Optimizely Feature Experimentation

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What is Optimizely Feature Experimentation?

Optimizely Feature Experimentation combines experimentation and feature flagging into one platform with the addition of integrated, built for purpose collaboration tools. It is used to optimize user experiences across digital channels and make every release high quality by quickly and safely validating code and features with real users through the entire software development cycle.

Categories & Use Cases

Screenshot of Feature Flag Setup. Here users can run flexible A/B and multi-armed bandit tests, as well as:

- Set up a single feature flag to test multiple variations and experiment types
- Enable targeted deliveries and rollouts for more precise experimentation
- Roll back changes quickly when needed to ensure experiment accuracy and reduce risks
- Increase testing flexibility with control over experiment types and delivery methods

Screenshot of Audience Setup. This is used to target specific user segments for personalized experiments, and:

- Create and customize audiences based on user attributes
- Refine audience segments to ensure the right users are included in tests
- Enhance experiment relevance by setting specific conditions for user groups

Screenshot of Experiment Results, supporting the analysis and optimization of experimentation outcomes. Viewers can also:

- examine detailed experiment results, including key metrics like conversion rates and statistical significance
- Compare variations side-by-side to identify winning treatments
- Use advanced filters to segment and drill down into specific audience or test data

Screenshot of a Program Overview. These offer insights into any experimentation program’s performance. It also offers:

- A comprehensive view of the entire experimentation program’s status and progress
- Monitoring for key performance metrics like test velocity, success rates, and overall impact
- Evaluation of the impact of experiments with easy-to-read visualizations and reporting tools
- Performance tracking of experiments over time to guide decision-making and optimize strategies

Screenshot of AI Variable Suggestions. These enhance experimentation with AI-driven insights, and can also help with:

- Generating multiple content variations with AI to speed up experiment design
- Improving test quality with content suggestions
- Increasing experimentation velocity and achieving better outcomes with AI-powered optimization

Screenshot of Schedule Changes, to streamline experimentation. Users can also:

- Set specific times to toggle flags or rules on/off, ensuring precise control
- Schedule traffic allocation percentages for smooth experiment rollouts
- Increase test velocity and confidence by automating progressive changes

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Screenshot of Feature Flag Setup. Here users can run flexible A/B and multi-armed bandit tests, as well as: - Set up a single feature flag to test multiple variations and experiment types - Enable targeted deliveries and rollouts for more precise experimentation - Roll back changes quickly when needed to ensure experiment accuracy and reduce risks - Increase testing flexibility with control over experiment types and delivery methods

Technical Details

Technical Details
Deployment Types	SaaS
Mobile Application	No
Supported Countries	All

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What is Optimizely Feature Experimentation?

Optimizely Feature Experimentation unites feature flagging, A/B testing, and built-in collaboration—so marketers can release, experiment, and optimize with confidence in one platform.

What are Optimizely Feature Experimentation's top competitors?

VWO, AB Tasty, and LaunchDarkly are common alternatives for Optimizely Feature Experimentation.

Optimizely Feature Experimentation

What is Optimizely Feature Experimentation?

Categories & Use Cases

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