Optimizely Feature Experimentation Reviews and Ratings

Use Cases and Deployment Scope

Our team uses it as a core part of our release and Validation process across client projects, not just internally. Currently, we are working with a whale client, a bank, and we've leveraged feature flags to test different credit card recommendation engines in prod without risking the live customer base. That has solved a longstanding problem of all-or-nothing releases, where any new algorithm introduced carried a huge rollback risk.

Likelihood to Recommend

While Optimizely has genuinely elevated how we test and validate product ideas, it is without its pain points. The positives are clear, and I've alluded to them already, like clean experiment setups and reliable feature flags. But at the same time, scaling experiments across microservices and stitching data with external systems have proven to be a huge challenge.

Use Cases and Deployment Scope

We use Optimizely Feature Experimentation to solve business problems with conversion funnels and conversion rate optimization. Our implementation is purely server side, so we use it mainly to experiment and then roll out features on the web and app. For us, it helps test ideas and hypotheses before risking it and rolling it out to a hundred percent.

Likelihood to Recommend

We use it well on the app because we can roll out features and turn them on and off quite easily because the app or any native app is known for being quite fiddly and making changes can be long and hard. But with Optimizely we can roll out features and change its allocation from 10 to 20 to 30% all remotely, which is a great benefit for us.

Use Cases and Deployment Scope

We utilize feature experimentation for our web experiments and our app experiments. Currently we have iOS and Android on there, and whenever we make any copy changes or new product features are rolled out, then we use it for our experimentation it addresses. It addresses the fact that we have this usually quite company wide and so having other users and other stakeholders that can access the charts to see the primary metrics, how they're performing secondary metrics is really helpful. And we also use the multi-arm bandit, for instance, whenever we're doing tester and peak periods like Mother's Day because we're a flower company. It addresses quite a few problems in that sense. It allows everyone to be unified with the experiments.

Likelihood to Recommend

I think it's good. Like I said, I think it's very good at unifying teams and everybody being on the same page. I think it's also, I love just seeing the charts and being able to see how a metric is performing throughout, so that's just super helpful for us, especially when we were trying to make quick decisions. Sometimes for some markets it's very difficult to reach significance or depending on the type of feature that you're rolling out or trying to experiment, it's quite difficult and so you want to see how is the metrics performing over time, so it's really helpful

Use Cases and Deployment Scope

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.

Likelihood to Recommend

It is really well suited when we want to test client facing features in a controlled safe way.

Where it feels less appropriate is in experiments that require complex multi system coordination

Use Cases and Deployment Scope

We primarily use this tool to run AB tests on our website, answer our own hypotheses about what messaging and elements will drive growth and speak to the right audience, and measure those tests as well.

Likelihood to Recommend

It’s definitely well suited to a high-volume SaaS product that serves consumers or prosumers. I don’t recommend it for smaller-volume tests or upmarket B2B because test results are too expensive to make a difference.

Use Cases and Deployment Scope

We are constantly running tests with Optimizely Feature Experimentation, usually 2-3 at a time. For Optimizely Feature Experimentation, we are testing customer journeys, customization of our product cards, CTAs and mobile at any particular time. Most useful have been basic UX tests, such as the inclusion of CTAs or 'add to cart' type buttons in different areas.

Lately, we've been running shipping/delivery messaging on the product page.

Likelihood to Recommend

Our Optimizely Feature Experimentation setup has helped us test product card display across multiple areas of the site all at once, including on homepage, product page, product recommendation carousels and the shopping cart. Because Optimizely Feature Experimentation works across different areas of the codebase from the same flag, we're able to track and manage the entire experience from one place.

Use Cases and Deployment Scope

I used Optimizely to run A/B experiments on a web platform with a consumers to validate the usage of new features. The business value is that it helps us understand the product/market fit of a feature before we make it permanent for all customers. And with Optimizely we could easily feature flag the experience to groups of users and control when to turn on or turn off the experiment.

Likelihood to Recommend

I believe Optimizely is valuable for two scenarios

1) optimizations of a user experience to find opportunities for quick wins (Web UI) - this is something all A/B tools can do and Optimizely seems to provide all the capabilities needed<div>2) testing new features before rollout to all audiences of development of a native app experience (SDK) - this is where I think Optimizely shines as this is harder to do with A/B testing tools.</div>

Use Cases and Deployment Scope

We use Optimizely Feature Experimentation to conduct controlled A/B and multivariate experiments across our web and backend platforms. The core business problem it addresses is the need for data-driven decision-making—enabling us to test hypotheses about feature changes, UX optimizations, and algorithmic updates before rolling them out broadly. This helps reduce the risk of deploying underperforming or harmful changes, and accelerates learning cycles across product, design, and engineering teams.

Likelihood to Recommend

Optimizely is leading the industry with experimentations. All my previous organizations were using Optimizely for feature releases. I would definitely recommend to colleague.

Use Cases and Deployment Scope

We use it for Product AB and MVT testing, to both assist us with splitting traffic between variants and also to provide standardised reporting and calling of results on those tests. The scope is any test within our Product that can be easily implemented within Optimizely 's interface, which typically applies to all UI/UX changes, but also some different algorithm or multichannel approaches

Likelihood to Recommend

Good for standard Product UI/UX type experiments, such as changing the positioning of buttons on a page or how presented to user. Useful as well for adding in additional features and seeing how these are engaged with. Less useful for very complex testing that requires custom bucketing from DS/ML type teams, which may be easier to do offline

Use Cases and Deployment Scope

The use case was for a client to who wanted data on which user submission got better results; one with all of the form fields on the page, or one that had only a couple and was more of a multi step form to fill out.

Likelihood to Recommend

If you really wanted to a/b test a lot of specific areas of the site. And had a developer that can help install it. Less useful when you want to do just a single simple test, as it’s a lot of work to do.