make this process fast and easy, it is UI-driven, meaning you can get to these valuable insights without having to resort to code and complex queries.
- Approachability/Relevance to Marketers: While Customer Insights is not intended to be an “end user” application, it is designed so that marketing analysts and professionals will feel comfortable working in it. While both Customer Insights and Power BI may benefit from having a data analyst involved in the initial setup and configuration, I would argue that even a seasoned marketing professional might have difficulty extracting ad hoc data from Power BI. But they can quickly become adept at building segments in Customer Insights for use in their marketing efforts.
- Customer Profiles with Demographic, Behavioral Data and Activities: The old saying about “if your only tool is a hammer, every problem looks like a nail” comes to mind. Power BI is powerful and allows you to present data in many ways. But it is a general or horizontal tool for data analysis and reporting. Maybe it is more of a Swiss Army knife as opposed to a hammer. Customer Insights, on the other hand, is focused specifically on understanding your customer and the data points surrounding your customer – how they interact with your website and other marketing channels, what they purchase from you, their interests, etc. For professionals, it is often better to have a tool that is specifically crafted for a specific job, rather than a general-purpose tool that “almost” does the job as well.
- Data Ranking: As you unify your customer data, Customer Insights allows you to choose which data source will “win” for any attribute you wish to use on the customer card/profile. For example, your e-commerce data might be more reliable for email addresses, but your CRM data is more reliable for street addresses. Your frequent flyer data might be a more reliable source of mobile phone numbers, but your support desk data might be more reliable when it comes to work phone numbers. As you bring all of this together to make a single view of your customer, you can rank which system will win if both have different data for that attribute. This would be much more difficult to achieve in Power BI (I’m sure one of my Power BI friends will tell me how to do it though!), but it’s a simple matter of point-and-click in CI.
- Leapfrogging (Easy Introduction to Sophisticated Data Analysis): Customer Insights makes it easy for businesses to begin using tools and techniques that are typically reserved for very large or sophisticated enterprises. While those organizations who do not leverage CI might have to invest in expensive data scientists and months-long implementations, you can leapfrog them by quickly getting the value of these tools from Customer Insights. For example, connecting your data through the Customer Insights interface will bring your data into an Azure Data Lake. You don’t need to know anything about data lakes, how to construct them or use them, to make use of Customer Insights. But as your organization grows in sophistication, you will have a nicely managed data lake already available to leverage for other applications and scenarios. Similarly, your organization may be years away from building and using your own machine learning models, but with Customer Insights, you can begin using this technology right away and open the door much sooner to the possibilities of ML than you might otherwise have done.
Matt Wittemann 