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

Understanding ingestion status and archiving metrics after switching from event to metric import

When transitioning from event import to metric import, it is essential to understand the ingestion status and the steps required to archive metrics that are no longer needed. After switching to metric import, if you have precomputed metrics, ingesting these metrics directly can be more efficient than importing raw events.

Once the change is made, there may be a need for adjustments on the backend to accommodate the new import method. This initial setup is a one-time speedbump, and once resolved, metrics should start appearing as expected. If you notice that some metrics have complete data for a specific date range while others do not, it may be due to the ingestion process still being underway or a need for a backfill to import historical data.

For metrics generated from the previous event ingestion job, such as event_count and event_dau types, consider archiving them if they are no longer relevant to your current data structure and needs. Archiving these metrics can help maintain a clean and efficient data environment.

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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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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.
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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.
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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.
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