Optimizely Feature Experimentation vs. Split

Optimizely Feature Experimentation works really well for setting up feature flags with an easy UI for turning them on and off or ramping up a gradual rollout. It also works really well to set up split tests where you can split your traffic by percentage as well as almost any custom data attribute you wish to define. This is more for robust features and less for visual changes - Optimzely Edge or Web are better suited for that.

Incentivized

• Its ability to run A/B tests and multivariate experiments simultaneously allows us to identify the best-performing options quickly.
• Optimizely blends into our analytics tools, giving us immediate feedback on how our experiments are performing. This tool helps us avoid interruptions. With this pairing, we can arrive at informed decisions quickly.
• Additionally, feature toggles enable us to introduce new features or modifications to specific user groups, guaranteeing a smooth and controlled user experience. This tool helps us avoid interruptions.

Incentivized

• Splitting feature flags from actual experiments is slightly clunky and can be done either as part of the same page or better still you can create a flag on the spot while starting an experiment and not always needing to start with a flag.
• Recommending metrics to track based on description using AI

Incentivized

All features that we used were pretty clear. They have a good documentation

Incentivized

It’s straightforward. Docs are well written and I believe there must be a support. But we haven’t used it

Incentivized

Optimizely Feature Experimentation is better for building more complex experiments than Optimizely Web. However, Optimizely Web is much easier to kickstart your experimentation program with as the learning curve is much lower, and dedicated developer resources are not always necessary (marketers can build experiments quickly with Optimizely Web without developers' help).

Incentivized

had troubles with performance for SSR and the React SDK

Incentivized

• Experimentation is key to figuring out the impact of changes made on-site.
• Experimentation is very helpful with pricing tests and other backend tests.
• Before running an experiment, many factors need to be evaluated, such as conflicting experiments, audience, user profile service, etc. This requires a considerable amount of time.

Incentivized