Maze vs. Optimizely Feature Experimentation

Maze User Testing is great if you're interested in doing user research from the comfort of your own desk. You can easily setup usability tests, surveys, card sorting and tree tests among other things to get a better understanding of how customers use your product. The only limitation at the moment with Maze that I can identify is only being able to do unmoderated tests, so if you'd like to be able to ask follow up questions in the moment, Maze is not the tool for you.

Incentivized

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

• Reporting is top-tier with filtration, heatmaps, user data, and public URLs for stakeholders
• Figma integration with user testing software is about as fast as it gets
• The experience for testers is practically seamless going from our site to a Maze. Loads of completed Mazes.

• 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

• Change/Audit log to understand who is doing what and when
• Some simpler templates for simpler situations
• Additional means to take data out into 3rd party products for advanced analytics

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

Any issues that presented themselves were dealt with in a quick and efficient manner and fully rectified by the knowledgeable team over at Maze.

Incentivized

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

Incentivized

A Lookback is an alternative option if you think Maze User Testing is quite expensive for you, but look back has a different approach to Maze User Testing. Lookback focuses on qualitative usability testing instead of quantitative UserTesting. And also, Maze User Testing has a free option but Lookback doesn't have it, but Lookback has a cheaper option at $19/month than Maze.

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

• Easy to run quant test
• Easy to test with large number of people on production
• Easy to run unmoderated competitor studies

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