One of the most anticipated presentations at the Adobe Summit this year was the launch of Adobe’s  Auto-Target, a new automated personalization tool which uses machine learning to empower better A/B testing.
Auto-Target, which is coming out this month, extends its capability beyond just personalized images, and offers custom variations of page content mix. You will be able to access Auto-Target from Adobe Target just by selecting the radio button when specifying how to allocate traffic during creation of any A/B test activity.
In addition, ingesting machine data compiled from customer analytics enables the automation of dynamic decisions to make relevant, consistent, and personalized experiences at scale a reality.
“Use of customer analytics in A/B testing, results in 126% lift in profit over those who don’t”  – Jennifer Sun of Adobe
Adobe also presented it as using the latest in machine learning models which inform algorithms to empower the automation of AB/n test for any type of experience.
Basically, the way they’ve put it: each visitor sees what wins for them every visit. There are four different types of automation available for use in Adobe Target:

  • Auto-Allocate
  • Auto-Target
  • Automated Personalization
  • Recommendation.

 
You should use Auto-Allocate when you want one winner to be pushed live faster than a traditional A/B test and want to take advantage of the winning experience as soon as its identified.
On the other hand, Auto-Target should be used when you want more than a single winner and want to target multiple winners to individuals who adapt over time as each visitor’s interests changes.
Automated personalization is best used when you want to show the right offer or message to the right person at the right time (lots of rights, I know).
With Adobe’s Target you can also use Recommendations, when you want to recommend specific content, such as articles, videos, downloads, products, fact sheets, etc.
 

How to use the varying types of automation for different marketing strategies

 
With the features provided by Target, even if they are complementary, they have different purposes since they address different marketing strategies. For Auto-Allocate, you want winners to receive more traffic as the test matures, while with Auto-Target, you will want each visitor to see what wins for them every visit.
Automated Personalization will create the “perfect offer” for each visitor every time, while Recommendation will personalize suggestions at the user level.
Both Auto-Allocate and Auto-target are basically creating experiences from a Visual Experience Composer, a Visual Code Editor and Form Composer – which is used for mobile and Internet of Things. What distinguishes the two tools is that Auto Allocation is producing a statistical guarantee on a true winner, while Auto-Target is creating one-click personalization for the whole site experience, not just a banner or offer.
The distinguishing feature for Automated Personalization is the multivariate offer ranking for multiple content blocks.
Auto-Target and Auto-Allocate can be used for everything from content to UX, UI, functionality, layout.  Adobe takes great pride in saying that any A/B test can become Auto-Targeted and Auto-Allocated.  Automated Personalization to be used for high-level content and offers, but not for UX, while the Recommendations will be used for item-level content.
@Jennifer Sun, Senior Manager and Marketing Cloud Technical Consulting  at Adobe, and Shawn Martin, Growth Hacker at AT&T, both presented the new Adobe-Target tool. They also showed the results the major telecom company got from implementing auto personalization. And the results are indeed impressive. After AT&T launched automated personalization, machine learning led to some incredible lifts just by delivering right content to users. For example, Bogo (buy one, get one), got +220% lift, while Grab and Save Promo got a +82,09% lift, and goPhone got a +75,90% lift!

About the author

Sebastian Stan

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