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Optimizely Feature Experimentation

Optimizely Feature Experimentation

Overview

What is Optimizely Feature Experimentation?

Optimizely Feature Experimentation combines experimentation, feature flagging and built for purpose collaboration features into one platform.

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Recent Reviews

Optimizely Review

7 out of 10
November 27, 2023
Incentivized
We use Optimizely Feature Experimentation to test and optimize user experiences on our digital platforms. It addresses issues like user …
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Awesomeness

10 out of 10
October 12, 2023
Incentivized
Create meaningful experiments and personalization that allow us to have an impact on the business and customers. We try to create …
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Awards

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Reviewer Pros & Cons

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Pricing

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What is Optimizely Feature Experimentation?

Optimizely Feature Experimentation combines experimentation, feature flagging and built for purpose collaboration features into one platform.

Entry-level set up fee?

  • Setup fee required
For the latest information on pricing, visithttps://www.optimizely.com/plans/

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting/Integration Services

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Product Demos

Jewel - Feature Experimentation

Navattic
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Product Details

What is Optimizely Feature Experimentation?

Optimizely Feature Experimentation combines experimentation and feature flagging into one platform with the addition of integrated, built for purpose collaboration tools. It is used to optimize user experiences across digital channels and make every release high quality by quickly and safely validating code and features with real users through the entire software development cycle.

Optimizely Feature Experimentation Features

  • Supported: Testing & Experimentation
  • Supported: Feature Flags
  • Supported: Collaboration

Optimizely Feature Experimentation Screenshots

Screenshot of AI Variable suggestions: AI helps to develop higher quality experiments. Optimizely’s Opal suggests content variations in experiments, and helps to increase test velocity  and improve experiment qualityScreenshot of Integrations: display of the available integrations in-app.Screenshot of Reporting used to share insights, quantify experimentation program performance using KPIs like velocity and conclusive rate across experimentation projects, and to drill down into the charts and figures to see an aggregate list of experiments. Results can be exported into a CSV or Excel file, and KPIs can be segmented using project filters, experiment type filters, and date rangesScreenshot of Collaboration: Centralizes tracking tasks in the design, build, and launch of an experiment to ensure experiments are launched on time . Includes calendar, timeline, and board views in customizable views that can be saved to share with other stakeholdersScreenshot of Scheduling: Users can schedule a Flag or Rule to toggle on/off,  traffic allocation percentages, and achieve faster experimentation velocity and smoother progressive rolloutsScreenshot of Metrics filtering: Dynamic event properties to filter through events. Dynamic events provide better insights for experimenters who can explore metrics in depth for more impactful decisions

Optimizely Feature Experimentation Video

Optimizely Feature Experimentation Competitors

Optimizely Feature Experimentation Technical Details

Deployment TypesSoftware as a Service (SaaS), Cloud, or Web-Based
Operating SystemsUnspecified
Mobile ApplicationNo
Supported CountriesAll

Frequently Asked Questions

Optimizely Feature Experimentation combines experimentation, feature flagging and built for purpose collaboration features into one platform.

LaunchDarkly, AB Tasty, and VWO are common alternatives for Optimizely Feature Experimentation.

The most common users of Optimizely Feature Experimentation are from Mid-sized Companies (51-1,000 employees).
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Comparisons

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Reviews and Ratings

(34)

Attribute Ratings

Reviews

(1-7 of 7)
Companies can't remove reviews or game the system. Here's why
Dimos Papadopoulos | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Incentivized
We are using Optimizely Feature Experimentation (FE) across all of our live eCommerce websites and we are actively using it across core experiences to test, analyse, experiment as well as activate features and control them on the spot through the various entities. The scope spans across multiple markets (UK/US) and we are aiming to always experiment major changes or go live in small alpha and beta groups before hitting the whole userbase with new features.
  • AB experiments
  • Multivariant experimentation
  • Traffic split
  • Applying filters and exclusions
  • Connected end2end and analyzing metrics
  • Implementing new features
  • Splitting feature flags from actual experiments is slightly clunky and can be done either as part of the same page or better still you can create a flag on the spot while starting an experiment and not always needing to start with a flag.
  • Recommending metrics to track based on description using AI
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.
Score 9 out of 10
Vetted Review
Verified User
Incentivized
We use Optimizely Feature Experimentation for managing our releases, A/B testing of new features, and measuring the impact of our releases. It's used by our entire product organization for feature flagging too.
  • Integrated roll-out and A/B testing ability
  • Ability to personalize roll-outs based on other user traits
  • Measure the impact of releases
  • Better integration with web experimentation product
  • Integrate with Segment and other data sources for easier feature release measurement
  • More granular audience building / available traits
Well suited to teams who are shipping fast but wish to control roll outs to certain audiences, or to test the impact of releases for safety.
Ray Law | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Incentivized
It's a great tool to introduce new features and conduct experiments. We used it to try out new beta capabilities as experimentation. The scope includes all US and Canada users who make mobile orders.
  • A/B testing.
  • Percentage of traffic.
  • Target audience.
  • SDK of API calls.
  • Provide command line interface.
  • Simplify the user interface.
It's a great to test the customer behavior on new user interfaces and beta features. For example, before we invest a lot of money to develop a new capability or feature of the application, we want to create a quick proof-of-concept application. After the experiment, the business can a go or no-go decision.
November 27, 2023

Optimizely Review

Score 7 out of 10
Vetted Review
Verified User
Incentivized
We use Optimizely Feature Experimentation to test and optimize user experiences on our digital platforms. It addresses issues like user engagement and conversion rates. By A/B testing different features, we gain insights into user preferences, enabling us to make data-driven decisions to enhance our website's performance.
  • a/b testing
  • multivariate testing
  • Feature Flagging
  • Learning Curve
  • Limited Customization in Reporting
As a digital marketing manager, I've found Optimizely extremely effective for refining web interfaces, testing marketing messages, and optimizing user journeys on large e-commerce platforms. Its strengths shine in environments with high user traffic. However, for smaller websites or those with limited traffic, it's less appropriate, as substantial user data is essential to achieve statistically reliable results and insights. This tool is best leveraged in scenarios where user engagement can be measured at scale.
Score 3 out of 10
Vetted Review
Verified User
Incentivized
We use Optimizely Feature Experimentation for its feature flag features, which lets my team and I develop features of our product privately behind a gate before rolling it out to steadily larger internal and then external audiences. We then use the feature to fully release to the full user base.
  • Fine grained controls of target audiences
  • Offers an API with support for multiple languages
  • Extremely confusing, complicated, and unintuitive webapp. It's hard to figure out if a feature flag is active and what it will evaluate to for a given user, organization, or audience. The app has many different toggles for enabling, disabling, and targeting a flag, and they don't follow a consistent design.
  • Slow and buggy login process
  • Difficult to use human-readable aliases for user IDs and organization IDs when defining audiences. We maintain spreadsheets to understand our Optimizely configurations
Optimizely Feature Experimentation has all the features required to create feature flags and use them to control the release of software and to run hold backs. It's technically a good fit for these development and rollout use cases and helps avoid complicated version control alternative solutions. However, Optimizely Feature Experimentation is not easy or intuitive to use in my opinion. This is annoying from a usability and learning curve perspective. Worse, the unintuitive webapp can introduce risk where users may think they've toggled a feature flag for a target audience but instead have applied a different configuration (or don't have the flag active at all).
Score 8 out of 10
Vetted Review
Verified User
Incentivized
We're an online store dealing with a market that evolves quickly. Optimizely Feature Experimentation assists us in meeting the vital demand of refining our site to improve purchases. We apply it all over our internet site, assessing and perfecting new characteristics and experiences and examining various features, designs, and content from the start page to the payment page. It helps us solve important business issues, such as improving user experiences and increasing conversion rates.
  • 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.
  • Anyone new to A/B testing may struggle with the learning curve.
  • To give product teams more control over feature releases, we should improve how we target and segment audiences.
  • We've had some problems with the user interface, especially when setting up experiments.
Optimizely is a useful tool for businesses looking to improve specific aspects of their website, such as the checkout process, product page layout, or homepage design. Although it may not be practical for small websites or businesses with limited online operations, Optimizely's Feature Experimentation is crucial for smooth feature rollouts and successful A/B testing. It significantly improves the online customer experience and optimizes conversion funnels. However, companies that do not heavily rely on ongoing feature deployment may not require all of Optimizely's functions.
Score 9 out of 10
Vetted Review
Verified User
Incentivized
Currently being used across the entire organization, and helps address the issue of testing and validating UX changes to our website.
  • Allow for website changes otherwise dependent on code
  • Analytics of those change
  • Customer support
  • Ease of use
  • Initially training of program
  • Ability to customize certain aspects of the website were limited
  • Expensive for smaller organizations
When testing if a different color button, or different size, leads to a higher conversion rate.

Improvement could be when trying to do a more complicated test as add a rotating carousel functionality, which I don’t believe is currently supported.
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