Define Your Goals for a Winning Product Strategy

Product teams are looking to enrich and differentiate products with analytics. The ability to deploy Generative AI (GenAI) capabilities creates opportunities for competitive differentiation and revenue streams. 

Software vendors can extend their offerings with branded custom analytics and data workflows. 

Organizations can deploy custom workflows within existing internal applications to create actionable outcomes and drive more value from data investments.

Consider what business goals you want to solve

Your potential business goals of AI-powered embedded analytics may include:

• Increase revenue of existing products and services as part of the “plain vanilla” service that every customer receives.
• Created a tiered offering whereby customers pay more for the higher-level plans that include AI-powered embedded analytics.
• Raise customer satisfaction and reduce churn by adding more value to existing services.
• Launch brand new data-driven services.
  Guide your organization’s workforce to make better data-informed decisions, improve productivity and gain a competitive advantage.

Identify Key Questions your Analytics will Answer

A next step is to identify key questions your analytics will answer. Consider what people will learn and what actions they can take based on the data in the embedded analytics. 

From a differentiation standpoint, consider how this is better or different than the options that people have today. Clearly describe the purpose of the new functionality and its benefits to your target audiences.

Specify the data sources and levels of interactivity to drill down or filter 

At some point you should start to scope the types of data your AI-powered analytics will expose. Your options for data sources range from comma-separated values (CSV) and text files to databases, data warehouses, data lakes and in-memory engines as well as streaming data integration pipelines.

Your options to allow people to interact with their data include to hover over an object to reveal some additional text or to trigger an action, filter to see a subset, click to drill down to more granular underlying data, and use GenAI to ask natural language questions in a chat box.

Often embedded analytics involve read-only use cases. If the user makes an action, such as an online banking customer clicking to set up a new savings account, this can then be recorded in a separate parameter table. In some use cases, you may want to allow write-back to the data source, such as a person updating their mailing address. 

Find Areas of Personalization

Find areas of personalization through AI-generated insights or recommendations 

For competitive differentiation, it’s helpful to evaluate areas for personalization through AI-generated insights or recommendations. Stay competitive with augmented analytics, guided machine learning (ML) workflows and AI integration.

When done well, embedding natural language chat boxes can be an elegant addition to your product with functionality that users find appealing and natural. 

One important nuance to consider for GenAI is whether the user prompts and answers are persisted or not. A consumer may conflate the GenAI chat box with customer support chat and give an instruction for example to update their mailing address. Many GenAI models will respond along the lines of “Thank you, I have updated your address”. If the GenAI prompts and replies are not persisted, the GenAI model will forget that the user has a new address.

Avoid the deficiencies of previous-generation embedded analytics 

With the previous generation of embedded analytics, the iframes can slow down page load time and feel out of place in the navigation. Security problems include cross-site scripting attacks and redirects from the source site URL to fradulent websites.

Website content within iframes may not be indexed or considered in the same way as main page content by search engines.

Customers increasingly expect to find analytics and reporting as part of their everyday services. Meanwhile, many product teams face limited development time, lengthy product roadmaps, and the management of a complex code base. 

These considerations for how to do more with less favor a newer approach for AI-powered embedded analytics such as MicroStrategy ONE.

Next Steps 

If you would like to see samples of what you can build with the next generation of AI-powered embedded analytics, explore the MicroStrategy Demo Library. 

Introduction to MicroStrategy for AI-Powered Embedded Analytics

Hundreds of global companies and public sector agencies trust MicroStrategy as an AI-powered embedded analytics partner to deliver customized analytics experiences on top of a secure layer of trusted metadata with options to add natural language discovery, predictive analytics, and geospatial analysis. 

While other BI and AI vendors have been acquired, merged, sold, or divested, MicroStrategy is the world’s largest independent provider of AI-powered pervasive analytics at scale.

Unlike the previous generation of embedded analytics, MicroStrategy makes it possible for you to integrate controls and actions so they feel like part of your apps and systems. MicroStrategy offers several choices for pricing models including white labeling and revenue sharing.

Learn more about how to make every app intelligent with MicroStrategy AI-powered embedded analytics at https://www.microstrategy.com/research-and-reports/guide-to-making-every-app-intelligent-with-embedded-analytics.