• by Laurie Sullivan  @lauriesullivan, September 24, 2018

Adobe on Monday unveiled technology in Adobe Analytics that unearths insights without the user asking. John Bates, director of product management at Adobe Analytics, says the product focuses on uncovering “unknown unknowns” -- the ability to collect, analyze and understand the data based on triggers users may not ask for.

Three years in the making, the virtual analyst -- a machine-learning tool built on a variety of Adobe products like Anomaly Detection and Contribution Analysis -- integrates into Adobe Sensei, the company’s AI and machine-learning framework. It surfaces signals that would have otherwise gone unnoticed.

In a first for Adobe, the platform collects the data on behalf of brands and applies machine learning to better understand preferences and tastes of individuals who log into Adobe Analytics, before combining it with insights that the system recommends. It continually analyzes the trending data based on similarities, identifies meta or macro events and eventually adjusts based on machine learning.

Some of the features that users can expect to see in the future will focus on the platform suggesting next actions, actions to take as a result of the insights such as email remarketing or targeting campaigns. “This is a stepping stone for a much broader vision,” he says.

The data collected by Adobe on behalf of its customers increase at a double digit rate, year over year, he says. They only use a sliver. “We collect hundreds of billions of data points, but the portion being accessed by marketers and analysts across the enterprise is between 1% and 3%,” he says.

In addition to identifying unknown knowns, the technology prioritizes  data  based  on  business  and  user  context  without  the  user  having  to  prompt  the  system.  It will identify nuances in historical data for analysts. The platform also uses AI to identify and understand the context.

Adaptive learning helps the platform understand the data important to the user and provide alerts. It provides  a  means  to  “like”  or  “not  like” recommendations,  which  reinforces  the  machine  learning  model  and  make s the virtual  analyst  more  intelligent.