Product Updates

We ship fast to help you ship faster
Vineeth Madhusudanan
Product Manager, Statsig
10/29/2024
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Warehouse Native Compute Transparency Dashboard

Statsig Warehouse Native now lets you get a birds eye view across the compute time experiment analysis incurs in your warehouse. Break this down by experiment, metric source or type of query to find what to optimize.

Common customers we've designed the dashboard to be able to address include

  • What Metric Sources take the most compute time (useful to focus optimization effort here; see tips here)

  • What is the split of compute time between full loads vs incremental loads vs custom queries?

  • How is compute time distributed across experiments? (useful to make sure value realized and compute costs incurred are roughly aligned)

You can find this dashboard in the Left Nav under Analytics -> Dashboards -> Pipeline Overview

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This is built using Statsig Product Analytics - you can customize any of these charts, or build new ones yourself. A favorite is to add in your average compute cost, so you can turn slot time per experiment into $ cost per experiment.

Vineeth Madhusudanan
Product Manager, Statsig
10/25/2024
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Power Analysis attached to Experiments

Power Analysis is critical input into experiment durations when you care about trustworthy experiments. When you perform Power Analysis for an experiment, the analysis is now automatically attached to the experiment and available to other reviewers. When you start Power Analysis for an experiment, we'll prepopulate any Primary Metrics you've already configured on the experiment.

This feature is rolling out to Statsig Cloud and Warehouse Native customers over the next week.

Experiment Setup Screen

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Starting Power Analysis from an Experiment

Start Power Analysis
Craig Sexauer
Data Scientist, Statsig
10/25/2024
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🥇 First-Value metrics

Alongside latest value metrics, we’re also announcing First-Value metrics. These allow you to see the value from the first record the user logged while exposed to an experiment. Imagine being able to track first purchase value, first subscription plan price, or first-time time-to-load on a new page.

Learn more in our documentation

Akin Olugbade
Product Manager, Statsig
10/25/2024
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🔍 Focused Analysis with Top Group Limits

Use Case

Imagine you’re analyzing user behavior across segments like browser types, referral sources, or search terms. Applying a group-by might return a long list of groups, many with minimal impact on your metrics. This volume of data can make it difficult to focus on the most significant segments.

Why It’s Important

By setting a limit on the number of groups displayed, you can reduce clutter and concentrate on the segments that matter most. This helps you avoid distractions from less impactful data points and enables you to focus on meaningful insights that can inform your decisions.

What It Does

When applying a group-by in your charts, you can now specify a limit on the number of groups returned. The groups are sorted by the highest value of the metric you’re analyzing, so you’ll see only the top-performing segments. To use the feature click the "..." in the group-by section and select "Add Group-By Limit"

group-by-limits
Craig Sexauer
Data Scientist, Statsig
10/24/2024
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🪵🪄 Log transforms

We’re adding the ability to log-transform sum and count metrics, and measure the average change to unit-level logged values.

Log Transforms are useful when you want to understand if user behavior has generally changed. If a metric is very head-driven, even with winsorization and CUPED the metric movement will generally driven by power users.

Logs are multiplicative, so a user going from spending $1.00 to spending $1.10 is the same “metric lift” as another going from $100 to $110. This means that what they measure is closer to shifts in relative distribution, rather than topline value.

Because of this divorce from “business value,” log metrics are usually not good evaluation criteria for ship decisions, but alongside evaluation metrics, they can easily provide rich context on the change in the distribution of your population.

By default, the transform is the Natural Log, but you can specify a custom base if desired.

Learn more in our documentation.

Craig Sexauer
Data Scientist, Statsig
10/23/2024
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⌚💰📊 Latest value metrics

We launched latest value metrics for user Statuses in March this year, and just extended support to numerical metrics. This will be useful for teams that want to track how experiments impact the “state” of their userbase.

You could already track subscription status, but now you can track users’ current balance, lifetime spend, or LTV - without duplicating the data across multiple different days. Each day in the pulse time-series will reflect the latest value as of that day.

Learn more in our documentation.

Akin Olugbade
Product Manager, Statsig
10/18/2024
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👯 Cohort Analysis Now Available Across All Chart Types

Cohort analysis is now supported across all chart types in Metrics Explorer. Previously available only in drilldown charts, this feature allows you to filter your analysis to specific user cohorts or compare how different groups perform against various metrics.

Filtering to an interesting cohort is supported across all chart types and can be accomplished by adding a single cohort to your analysis. Cohort comparison is available in metric drilldown, funnel, and retention charts and can be accomplished by adding multiple cohorts to your analysis.

What’s New

  • Expanded Support: Cohort filtering is now integrated into funnels, retention charts, user journeys, and distribution charts.

  • Detailed Comparisons: You can compare how different cohorts, such as casual users and power users, navigate through funnels like the add-to-cart flow.

  • Focused Analysis: Easily scope your analysis to understand how specific user groups perform, helping you identify patterns and behaviors unique to each cohort.

Expanded support for cohort analysis will begin rolling out today.

Akin Olugbade
Product Manager, Statsig
10/18/2024
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🧲 Group-by in Retention Analysis

We’ve added group-by functionality to retention charts, enabling you to break down your retention analysis by various properties and gain deeper insights into user behavior. This feature allows you to segment your retention data across event properties, user properties, feature gate groups, and experiment variants.

Group-By in retention charts is available for:

  • Event and User Properties: Break down retention based on event and user properties such as location, company or different context about an event or feature..

  • Feature Gate Groups: Understand retention among different user groups gated by feature flags.

  • Experiment Variants: Compare retention across experiment groups to see how different variants impact user retention.

Expanded support for group-by in retention charts is rolling out today.

Akin Olugbade
Product Manager, Statsig
10/8/2024
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🪜Funnels Now Support Up to 15 Steps

We’ve tripled the maximum number of steps allowed in funnels from 5 to 15. This change allows you to build more detailed funnels that capture longer and more complex user journeys. With up to 15 steps, you can analyze extended sequences of user actions, gain deeper insights into user behavior, and identify opportunities to optimize each stage of your funnel.

Akin Olugbade
Product Manager, Statsig
10/8/2024
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📊 Cohort Analysis in Funnels

We have introduced Cohort Analysis to our funnel feature, allowing you to filter your funnel analysis to specific cohorts or compare how different cohorts progress through the same funnel.

Filter Funnels by Cohort

You can now focus your funnel analysis on specific user cohorts. This means you can examine how particular groups—like new users, users from a specific marketing campaign, or users who have completed a certain action—navigate through your funnels. Filtering by cohort helps you identify unique behaviors and patterns within these groups, enabling you to tailor your strategies to improve their experience.

Compare Conversion Across Cohorts

In addition to filtering, you can compare how different cohorts convert across the same funnel. This comparative view lets you see how various segments of your user base perform relative to each other. For example, you might compare first-time users to returning users, users from different geographic regions, or users acquired during different time periods. Understanding these differences can inform targeted improvements and highlight areas where certain cohorts may need additional support.

Cohorts in Funnels

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OpenAI
"Statsig's experimentation capabilities stand apart from other platforms we've evaluated. The ease of use, simplicity of integration help us efficiently get insight from every experiment we run. Statsig's infrastructure and experimentation workflows have also been crucial in helping us scale to hundreds of experiments across hundreds of millions of users."
Paul Ellwood
Head of Data Engineering
SoundCloud
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Don Browning
SVP, Data & Platform Engineering
Whatnot
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Rami Khalaf
Product Engineering Manager
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Shannon Priem
Lead PM
Ancestry
"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."
Partha Sarathi
Director of Engineering
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Jeff To
Engineering Manager
"[Statsig] enables shipping software 10x faster, each feature can be in production from day 0 and no big bang releases are needed."
Matteo Hertel
Founder
OpenAI
"Statsig has been an amazing collaborator as we've scaled. Our product and engineering team have worked on everything from advanced release management to custom workflows to new experimentation features. The Statsig team is fast and incredibly focused on customer needs - mirroring OpenAI so much that they feel like an extension of our team."
Chris Beaumont
Data Scientist
"The ability to easily slice test results by different dimensions has enabled Product Managers to self-serve and uncover valuable insights."
Preethi Ramani
Chief Product Officer
"We decreased our average time to decision made for A/B tests by 7 days compared to our in-house platform."
Berengere Pohr
Team Lead - Experimentation
"Statsig is a powerful tool for experimentation that helped us go from 0 to 1."
Brooks Taylor
Data Science Lead
"We've processed over a billion events in the past year and gained amazing insights about our users using Statsig's analytics."
Ahmed Muneeb
Co-founder & CTO
SoundCloud
"Leveraging experimentation with Statsig helped us reach profitability for the first time in our 16-year history."
Zachary Zaranka
Director of Product
"Statsig enabled us to test our ideas rather than rely on guesswork. This unlocked new learnings and wins for the team."
David Sepulveda
Head of Data
Brex
"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."
Karandeep Anand
President
Ancestry
"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."
Partha Sarathi
Director of Engineering
Recroom
"Statsig has been a game changer for how we combine product development and A/B testing. It's made it a breeze to implement experiments with complex targeting logic and feel confident that we're getting back trusted results. It's the first commercially available A/B testing tool that feels like it was built by people who really get product experimentation."
Joel Witten
Head of Data
"We realized that Statsig was investing in the right areas that will benefit us in the long-term."
Omar Guenena
Engineering Manager
"Having a dedicated Slack channel and support was really helpful for ramping up quickly."
Michael Sheldon
Head of Data
"Statsig takes away all the pre-work of doing experiments. It's really easy to setup, also it does all the analysis."
Elaine Tiburske
Data Scientist
"We thought we didn't have the resources for an A/B testing framework, but Statsig made it achievable for a small team."
Paul Frazee
CTO
"We use Statsig's analytics to bring rigor to the decision-making process across every team at Wizehire."
Nick Carneiro
CTO
Notion
"We've successfully launched over 600 features behind Statsig feature flags, enabling us to ship at an impressive pace with confidence."
Wendy Jiao
Staff Software Engineer
"We chose Statsig because it offers a complete solution, from basic gradual rollouts to advanced experimentation techniques."
Carlos Augusto Zorrilla
Product Analytics Lead
"We have around 25 dashboards that have been built in Statsig, with about a third being built by non-technical stakeholders."
Alessio Maffeis
Engineering Manager
"Statsig beats any other tool in the market. Experimentation serves as the gateway to gaining a deeper understanding of our customers."
Toney Wen
Co-founder & CTO
"We finally had a tool we could rely on, and which enabled us to gather data intelligently."
Michael Koch
Engineering Manager
Notion
"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
OpenAI
"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."
Dave Cummings
Engineering Manager, ChatGPT
OpenAI
"Statsig has helped accelerate the speed at which we release new features. It enables us to launch new features quickly & turn every release into an A/B test."
Andy Glover
Engineer
"We knew upon seeing Statsig's user interface that it was something a lot of teams could use."
Laura Spencer
Chief of Staff
"The beauty is that Statsig allows us to both run experiments, but also track the impact of feature releases."
Evelina Achilli
Product Growth Manager
"Statsig is my most recommended product for PMs."
Erez Naveh
VP of Product
"Statsig helps us identify where we can have the most impact and quickly iterate on those areas."
John Lahr
Growth Product Manager
Whatnot
"With Warehouse Native, we add things on the fly, so if you mess up something during set up, there aren't any consequences."
Jared Bauman
Engineering Manager - Core ML
"In my decades of experience working with vendors, Statsig is one of the best."
Laura Spencer
Technical Program Manager
"Statsig is a one-stop shop for product, engineering, and data teams to come together."
Duncan Wang
Manager - Data Analytics & Experimentation
Whatnot
"Engineers started to realize: I can measure the magnitude of change in user behavior that happened because of something I did!"
Todd Rudak
Director, Data Science & Product Analytics
"For every feature we launch, Statsig saves us about 3-5 days of extra work."
Rafael Blay
Data Scientist
"I appreciate how easy it is to set up experiments and have all our business metrics in one place."
Paulo Mann
Senior Product Manager
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