Feature Experimentation

Optimize the performance of your application

Feature Experimentation

In order to provide a fully customizable, proven digital experience for visitors, consider optimizing your applications. In other words, let’s not only test the website, let’s test everything! While Optimizely’s Web Experimentation product helps to test and optimize your web experiences, the Feature Experimentation product will help you go further by optimizing your development applications. Feature Experimentation (previously known as Full Stack) is a feature flagging and experimentation platform for websites, mobile apps, chatbots, APIs, smart devices, and anything else with a network connection. Some example applications include search results, promotions, payment options, recommendations, and much more.

New Feature Tiers

Rollout Plus

Feature Rollouts (deliver new features to users by controlling visibility)
Feature Flags (turn certain functionality on and off without code)
Stats Engine (statistical framework for A/B testing)

Accelerate

+ Multiple Concurrent Experiments
Stats Accelerator (reduces the time to statistical significance)
Jira Integration
SSO (single sign on)
Account 2-step Verification
Multi-account Login (login to multiple accounts simultaneously)
Cross-Project Events (track the same event across different projects)
Offline Events (programmatically send events to results using offline events endpoint)
Bot Filtering (removes bot traffic from your results to decrease traffic)

Scale

+ Multi-Armed Bandits (pushes traffic to whichever variations perform best)
Mutually Exclusive Experiments (remove noise and bias from your results for accurate inferences)
Custom Segmentation (to see how different groups of visitors behave)

Additional Add-on

Salesforce Optimizely Data & Attribution Add-on

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FAQs

What are the skillset requirements?

Check out the skillsets page for a full list of skills needed to implement Optimizely’s Feature Experimentation.

What is the size of the datafile?

The empty datafile is around 0.5KB and grows with experiments and feature flags added. However, it is merely a JSON representation of the state of the project, and it does not grow significantly through that. Since this file will not be downloaded for every user, this should not impact performance significantly in most cases. Learn more on this topic here.

How long should I expect implementation to take?

This will depend upon the complexity of the stack and scope of experimentation within that stack. A simple implementation on an iOS or Android App with no integrations and basic event tracking can be done wtihin a day. An enterprise level implementation within a stack of multiple microservices, applications, and multiple clients will take longer and has a lot more factors to consider.

What happens if the Experimentation dashboard becomes unavailable?

From the perspective of Feature Experimentation, if the Experimentation dashboard becomes unavailable you will no longer be able to edit experiments or feature flags using that dashboard. Your experiments, feature flags, event tracking etc. will continue to function as they used to as the datafile is independent of our dashboard. However, you still can make changes via the API in that case.