Target activity types
Download an interactive PDF that describes the different activity types in Adobe Target.
What does it do? section_4ECAACC68723402EB3649033190E1BBC
Manual A/B Test
Compares two or more experiences to see which experience best improves conversions throughout a pre-specified test period.
For more information, see A/B Test.
Auto-Allocate
Identifies a winner among two or more experiences, and then redirects traffic to the winning experience, increasing conversion as the test runs and learns.
For more information, see Auto-Allocate.
Auto-Target
Uses advanced machine learning to personalize content and drive conversions by identifying multiple high-performing, marketer-defined experiences, and then serving the most tailored experience to visitors based on their individual customer profiles and past behaviors of similar visitors.
For more information, see Auto-Target For Personalized Experiences.
Automated Personalization (AP)
Uses advanced machine learning to personalize content and drive conversions by combining specific offers or messages, and then matching different offer variations to visitors, based on their individual customer profiles.
For more information, see Automated Personalization.
Multivariate Test (MVT)
Compares combinations of offers among elements on a page to see which combination of offers performs the best for a specific audience. Also, identifies which element of the page best improves conversions throughout a pre-specified test period.
For more information, see Multivariate Test.
Experience Targeting (XT)
Delivers content to a specific audience based on a set of marketer-defined rules and criteria.
For more information, see Experience Targeting.
Why are you using this type of activity? section_46A70DD7CE3448749E635DDF5EAFC131
What kind of marketer should use type of activity? section_A843D663D3E543FFB1A594266B560395
Is knowledgeable in stats.
Has the time to wait until end of test period to analyze results.
Has a short time frame.
Must identify the best experience and deliver quickly.
Wants to be able to “peek” at results as test runs.
Has several eligible experiences.
Wants to match experiences to specific visitors at optimal times based on their dynamic and changing profiles.
Has one or more offers.
Wants to create offers combinations that yield optimal personalized experiences for specific visitors across various unique profiles and behaviors.
Is knowledgeable in stats.
Has one or more offers.
Wants to analyze conversion trends relating to page element interactions.
Statistical details section_22CF2D07DB054505AB5EC702B99A5BB0
Benefits and considerations section_56C46ABEF7B945DDA0C1E6D714377123
In an A/B Test, if you look at the test results before the sample size is reached, you risk relying on inaccurate results (you cannot "peek"earlier!).
Unlike Auto-Allocate, in an A/B test, the traffic distribution remains fixed even after you recognize that some experiences are outperforming others.
For information about best practices for A/B Test activities, see How long should you run an A/B Test and Ten common A/B testing pitfalls and how to avoid them.
Auto-Allocate identifies the winner but does not differentiate among the losers. If you must know how each experience performed, A/B testing is preferable.
The Auto-Allocate feature works with only one advanced metric setting, which is “Increment Count and Keep User in Activity.” If you do not want to count repeat conversions, you should use A/B testing instead.
When you combine multiple offers, a combinatorial explosion occurs resulting in the need for a significant amount of traffic. The Automated Personalization algorithm accounts for many factors; therefore, requiring the most amount of traffic.
Automated Personalization cannot consume reports in Analytics for Target (A4T).
A Multivariate Test is time consuming, and due to the multiple variables at play, it does not necessarily produce a winning experience with confidence.
It is often challenging to reach the amount of traffic needed to complete the test. Because all Multivariate Test experiments are fully factorial, too many changing elements at once can quickly add up to many possible combinations that must be tested.
Even a site with fairly high traffic might have trouble completing a test with more than 25 combinations in a feasible amount of time.
With Experience Targeting, you can quickly act on insights deduced from any activity results.
For example, if you ran an A/B test where the challenger did not outperform the control, but the results indicate that a specific segment of visitors converted four times more with the challenger than they did with the control, then you can use Experience Targeting to direct the challenger experience to that particular segment.