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To continue making our product better and easier to use for everyone, we have made a slight UI update to communicate better how Optimizely's Stats Engine handles outliers in your revenue metrics. There have not been any underlying changes in our Stats Engine or how Optimizely calculates results. This update is purely a phrasing enhancement to improve our user's understanding of what happens to their outlier values.
An outlier is an abnormally lower or higher value than other values in your results.
In Optimizely, a metric is a quantitative measurement of a visitor's action. Metrics are created out of events, which directly track actions like clicks, page views, form submissions, purchases, and scroll depth.
The total revenue metric tracks the total revenue generated from user interactions with your event. Revenue usually is not your primary metric because, unlike clicks or page views, revenue does not measure a specific, discrete action taken by your visitors. However, tracking revenue is a fantastic way to tie your optimization efforts to the metrics that your company values most.
We are only updating the Optimizely UI and documentation to read "Outlier Smoothing" instead of "Outlier Filtering." We believe this update to the phrasing will help users understand what is happening with their outlier values.
Outliers can severely skew the accuracy of any analysis conducted on a data set and can lead to potentially incorrect conclusions. Smoothing outliers results in more accurate findings. We recommend turning on this setting for your revenue metric to improve the integrity of your results.
For revenue metrics, Optimizely gives you the option to use outlier smoothing. Optimizely's outlier smoothing algorithm first identifies any values exceeding the daily exclusion threshold, extreme values three standard deviations higher than the observed mean. These extreme values are designated as outliers. Next, Optimizely replaces these outliers with the metric's arithmetic mean value. This step in the process is known as outlier smoothing. Optimizely recalculates the daily exclusion threshold for each day using a moving average of your metric's arithmetic mean and standard deviation over the previous seven (7) days. This process repeats each day of the experiment. Please view our documentation on how Optimizely handles outliers for more detailed information.
If you have any questions or feedback, feel free to email us at support@optimizely.com! Keep on optimizing!
Here at Optimizely, we have been working hard to start 2022 with significant upgrades to our QA experience and security. We are proud to announce the release of new API methods for our Full Stack SDKs and some critical security upgrades for Python. Thank you for reading this month’s release notes, and we are excited to see all the new places and faces 2022 will bring us!
We’ve recently rolled out a set of new APIs for our Full Stack SDKs that will make overriding and managing user-level flags, experiments, and delivery rules even more straightforward.
These new methods extend our OptimizelyUserContext object, which previously allowed you to make flag decisions and flag events for a specific user. Now we’ve taken things up a notch!
The new methods allow you to do the following:
Using these “Forced Decision” methods makes it even easier to set up automated testing and QA by forcing certain User IDs into specific variations regardless of audience conditions and previously configured traffic allocations.
For more detailed information, please click on the SDK you are interested in to view the developer documentation on the Forced Decision methods:
As part of Optimizely’s regular security updates and modernization of the Full Stack product, we will no longer officially support older versions of Python. These older versions do not provide secure libraries needed for the Optimizely Python SDK. For example, Python version 3.4 support has ended due to a known security vulnerability in the PyYAML library.
Optimizely supports the following versions:
Last month’s release notes announced that we are working on exciting UI changes to our developer and end-user documentation. If you visited our docs recently, you might have noticed some updates, including a completely new UI redesign. We’re happy to report that these changes are going well and will be fully released soon.
If you have any questions or feedback, feel free to email us at support@optimizely.com! Keep on optimizing!
Quality assurance just got easier for Full Stack – Flag’s customers. Optimizely is excited to announce that Allowlisting is now available to all customers. Allowlisting, previously known as whitelisting, was available in the previous version of Full Stack, so we’re excited to continue putting our developers first by fully releasing Allowlisting in our Flags experience.
Allowlisting enables you to force certain users into a specific variation of an experiment. This capability can be beneficial during the QA process of development. You can view updated developer documentation for additional helpful QA scenarios and steps to enable Allowlisting in your Flag Rules.
While viewing our docs, you may have noticed some exciting UI changes! We’re working behind the scenes on some exciting updates on our developer and knowledge base documentation. As our Optimizely brand continues to grow, we’re excited to combine all of our documentation under one instance. These updates will help you find the information you need quicker and easier than ever! There will be some more exciting updates coming soon, so stay tuned!
If you have any questions or feedback, feel free to email us at support@optimizely.com! Keep on optimizing!
Last updated: Dec 13, 2021