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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Optimizely Feature Experimentation

    Score8.2 out of 10
    N/AOptimizely Feature Experimentation unites feature flagging, A/B testing, and built-in collaboration—so marketers can release, experiment, and optimize with confidence in one platform.N/A
    Pricing
    Optimizely Feature Experimentation
    Editions & Modules
    No answers on this topic
    Offerings
    Pricing Offerings
    Optimizely Feature Experimentation
    Free Trial
    No
    Free/Freemium Version
    Yes
    Premium Consulting/Integration Services
    Yes
    Entry-level Setup FeeRequired
    Additional Details—
    More Pricing Information
    Community Pulse
    Optimizely Feature Experimentation
    Considered Both Products
    Optimizely
    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 …
    Incentivized
    Chose Optimizely Feature Experimentation
    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.
    Incentivized
    Chose Optimizely Feature Experimentation
    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.
    Incentivized
    Chose 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
    Incentivized
    Chose Optimizely Feature Experimentation
    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 …
    Incentivized
    Chose Optimizely Feature Experimentation
    Because we were looking for a solution to a/b test.
    Incentivized
    Chose Optimizely Feature Experimentation
    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.
    Incentivized
    Chose Optimizely Feature Experimentation
    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.
    Incentivized
    Chose Optimizely Feature Experimentation
    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 …
    Incentivized
    Chose Optimizely Feature Experimentation
    not too much experience on that to answer this question
    Incentivized
    Chose Optimizely Feature Experimentation
    There is a lot more flexibility with Optimizely once you have customized the implementation and better tools.
    Incentivized
    Chose Optimizely Feature Experimentation
    WebX and FeatureX work well in pair, they organically complement each other
    Incentivized
    Chose Optimizely Feature Experimentation
    Simple interface and ability to create audiences and assign them to experiments.
    Incentivized
    Chose Optimizely Feature Experimentation
    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.
    Incentivized
    Chose Optimizely Feature Experimentation
    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.
    Incentivized
    Chose Optimizely Feature Experimentation
    With the Netspring acquisition I think it's closer to its competitor's features
    Incentivized
    Chose Optimizely Feature Experimentation
    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.
    Incentivized
    Chose Optimizely Feature Experimentation
    Because allows us to do test and Learn from
    Incentivized
    Chose Optimizely Feature Experimentation
    we wanted a shift with the tool that helps us with managing our data
    Incentivized
    Chose Optimizely Feature Experimentation
    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 …
    Incentivized
    Chose 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.
    Incentivized
    Chose Optimizely Feature Experimentation
    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 …
    Incentivized
    Chose Optimizely Feature Experimentation
    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.
    Incentivized
    Chose Optimizely Feature Experimentation
    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 …
    Incentivized
    Key User Insights
    Would buy again
    89%
    Would buy again
    41 Answers
    Delivers good value for the price
    97%
    Delivers good value for the price
    33 Answers
    Happy with the feature set
    91%
    Happy with the feature set
    42 Answers
    Lived up to sales and marketing promises
    88%
    Lived up to sales and marketing promises
    21 Answers
    Implementation went as expected
    80%
    Implementation went as expected
    28 Answers
    User Ratings
    Optimizely Feature Experimentation
    Likelihood to Recommend
    8.2
    (47 ratings)
    Likelihood to Renew
    4.5
    (2 ratings)
    Usability
    7.6
    (26 ratings)
    Support Rating
    3.6
    (1 ratings)
    Implementation Rating
    10.0
    (1 ratings)
    Product Scalability
    5.0
    (1 ratings)
    User Testimonials
    Optimizely Feature Experimentation
    Likelihood to Recommend
    Optimizely
    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 -
    Incentivized
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    Pros
    Optimizely
    • 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.
    Incentivized
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    Cons
    Optimizely
    • 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
    Incentivized
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    Likelihood to Renew
    Optimizely
    Competitive landscape
    Incentivized
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    Usability
    Optimizely
    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
    Incentivized
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    Support Rating
    Optimizely
    Support was there but it was pretty slow at most times. Only after escalation was support really given to our teams
    Incentivized
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    Implementation Rating
    Optimizely
    It’s straightforward. Docs are well written and I believe there must be a support. But we haven’t used it
    Incentivized
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    Alternatives Considered
    Optimizely
    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
    Incentivized
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    Scalability
    Optimizely
    had troubles with performance for SSR and the React SDK
    Incentivized
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    Return on Investment
    Optimizely
    • 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.
    Incentivized
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    ScreenShots

    Optimizely Feature Experimentation Screenshots

    Screenshot of Feature Flag Setup. Here users can run A/B and multi-armed bandit tests, as well as:

- Set up a single feature flag to test multiple variations and experiment types
- Enable targeted deliveries and rollouts for more precise experimentation
- Roll back changes quickly when needed to ensure experiment accuracy and reduce risks
- Increase testing flexibility with control over experiment types and delivery methodsScreenshot of Audience Setup. This is used to target specific user segments for personalized experiments, and:

- Create and customize audiences based on user attributes
- Refine audience segments to ensure the right users are included in tests
- Enhance experiment relevance by setting specific conditions for user groupsScreenshot of Experiment Results, supporting the analysis and optimization of experimentation outcomes. Viewers can also:

- examine detailed experiment results, including key metrics like conversion rates and statistical significance
- Compare variations side-by-side to identify winning treatments
- Use advanced filters to segment and drill down into specific audience or test dataScreenshot of A Program Overview. These offer insights into any experimentation program’s performance. It also offers:

- A comprehensive view of the entire experimentation program’s status and progress
- Monitoring for key performance metrics like test velocity, success rates, and overall impact
- Evaluation of the impact of experiments with easy-to-read visualizations and reporting tools
- Performance tracking of experiments over time to guide decision-making and optimize strategiesScreenshot of AI Variable Suggestions. These enhance experimentation with AI-driven insights, and can also help with:

- Generating multiple content variations with AI to speed up experiment design
- Improving test quality with content suggestions
- Increasing experimentation velocity and achieving better outcomes with AI-powered optimizationScreenshot of Schedule Changes, to streamline experimentation. Users can also:

- Set specific times to toggle flags or rules on/off, ensuring precise control
- Schedule traffic allocation percentages for smooth experiment rollouts
- Increase test velocity and confidence by automating progressive changes