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

How to run an experiment with both anonymous and logged-in users using Segment and Statsig?

When running experiments with both anonymous and logged-in users using Segment and Statsig, it is crucial to have a common identifier to track user interactions accurately.

For anonymous users, you can either pass the stable ID generated by Statsig to Segment or generate a stable ID and pass it to Statsig. This ensures that there is a consistent identifier across both platforms.

For logged-in users, you can use the Segment userID, which should be included in the userObject during initialization. This setup allows Statsig to recognize both anonymous and logged-in users in the experiment. To facilitate this, you may need to provision a new custom ID, such as segmentAnonymousId, and map Segment's anonymousId to this new custom ID in the integration settings.

This approach ensures that exposure events sent via the Statsig SDK and custom events sent via Segment are associated with the same user, regardless of their logged-in status.

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OpenAI OpenAI
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Notion Notion
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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.
Dave Cummings
Engineering Manager, ChatGPT
Brex's mission is to help businesses move fast. Statsig is now helping our engineers move fast. It has been a game changer to automate the manual lift typical to running experiments and has helped product teams ship the right features to their users quickly.
Karandeep Anand
At Notion, we're continuously learning what our users value and want every team to run experiments to learn more. It’s also critical to maintain speed as a habit. Statsig's experimentation platform enables both this speed and learning for us.
Mengying Li
Data Science Manager
We evaluated Optimizely, LaunchDarkly, Split, and Eppo, but ultimately selected Statsig due to its comprehensive end-to-end integration. We wanted a complete solution rather than a partial one, including everything from the stats engine to data ingestion.
Don Browning
SVP, Data & Platform Engineering
We only had so many analysts. Statsig provided the necessary tools to remove the bottleneck. I know that we are able to impact our key business metrics in a positive way with Statsig. We are definitely heading in the right direction with Statsig.
Partha Sarathi
Director of Engineering
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