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 can I filter out users who trigger events unusually often?

Date of slack thread: 6/11/24

Anonymous: I see some users who trigger some event unusually often. Is there a way to filter users like this out on a regular basis

Timothy Chan (Statsig): Hi Nadira, can you provide an example? We are working on automated bot filtering. There are also ways to create segments of users which can be excluded from experiments, but we do not support this for metrics at the moment.

Anonymous: <@U01RGJZBTLL> In which format should I send you an example

Timothy Chan (Statsig): I’d like to learn at a high level what you are trying to accomplish. I’m familiar with the bot example which can rack up some common events. Do you have a different use case for filtering users, and where do you expect the filter to be applied?

Anonymous: I have a user who triggered event A 200000 times an hour in Statsig. For this users I see only 200 events that hour in my backend log. I suppose that he is a bot who has a script that emulates button presses. I would like him not to appear in statsig at all because my company pays per event count

Timothy Chan (Statsig): Can you send me the user_id, event, and the date you observe this? I’d like to look closer and see whether our bot detection would work here.

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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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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.
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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.
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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
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