Amplitude Analytics vs. Optimizely Feature Experimentation

Amplitude Analytics is well suited for scenarios where you have multiple data points to understand customer behavior and journeys and utilize simple to medium complexity graphs/charts. It may not be suitable for scenarios where you need to slice and dice data into highly customizable dashboards as that requires significant effort from technical teams.

Incentivized

I would use Optimizely Feature Experimentation when we would like to run basic experiments where metrics to be tracked are impressions, revenue or clicks. However, most of our experiments are tracking more complex metrics and this functionality is not enough. We still need to do work to analyse the data in our end.

Incentivized

• Feature analytics - measuring frequency of usage
• Time taken by users to convert in funnels
• Actions that drive certain user behaviors in funnels via correlation
• Actions that lead to user drop off in funnels via correlation
• Comparing behavior of two custom user cohorts

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

• Some more clarity and examples of implementation with GDPR in mind
• Some segregation inside user properties can be difficult to implement
• Splicing information inside funnels could be more intuitive
• User support for the cheaper tiers is hard to access
• Pricing transparency really needs to be improved

• 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

I haven't used the Amplitude support other than their training docs so I can't speak too much to the in-person support but the docs are serviceable. Nothing too crazy but between the user tips, email notifications, and the decent number of docs I was able to get the support I needed to ramp up on the tool.

Incentivized

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

Incentivized

It's the best in class with all the bells and whistles. Other options could suit you just fine at a lower price point, but you need to be sure of what you are not getting and the switching cost associated with when you do need 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

• It collects data of great importance which allows to make product improvements.
• It easily identifies problems a user may have with the product.
• The flow of users who have used the product has improved, thus being able to generate more income thanks to the improvements and new functions.

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