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

How does Statsig bucket users in experiments and differentiate between new and returning users?

Date of slack thread: 8/9/24

Anonymous: When running experiments does StatSig bucket users only if they are a first-time visitor or do we bucket on ANY user ever? The reason I ask is that the effect of tests can be different depending on if someone is a returning user vs a new user. Essentially how are users defined?

Statbot: Statsig buckets users deterministically based on a unique identifier, such as userId or organizationId. This means that given the same user object and the same state of the experiment or gate, the user will always be assigned to the same bucket, regardless of whether they are a first-time visitor or a returning user. This ensures consistent and repeatable results across different sessions and devices [source]. If you need to differentiate between new and returning users, you might consider using different identifiers or custom properties to segment your users accordingly.

Statbot: I’d suggest reading through this to learn how bucketing works: https://docs.statsig.com/faq#how-does-bucketing-within-the-statsig-sdks-work. It’s a deterministic hash based on the input, and does not account for state like first-time visitor vs repeat user. If you have a flag for that, you can set it as a custom field to target separately.

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Why the best build with us

OpenAI OpenAI
Brex Brex
Notion Notion
SoundCloud SoundCloud
Ancestry Ancestry
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.
OpenAI
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.
Brex
Karandeep Anand
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.
Notion
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.
SoundCloud
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.
Ancestry
Partha Sarathi
Director of Engineering
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