Customer Feedback

We love feedback from you on our products and the problems in your daily work that you would like us to solve. Please describe the challenge you're encountering and your desired outcome. Be as detailed as possible.

For technical issues or bugs please head to Support or our Developer Community. You can assign up to 20 votes in total. Thank you for your feedback.

Status explanation: 'Future Consideration' = Continuing to collect further feedback, not planned at this time. 'Investigating' = Prioritized for deeper customer and feasibility investigations ahead of planning development.

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Feature Experimentation

Showing 59 of 1433

Full Stack tests show as an ID number instead of Experiment Name.

Expected Experience Full stack tests should be reported using Flag Names and not flag id numbers on https://app.optimizely.com/v2/accountsettings/account/usage . Makes review difficult. Actual Experience (please describe what you see): For the acc...
over 1 year ago in Feature Experimentation / Project / Account Configuration / Settings 1 Future consideration

CMS + FX

We need an integration between FX and CMS similar to the one Contentful has but better.
about 2 months ago in Feature Experimentation / Experiment Authoring 0 Investigating

GCP Cloud Functions + Edge SDK

Since we are now on Google Cloud a customer has asked us about the possibility of Edge functions similar to AWS Lambda for GCP
6 months ago in Feature Experimentation / SDKs & Agent 0 Future consideration

Full Stack Experiment Scheduler

What is the problem you’re trying to solve with this request?Rather than providing a solution, please try to describe the problem in as much detail as possible. Calendar Experiment scheduling - on and off Repeatability Weekly, Monthly, Yearly, Cus...
about 2 years ago in Feature Experimentation / Feature Flagging Workflows 4 Already exists

When adding multiple audiences to a rule, don't default to matching ANY

When you add multiple audiences to a new rule, the default is to match ANY of the selected audiences. We always want to match ALL of the selected audiences, but our users often forget to change ANY to ALL. They put their experiment live and don't ...
over 1 year ago in Feature Experimentation 0 Future consideration

Bounce/exit metrics in Optimizely Full Stack

Bounce and exit rate are key metrics for our business, as they often reach statistical significance and are crucial for us as an indication of experiment performance. At present, these metrics are only available in the Optimizely Web product and n...
over 1 year ago in Feature Experimentation / Other 1 Future consideration

Use human language on key experiment changes

We want to quickly find the dates of an experiment starts, ends, ramp-up percentage changes, so that we can keep track of the changes made in the experiment. Currently in the history, only the code changes are listed and we have to read through th...
6 months ago in Feature Experimentation / Feature Flagging Workflows 0 Future consideration

Logger having error categorised into types

The Optimizely logger indicates different types of errors. Our client suggests to divide the different types of errors by type. Example use case: our client is currently using a Regex to identify the error messages received for the error "not in d...
12 months ago in Feature Experimentation / SDKs & Agent 1 Future consideration

Supporting Custom Javascript or some inbuilt functions in the Audience Builder

We would like to be able to perform some minimal data manipulation before matching when creating audiences. Either supporting custom javascript in Audience Builder in the Full Stack version, or accommodating common Javascript or optimizely custom ...
over 1 year ago in Feature Experimentation / SDKs & Agent 0 Future consideration

Feature Flag Lifecylcle Analytics

One of my goals as an administrator of feature flags is to understand the lifecycle of our feature flags. When it was created and the various data points for it's rollout. I struggle to maintain the lifespan of our flags without alot of manual work.
about 2 years ago in Feature Experimentation / Feature Flagging Workflows 1 Future consideration