Goal smart assets operate in one of two modes – A/B/n Testing and Machine learning.
When operating in A/B/n testing mode, a goal smart asset initially serves random content from a series of content elements for the duration of a test phase. During this time, visitors are monitored to determine whether they attain a defined goal – e.g., clicking a link. At the end of the test phase, the results are analyzed by RPI to determine whether one of the content elements was statistically significant in influencing behavior regarding the defined goal. If so, the winning content element will henceforward be served by the goal smart asset. After the initial test phase’s completion, a percentage of targets can continue to be tested at a defined interval. If another content element proves more successful than the winning asset, it becomes the winner, and is served thereafter.
When operating in machine learning mode, the content element served to a visitor is based on an analysis of their visitor profile. A machine learning algorithm is used instead of the statistical approach employed when using A/B/n testing.
The goal smart asset content panel contains the following:
•Tabset: exposing Asset Settings and Training Settings tabs.
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