Optimizely Feature Experimentation unites feature flagging, A/B testing, and built-in collaboration—so marketers can release, experiment, and optimize with confidence in one platform.
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Optimizely Feature Experimentation
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Optimizely Feature Experimentation
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Chose Optimizely Feature Experimentation
In previous companies I've used Monetate which is a similar A/B testing kind of feature experimentation engine that is very similar from my memory, but again, back to the point of these new features of the analytics engine and Opal, it kind of cuts it above Monetate from my …
I wasn’t part of the team that selected Optimizely, but its integrations with other tools were a big plus for us in making our decision. It was more expensive, however.
We have not used any other similar tools, we evaluated both Kameleoon and VWO. With the combination of price, features, and expandability, we moved forward with Optimizely Feature Experimentation.
Google Optimize is great that it is an add on to an existing Analytics implementation, but only has a web version. Optimizely has the SDK so better option for testing new features
We selected Optimizely as it was easy to use/understand, had clearly defined SLAs for keeping the platform up and was regarded as resilient within the industry. We needed something at our point in our experimentation journey that could be used for Product testing at scale and …
Optimizely Feature Experimentation has similar features to Amplitude. As a matter of fact it looks like Amplitude copied Optimizely. However, Amplitude did not mimic the nomenclature issues.
When Google Optimize goes off we searched for a tool where you can be sure to get a good GA4 implementation and easy to use for IT team and product team.
Optimizely Feature Experimentation seems to have a good balance between pricing and capabilities.
In other companies, all of the feature flag controls were done locally and it got messy after a while. There was no much control on who was doing what. With Optimizely Feature Experimentation, it is clear what feature flags are enabled and which ones are not. It is easier to …
Optimizely offered both web experimentation (WSYWIG editor for nontechnical marketing folks) and Feature Experimentation. That made the decision easier to get max value across different stakeholder groups.
Optimizely FX is the only tool I've used that specifically allows for testing in the back-end. Most front end tools are great for simple tests, but there comes a time when you need to go a level deeper and that's not possible with front-end tools.
I prefer Optimizely Feature Experimentation to web experimentation. I think it's more straightforward to set up and as an engineer, I like being able to have more control from the code side.
We haven't evaluated other products. We have an in-house product that is missing a lot of features and is very behind from making the test process easier.
Instead of evolving our in-house product with limited resources, we decided to go with Optimizely Feature Experimentation …
Overall, Optimizely Feature Experimentation is an industry leader in terms of experimentation across web and mobile. For apps I would say amplitude does slightly a better job as it is tailored to that niche.
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 …
Optimizely Feature Experimentation is less of a point solution than LaunchDarkly, so LD has a few extra features, but Optimizely offers a much greater solution for experimentation, personalization etc.
Feature experimentation is much more robust and allows more granular control over the decisions you want to make. While Optimizely Feature Experimentation is nice and can be delivered via Optimizely Feature Experimentation's UI, its still not ideal because its brittle and can …
Based on my experience with Optimizely Feature Experimentation, I can highlight several scenarios where it excels and a few where it may be less suitable. Well-suited scenarios: - Multi-Channel product launches - Complex A/B testing and feature flag management - Gradual rollout and risk mitigation Less suited scenarios: - Simple A/B tests (their Web Experimentation product is probably better for that) - Non-technical team usage -
It is easy to use any of our product owners, marketers, developers can set up experiments and roll them out with some developer support. So the key thing there is this front end UI easy to use and maybe this will come later, but the new features such as Opal and the analytics or database centric engine is something we're interested in as well.
Would be nice to able to switch variants between say an MVT to a 50:50 if one of the variants is not performing very well quickly and effectively so can still use the standardised report
Interface can feel very bare bones/not very many graphs or visuals, which other providers have to make it a bit more engaging
Doesn't show easily what each variant that is live looks like, so can be hard to remember what is actually being shown in each test
Easy to navigate the UI. Once you know how to use it, it is very easy to run experiments. And when the experiment is setup, the SDK code variables are generated and available for developers to use immediately so they can quickly build the experiment code
When Google Optimize goes off we searched for a tool where you can be sure to get a good GA4 implementation and easy to use for IT team and product team. Optimizely Feature Experimentation seems to have a good balance between pricing and capabilities. If you are searching for an experimentation tool and personalization all in one... then maybe these comparison change and Optimizely turns to expensive. In the same way... if you want a server side solution. For us, it will be a challenge in the following years
We have improved various metrics throughout the course of our experimentation program with Optimizely and therefore sharing numbers is tricky. Essentially we only implement versions of the product that perform the best in terms of CVR, revenue/visitor, ATV, average order value, average basket size and so forth dependent on the north star we are trying to move with each release.