How we use Optimizely Feature Experimentation in Healthtech
Updated November 13, 2025
How we use Optimizely Feature Experimentation in Healthtech
Score 7 out of 10
Vetted Review
Verified User
Overall Satisfaction with Optimizely Feature Experimentation
The Optimizely feature experimentation is our backbone for how we release and validate new product capabilities. It helps us to derisk client facing changes. In our space, a poorly designed flow or misstep in a clinical tool can erode trust quickly so we always need to be certain before scaling any feature.
Pros
- Feature flagging combined with metrics tracking.
Cons
- I had to build custom connectors so that experimentation results flowed into our analytics stacks automatically. That's because of its poor integration with our legacy record systems
- avaerage feature rollout time went down by a third
- less engineering waste from killing weak features early
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?
No
Did implementation of Optimizely Feature Experimentation go as expected?
No
Would you buy Optimizely Feature Experimentation again?
Yes
Using Optimizely Feature Experimentation
| Pros | Cons |
|---|---|
Consistent Convenient | Difficult to use Requires technical support Not well integrated Slow to learn |
- rolling out features and controlled experiments. It saves us weeks of dev sprints
- You can tie business KPIs directly to experiment variations
- any type of analysis that goes beyond the surface level metrics is anything but intuitive
- integrating it with most of our health-tech custom systems
Yes - I'd say well enough. We plug it into android and ios builds gradually to roll out updates in pilot sites
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