Frequently Asked Questions

A curated summary of the top questions asked on our Slack community, often relating to implementation, functionality, and building better products generally.
Statsig FAQs
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GENERAL FEATURE FLAGS

How to send heads-up emails to users before exposing them to a new feature?

In order to notify users before they are exposed to a new feature, you can create a separate feature gate to control the rollout percentage and a segment to contain the account IDs that can be exposed to the feature. This approach allows you to effectively manage the rollout process and ensure that users are notified in advance.

The main feature gate will only pass for the accounts in the segment that also pass the separate feature gate. This provides a clear distinction between the users who are eligible and those who have been exposed to the feature.

Here's a brief overview of the process:

1. Create a main feature gate (rollout_feature_gate). The users that pass this gate will be exposed to the feature.

2. Create a separate feature gate (exposure_eligibility_gate) to control the rollout percentage. The users that pass this gate are the ones eligible to be exposed to the feature.

3. Create a segment (allegeable_accounts) that contains all the account IDs that can be exposed to the feature.

The rollout_feature_gate will return pass only for the accounts in the allegeable_accounts segment, while questioning the exposure_eligibility_gate. After a certain amount of time, export all account IDs in the exposure_eligibility_gate to the allegeable_accounts segments, and increase the exposure_eligibility_gate percentage.

This approach allows you to have a distinction between the eligible users that were exposed to the feature (allegeable_accounts segment) to the ones that are allegeable but potentially not yet exposed to it (exposure_eligibility_gate). You can manage additional rules and environment conditions in the main feature gate (rollout_feature_gate).

Remember to test your setup thoroughly in a pre-production environment before rolling it out to ensure everything works as expected.

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At OpenAI, we want to iterate as fast as possible. Statsig enables us to grow, scale, and learn efficiently. Integrating experimentation with product analytics and feature flagging has been crucial for quickly understanding and addressing our users' top priorities.
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