Adobe Target now features Adobe Analytics-enhanced reporting with AI-powered testing and personalization capabilities, such as Auto-Allocate, Auto-Target, and Recommendations. 


The new updates are leveraging Adobe Sensei and machine learning technology, which provides a new way for marketers to optimize their campaigns, view comprehensive reporting, and have a full outlook of performance impact. 


In addition, there is now a new Target dashboard available in Analysis Workspace that provides rich visualizations and deeper analysis. This means faster automation-driven testing and personalization activity execution, greater confidence in what the algorithms are driving, and a holistic view of performance impact.


With the sudden rise in working from home due to the COVID-19 pandemic, digital traffic levels have soared, and according to Adobe, they are approaching Cyber-Monday levels. While companies are constantly trying to find new ways to adapt and optimize customer engagement methods, personalization and testing teams objective must be to find and launch new customer journeys. 


“The ability to personalize and test new experiences with an optimization solution helps to avoid what can be months of development through normal processes with the creative and design teams. For example, one major media provider optimized a new self-service customer journey with Adobe Target in their mobile app for sports package deferment in a matter of hours that saved millions in call center costs,” Adobe states. 


What’s New In Adobe Target


  • Confidence in AI reporting and impact with analytics: Companies using a third-party tool can see a wide variance, sometimes up to 35-50%, between their testing and personalization reporting vs. an analytics tool, leading to a lack of confidence in results. Adobe Target alongside Adobe Analytics provides no-variance results, enabling deep drill-down and qualification of impact.


  • Easy integration development/maintenance: Adobe Target provides a pre-configured, server-to-server integration with Adobe Analytics, so there is no need for heavy implementation and support from IT or developers to use a testing or personalization tool.


  • Accelerated activity setup: Rather than manually configuring segments or success metrics for reporting, marketers can click “Analytics” and use all of their analytics segments and success metrics in reports. This saves hours in each activity setup and ultimately thousands of hours in analysis across activities.


  • Ad-hoc and post-hoc reporting: Marketers can be confident in the data from the rich reporting in Adobe Analytics. It’s even possible to answer ad-hoc questions on audience segments that they didn’t think of when initially setting up the activity.


AI-Powered Personalization Use Cases


  • Auto-Allocate is used for testing in low traffic areas or to find a winner faster on time-sensitive campaigns. Marketers can test several options for a new customer self-service funnel on the site or app and use the algorithm to ensure that they quickly provide the best journey.


  • Auto-Target replaces the need for hundreds of static landing pages by dynamically ranking the right offers or creative to show at each visit, which is personalized for each individual. Marketers can use analytics-enhanced reporting to show how each experience impacted the performance of each key metric further down the customer journey. Analytics-enhanced reporting for auto-target is now in Beta, and will be generally available later this summer.


  • Recommendations provide a spectrum of customizable algorithms including “Recommended For You,” offering personalized options based on a visitor’s browsing history with the brand to reduce clicks and increase conversions.
About the author

Sebastian Stan

Sebastian is a journalist and digital strategist with years of experience in the news industry, social media, content creation and management, and digital analytics.

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