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 73

Option to remove percentage roll out for simpler flag management

We are exploring using Optimizely to manage critically important feature flags. Sometimes, a flag needs to be turned off instantly, out of hours or by Ops personnel eg in the event of an outage of service. In these instances, we want increased con...
over 1 year ago in Feature Experimentation / Feature Flagging Workflows 0 Future consideration

Introduce distinct lexicon to distinguish between flag status and rule status (don't use on/off for both)

It is currently confusing to use the same terminology for both flags and the rules that control flags. For example, I can have a rule that is 'flag:off' but the rule in the list shows as 'on'. Suggestion would be to have different lexicon for rule...
over 1 year ago in Feature Experimentation / Feature Flagging Workflows 0 Future consideration

Type check for userId in Javascript (Browser/Node) SDKs

The customer (Yuliyan Yordanov from Tesco Stores) suggested a failsafe solution to reject empty string userId upon activating/enabling an experiment/feature: "A simple check for (typeof user_id ===’string’ && user_id.length ) would fix the...
over 1 year ago in Feature Experimentation / SDKs & Agent 1 Future consideration

Normalizing metrics

Groupon experimenters are looking for ways to normalize certain metrics. We need to have an ability to change the denominator for each metric. e.g Deal View CTR (deal view/total unique impressions) Session CVR (units/session) Deal Purchase CVR (Un...
over 1 year ago in Feature Experimentation / Other 0 Future consideration

JavaScript SDK handles large datafiles in a performant manner. It is possible to process large datafile initialisation on a web worker.

The javascript SDK client initialises the client form the datafile. When the datafile is large, processing the JSON takes a long time. P50 20ms and P75 already gives 70ms. This is significantly long when considering page load time. It would be ben...
over 1 year ago in Feature Experimentation / SDKs & Agent 1 Future consideration

Streaming Datafiles

Offer an option to consume datafiles immediately when updated via websocket or other streaming architecture. This would enable faster updates to flags and bring us into parity with competitors.
about 3 years ago in Feature Experimentation / SDKs & Agent 0 Future consideration

Roku SDK

No description provided
over 3 years ago in Feature Experimentation / SDKs & Agent 0 Future consideration

Manage Collaborators via API

Customers wants to manage collaborators via REST api, so that they can programmatically grant permissions via their existing permissioning tool that they've created. They do this because it's far more convenient for them to manage this in a centra...
over 3 years ago in Feature Experimentation 0 Future consideration

Event tags to segment results and create metrics- Segment integration

Customers wants to create Optimizely events via Segment. They do this because they already have evnts in Segment and then data is nicely aligned. They wish that Optimizely had the option to filter results by event tags and use the atgs to create m...
over 3 years ago in Feature Experimentation 0 Future consideration

Grant Editors to have access to configure roll out rules in Production, while restricting them from configuring experiments

The goal is for us to make sure only members from the Experimentation Operations function can configure launch experiments in production, but the Product team can control roll outs in production.
almost 2 years ago in Feature Experimentation / Experiment Authoring 0 Future consideration