Optimizely Feature Experimentation combines experimentation, feature flagging and built for purpose collaboration features into one platform.
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Optimizely Web Experimentation
Score 8.7 out of 10
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Optimizely Web Experimentation empowers teams to conduct experiments (without having to rely on developer resources) in order to test various user interactions, make website changes backed by data, and personalize customer experiences.
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 …
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 …
Before we chose Optimizely, we looked at other options like Google Optimize. However, we decided on Optimizely because it excels at A/B testing, even compared to other A/B testing tools that only have basic capabilities. Since we were working on a controlled release project, we …
Feature experimentation had some extra benefits, specially with segmentation, but pricing was prohibited. So we sticked with Optimizely Web Experimentation.
To my experience, Optimizely should be used across all core software releases in platforms/apps and products that are customer facing regardless whether b2c or b2b. Even in cases of internal platforms with thousands of users, testing features or pre-releasing and monitoring functionality is key. Good to note that it is less appropriate in cases where minor releases, backend software improvements are referred to.
Optimizely Web Experimentation is appropriate if you have a big budget and your company is mature enough in experimentation to take all the advantage of a such an expensive price model. If you are just starting into AB testing, or not enough budget, I do not recommend Optimizely Web Experimentation, as it becomes quite frustrating to manage usage limits and technical set up to avoid extra charges.
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.
Running Experiments Exclusively with user profile service. If one experiment is already allocated with the user profile, another experiment in the exclusion group should not trigger the experiment.
Ability to reevaluate audience condition with user profile service, in case audience condition do not match remove override from user profile override. Differentiate between qe override and user profile override.
Ability to connect multiple IDs to achieve consistent experimentation across devices.
Any way to achieve optimizely web capability using Optimizely Feature Experimentation.
Because it's an incredible and essential tool for my line of work as a conversion optimization specialist. Really couldn't do my job nearly as effectively without it. It's paid for itself many times over and I feel like I'm only beginning to unlock the tools potential.
Usability is mostly great. I like the WYSIWYG functionality and adding in real code is simple as well. It's easy to target specific pages or audiences. I've knocked a couple of points off because of how difficult it is to set up URL redirect experiments, confusion around creating pages, and lack of data that can be further analyzed.
I would rate Optimizely Web Experimentation's availability as a 10 out of 10. The software is reliable and does not experience any application errors or unplanned outages. Additionally, the customer service and technical support teams are always available to help with any issues or questions.
I would rate Optimizely Web Experimentation's performance as a 9 out of 10. Pages load quickly, reports are complete in a reasonable time frame, and the software does not slow down any other software or systems that it integrates with. Additionally, the customer service and technical support teams are always available to help with any issues or questions.
They always are quick to respond, and are so friendly and helpful. They always answer the phone right away. And [they are] always willing to not only help you with your problem, but if you need ideas they have suggestions as well.
The tool itself is not very difficult to use so training was not very useful in my opinion. It did not also account for success events more complex than a click (which my company being ecommerce is looking to examine more than a mere click).
In retrospect: - I think I should have stressed more demo's / workshopping with the Optimizely team at the start. I felt too confident during demo stages, and when came time to actually start, I was a bit lost. (The answer is likely I should have had them on-hand for our first install.. they offered but I thought I was OK.) - Really getting an understanding / asking them prior to install of how to make it really work for checkout pages / one that uses dynamic content or user interaction to determine what the UI does. Could have saved some time by addressing this at the beginning, as some things we needed to create on our site for Optimizely to "use" as a trigger for the variation test. - Having a number of planned/hoped-for tests already in-hand before working with Optimizely team. Sharing those thoughts with them would likely have started conversations on additional things we needed to do to make them work (rather than figuring that out during the actual builds). Since I had development time available, I could have added more things to the baseline installation since my developers were already "looking under the hood" of the site.
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).
Overall, the tools we compared against were great, but we went with Optimizely because it has all the features we needed and has the market leadership that gave us trust we would be successful in our experimentation efforts.
While granted, I am not the one making the changes to deploy the software; it seems basically as simple as placing the optimized snippet on your site. If you place it in the header, it should reach across your entire site easily. I do think the test setup can be a bit temperamental at times, and having too many cooks in the kitchen can lead to errors. So, I would caution how many people have the access needed to make actual changes.
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.
Customer retention: We've reduced subscription service client churn by 20%+ using optimized unsubscribe flows.
Risk mitigation: Testing into full site redesigns has saved clients millions of dollars.
Feature prioritization: Identifying what painted door changes add value has allowed developers to focus on changes that add hundreds of thousands or even millions to the bottom line.
