Statsig

We are hosting a unique summit in San Francisco on September 25th. You can find more details and register on our event page. Since it's reaching capacity, there will be a conditional entry: https://lnkd.in/dbtusnMA We have an incredible lineup of speakers for this summit, including - Ben Gilbert and David Rosenthal, the hosts of Acquired - Fidji Simo, CEO of Instacart, - Guillermo Rauch, CEO of Vercel, - Karri Saarinen, CEO of Linear, and - Mike Vernal, former Facebook executive and Sequoia partner, among others. The event is limited to 150 participants and lasts the entire day, offering ample opportunities for networking. The focus will be on the modern tech stack, with panels moderated by the hosts of Acquired, promising a schedule filled with valuable content and unique insights. I'll be there too and hope to see many of you in person!

Experimentation Week Day 5/5 🔥 Scaling experimentation is as much a people and culture challenge as it is a statistical one. So far this week, we’ve been announcing various advanced statistical techniques. Today, we are sharing a suite of collaboration features that have been progressively developed into the core experimentation workflows in Statsig. Product Manager vineeth madhusudanan works closely with some of our largest customers and has seen companies grow from 0 to 1 to 100s of monthly experiments! He explains how our suite of collaboration features promotes the adoption of experimentation and brings everyone, including non-technical stakeholders, into the experimentation process. - Templates: Empower everyone to easily create experiments based on standardized blueprints at the team or project level. - Change Reviews: Ensure config changes go through approvals so you can scale experimentation in a controlled manner.  - Discussions: Leave comments inline to enable xfn collaboration and track the complete history of changes.  - Experiment Summaries: View all essential details about an experiment's setup, results, discussions, and action items, and export it as a PDF so learnings can be shared across the organization. Statsig is more than just a sophisticated experimentation tool; we aim to drive cultural change. Experimentation Week may be over, but we won't stop shipping new features every week! Thank you for following along, and our team would love to hear your feedback on what we shipped this week 🚀 #experimentation #analytics #datascience

It’s Day 4 of Experimentation Week already 🙌 Today, we are announcing Interaction Detection. Our customers run dozens or even hundreds of experiments concurrently. Data Scientist Kane Luo explains how multiple experiments can have an interaction and how Statsig now enables you to check for interaction effects on a set of metrics. As teams run tests on the same part of the product: - Statsig will now let you test for interactions and identify the type of interaction: synergistic or antagonistic. - You’ll also get actionable insights into traffic allocation and date ranges for the experiments in question.  Statsig also comes with Layers that let you run mutually exclusive experiments at scale. We agree with Lukas Vermeer, Ron Kohavi and literature in this space that lost sleep over potential interaction effects is a bigger concern than actual interaction effects. Now, you have the tools needed to sleep better 😴 Four down, one to go! Stay tuned for the final launch tomorrow 👀 #experimentation #analytics #datascience

Experimentation Week Day 3 🤓 Today, we are announcing Differential Impact Detection to automatically flag when segments of users respond differently to an experiment. This is also described as Heterogeneous Effect Detection. Data Scientist Liz Obermaier shares use cases to illustrate how this provides a great opportunity for experimenters to uncover insights about user segments and build more tailor-made features. Shoutout to Jimin (Anna) Yoon and Minhye Kim who also worked on this and to Ron Kohavi whose work on Segments of Interest inspired this. - Statsig can now investigate the top sub-populations across each user dimension that you specify as a segment of interest. - We'll automatically flag metrics sliced where a sub-population behaves significantly differently from the rest of the experiment audience. One case where this can be especially impactful is when you have a result that is overall positive and statistically significant, but there is a negative impact for some sub-population.  - You’re able to dig in and understand if the feature is broken on a specific browser or country, or if certain user segments, like new users, are responding very differently to the new feature.  - This means that you can iterate on your product by fixing a bug or addressing the needs of a specific user segment before shipping the experiment to all users. (This feature has started rolling out to Enterprise customers) Continue watching this space for two more powerful launches shipping this week 👀 #experimentation #analytics #datascience

Hiring: Customer Data Scientist Today is day 2 of spreading the word on open positions with the Statsig Data Team. Yesterday I promoted our Data Scientist - Growth role which is closely aligned with a traditional product analytics role, but is unique in that this is a pioneering role at our small but growing startup. Today, I'm promoting our Customer Data Scientist role which is even more unique! Customer Data Scientist is explicitly a customer-facing data role. You will work with our customers on the level of knowing their name, experimentation hopes, and worst nightmares. You will help them be successful with onboarding to Statsig, teach them how Statsig works, while proactively assisting and providing expert data science support. If you're a Data Scientist with 2+ yrs of product analytics experience and enjoys working with people, this is a role you should consider. Job description in the comments!

It’s happening today! You, Tyler VanHaren & me. Join our webinar with Statsig, where Tyler VanHaren and I will share what are the most common mistakes companies make and how to tackle them. In case you haven’t registered by now: https://lnkd.in/dJyJuQCu #abtesting #advacedanalytics #productmanagement #productanalytics #bi

It's Experimentation Week - Day 2 ✌️ Today, we are announcing one-click Stratified Sampling to make your experiment results more trustworthy and consistent.  Data Scientist Craig Sexauer explains how this feature works in Statsig and helps you reduce false positive rates at the same power. Shoutout to Nick Jiang Minhye Kim Timothy Chan who also worked on this! - Stratified sampling is especially useful for experiments where a tail-end of power users drive a large portion of a metric value, such as in B2B experiments with small sample sizes. - All you do is provide a metric to balance on (like contract size or number of employees) or a classification (like subscription tier or industry sector) during experiment setup, and we'll balance the control and test groups.  Note: This feature is available on both Statsig Warehouse Native and Statsig Cloud. That's 2 down, and 3 more to go! Keep following us throughout the week to see what else we're shipping 🚀 #experimentation #analytics #datascience

Statsig continues to grow, and we're looking to hire our first Growth Data Scientist. This individual will lead the analytics effort on our product-led growth (PLG) initiative by working cross-functionally across Data, Business Operations, and Marketing. You will find opportunities to improve user acquisition, onboarding success, and provide actionable insights for our marketing and product teams to assist our PLG efforts. This role is 100% in-person in Bellevue, WA. Interested? Application link is in the comments.

Experimentation Week is here! 📌 Day 1: We’re kicking off the week by announcing Meta-analysis. Head of Data, Timothy Chan, explains that many customers have been using Statsig long-enough to have accumulated a solid corpus of experimental results from which to derive valuable insights. We’ve shipped two powerful views that enable meta-analysis: 1. Experiment timeline view: Analyze insights from all your experiments, filter down to a team’s experiments, and instantly get answers to questions like: What experiments are running now? What percentage of experiments ship control vs. test? How much do they impact key metrics? 2. Metrics correlation view: Visualize and scan for correlations across metrics. Quickly plot two metrics on the same chart to check whether they tend to move together. We’d love for you to try this out and hear your feedback! We have more coming here too. Some examples: Near term, we’ll add a feature to look at “batting averages” for metrics you’re trying to move. Mid-term we want to make it easy to back test new metrics against old experiments to validate sensitivity. We’ll be announcing a new feature each day this week. Follow along on this feed and stay tuned 👀 #experimentation #analytics #datascience

Three months ago, I decided to join Statsig as the first customer data scientist. Many people have asked me how I made this decision. It comes down to three things: 🌻 I know the job is hard but valuable. Data is a powerful weapon in today’s world, from developing new medicines, to evaluating AI models, or driving viral product growth. However, data is often underutilized, relegated to being some ‘fancy’ charts in executive reports. I believe in Statsig’s mission; once we help people unlock the value of data, we can drive unprecedented innovation across different industries. 🏆 I am proud to be part of this team. I remember how eye-opening it was to attend my first standup at Statsig - the company has daily standup where everyone shares their works and get applauds. Since then, every day I have been impressed with how many cool things are shipped at Statsig. One great example is Statsig Warehouse Native (WHN), the most popular WHN experimentation product today - https://lnkd.in/dBX7FxpZ 🚀 Just hop on the rocket ship. A new job should be thoroughly evaluated from many dimensions, but at the end of day, it still takes a leap of faith. Like the famous Silicon Valley quote: “If you’re offered a seat on a rocket ship, don’t ask what seat. Just get on.” Now I am onboard. I am super excited for the wonderful adventure ahead! If you want to chat about experimentation, data science, startups or something else, feel free to reach out and lets grab a coffee or bubble tea 😊