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If you want to step up your experimentation program.
April 15, 2024

If you want to step up your experimentation program.

Anonymous | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User

Overall Satisfaction with Optimizely Feature Experimentation

We have a SPA, so it is more effective to use Optimizely Feature Experimentation for Experimentation, especially for complex experiments. Our developers also prefer the control and flexibility they have with Optimizely Feature Experimentation, which allows other teams to make controlled changes to the website easily and rapidly.
  • Allowing non-technical teams to be able to make controlled and speedy changes on the site.
  • Makes developers feel comfortable about experimentation.
  • Onboarding takes time because there is a steep learning curve.
  • The shift from the legacy version to the latest version required some changes in mindset.
  • The layouts of Optimizely Web and Optimizely Feature Experimentation are quite different, so it takes time to transfer from one to the other.
  • Improved our conversion rates.
  • Increased our speed to make changes and revert back if necessary.
  • Helped build Experimentation awareness across the business.
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).

Do you think Optimizely Feature Experimentation delivers good value for the price?

Yes

Are you happy with Optimizely Feature Experimentation's feature set?

Yes

Did Optimizely Feature Experimentation live up to sales and marketing promises?

Yes

Did implementation of Optimizely Feature Experimentation go as expected?

No

Would you buy Optimizely Feature Experimentation again?

Yes

Optimizely Feature Experimentation is good if you have a clear release process that incorporates It into your current product release cycle. However, It requires a lot of resources and time, especially at the start when teams are learning how to use and deploy it. Therefore, it may not be the best Experimentation to go for if you are starting in your Experimentation journey.