Key concepts and differences in Optimizely Feature Experimentation
Key differences between Full Stack legacy and Optimizely Feature Experimentation.
Optimizely Feature Experimentation is a completely redesigned Full Stack experience. We believe this new experience will provide several benefits to you and your teams. When changing between the two versions, several key differences between Full Stack and Feature Experimentation are important.
Key concept changes
The following table explains the new concepts in Optimizely Feature Experimentation.
|Old Full Stack concept||New Optimizely Feature Experimentation concept||Feature Experimentation information|
|Feature||Flag||A place in code where a decision is made.|
|Feature variable||A flag configuration, including feature variations and on/off state that is delivered to the end-user.|
|Rollout||Conditions for what to deliver and why.|
|N/A||Ruleset||Full set of Rules for a Flag within an Environment.|
|Feature test||A standalone test to answer a specific question. For example, which of two (or more) variations performs best?|
|Activate an experiment||Make a flag decision (for a flag rule)||Use the Decide methods to return flag decisions for a user.|
The flag decision includes flag enabled/disabled status and flag variation.
|Whitelist||Allowlist||Functionality remains the same, showing a specific variation to up to 10 users that you have selected. The name was only updated for Only the name was updated for inclusivity.|
Flags are the primary component of the new Optimizely Feature Experimentation experience. They represent any place in your code base where you use Optimizely to control how an experience is delivered. They can range from a simple on-off toggle to a complex set of instructions about handling each segment of your user base.
A rule is the unit of targeting and allocation logic within a flag. Rules describe what variation the flag should deliver to a given user.
A ruleset is the collection of prioritized rules associated with a flag at a given time.
A variation consists of an on-off status for the flag.
Default variables can be associated with a flag and reused and referenced across your A/B test and targeted delivery to configure your flag with Optimizely remotely.
Updated about 1 month ago