Advanced strategies
Mutual exclusion overview
Optimizely Web Experimentation Optimizely Personalization Optimizely Performance Edge Optimizely Feature Experimentation Optimizely Full Stack (Legacy) Mutual exclusion https //en. wikipedia.
Controlled traffic allocation ramping
Optimizely Web Experimentation Optimizely Performance Edge Optimizely Feature Experimentation Optimizely Full Stack (Legacy) Optimizely Personalization Ramping is when you gradually expose traffic to new test variations. The process can introduce inefficiency and risk if you ramp your traffic carelessly.
Simpson's Paradox: Discover possibilities with your segments, not shipping decisions
Optimizely Web Experimentation Optimizely Performance Edge Optimizely Feature Experimentation Optimizely Full Stack (Legacy) Changing the traffic distribution in your live experiment invites Simpsons paradox and ruins your experiment results. Simpson's Paradox is a statistical phenomenon where a trend observed in individual data groups vanishes or reverses when calculating the groups together.
History of how Optimizely Experimentation controls Simpson's Paradox in experiments with Stats Accelerator enabled
Optimizely Web Experimentation Optimizely Personalization Optimizely Feature Experimentation Optimizely Full Stack Experimentation (Legacy) In 2017, Optimizely announced the release of Stats Accelerator and multi-armed bandits https //www. optimizely.
History of Stats Accelerator and multi-armed bandits
Optimizely Web Experimentation Optimizely Performance Edge Optimizely Feature Experimentation Optimizely Full Stack Experimentation (Legacy) When you set up an A/B test using Optimizely Experimentation https //www. optimizely.
What to expect when you migrate from Optimizely Web Experimentation to Optimizely Feature Experimentation
Optimizely Web Experimentation Optimizely Feature Experimentation Choosing the right Optimizely Experimentation product requires knowing the differences between Optimizely Web Experimentation and Optimizely Feature Experimentation. Transitioning from Optimizely Web Experimentation to Optimizely Feature Experimentation can impact your company's experimentation program.
Stats accelerator overview
Optimizely Web Experimentation Optimizely Personalization Optimizely Feature Experimentation Optimizely Full Stack (Legacy) If you run a lot of experiments, you may face two challenges Data collection is costly, and time spent experimenting means you have less time to exploit the value of the eventual winner. Creating more than one or two variations can delay statistical significance longer than you might like.
Experimentation distribution methods
Optimizely Web Experimentation Optimizely Personalization Optimizely Feature Experimentation Optimizely Full Stack (Legacy) The intent of your test determines when you should run an optimization using Stats Accelerator, multi-armed bandit (MAB), or a contextual bandit distribution for the variations' traffic allocation. To discover which variation improves your product with statistical certainty – Run an experiment with Stats Accelerator .
Maximize lift with multi-armed bandit optimizations
Optimizely Web Experimentation Optimizely Personalization Optimizely Feature Experimentation Optimizely Full Stack (Legacy) Use multi-armed bandits (MABs) in Optimizely Experimentation to maximize traffic to your winning variations. MABs differ from A/B tests because they do not generate statistical significance and do not use a control or baseline experience.
Experiment end-to-end with Optimizely Web Experimentation and Optimizely Feature Experimentation
Optimizely Web Experimentation Optimizely Feature Experimentation Optimizely Web Experimentation and Optimizely Feature Experimentation share a common infrastructure and certain resources, such as Stats Engine https //www. optimizely.