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GENERAL

Do User Attributes in Statsig Retain History for Metrics Filtering?

Date of slack thread: 8/5/24

Anonymous: Do user attributes have history? Example: If a user has an attribute like enterprise_tier: true and triggers an event added_credit_card but then later the user attribute switches to enterprise_tier: false, will metrics that use the added_credit_card event and are filtering on the enterprise_tier user attribute use the current value of enterprise_tier or the value when the event was triggered?

Statbot: User properties in Statsig are frozen when a user is first exposed to a feature gate or experiment. This means that if a user attribute like enterprise_tier changes after an event is triggered, the metrics filtering on the enterprise_tier attribute will use the value of enterprise_tier at the time the event was triggered, not the current value. For example, if a user triggers the added_credit_card event when enterprise_tier is true, and later the enterprise_tier changes to false, metrics filtering on the enterprise_tier attribute will still consider the enterprise_tier as true for that event. This behavior ensures consistency in your metrics and analysis. For more details, you can refer to the documentation on user properties: source.

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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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President
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.
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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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Partha Sarathi
Director of Engineering
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