Earlier this year, Adobe took the wraps off its new Adobe Marketing Cloud, touting new data science capabilities like Adobe Analytics' Segment IQ, which uses machine learning to help marketers gain deep insight into audience segments. On Wednesday, Adobe advanced Segment IQ another step with the release of Segment Comparison for Analysis Workspace.

Segment Comparison for Analysis Workspace is the first in what Adobe promises will be a series of audience analysis and discovery tools within Segment IQ. It uses machine learning techniques to perform automated analysis on every metric and dimension to which you have access. Nate Smith, senior product marketing manager, Adobe Analytics, says this allows Segment Comparison to uncover the key characteristics of the audience segments that are driving your company's KPIs.

Once Segment Comparison is trained on your data — which takes virtually no time at all for existing Adobe customers because it can access historical data — Smith says brands can use it to complete a comprehensive segment analysis within minutes with just a few mouse clicks, comparing every dimension, metric or data point between any two segments and automatically discovering the most significant differences between them.

ML could eventually dictate not just the subject matter of content, but also the right medium, the tone, and other critical facets of how brand stories are told. It could drive immersive experiences based on interactive content: Basic ML components could be built into interactive content, creating a personalized experience that leverages a combination of in-house data and user feedback to customize real-time content on a one-to-one level. A short quiz to help consumers define their fashion preferences, for example, could use answers from past questions to refine recommendations much more quickly and accurately, and to present more relevant questions than ones presented through a standardized quiz.

Eventually, ML could also be used as a tool to drive non-linear brand storytelling, or to evaluate and maintain narrative arcs and brand voice. It isn’t hard to imagine a brand building a complex narrative over a large span of time and across a variety of mediums: In this scenario, ML could ensure that connecting pieces of content match one another in tone and fit into the rest of the content. ML can essentially test the countless paths consumers may take through content, crossing multiple channels in the process and make sure that each step is synced with the one before and after it. That’s a process that would take human users far too much time to do on their own, and it would be rife with human error if they did.