📏 Count Distinct Metric in Cloud

We’re excited to announce the Count Distinct metric type for Statsig Cloud. Count Distinct lets you measure unique entities—like transactions, devices, or pages views—by counting unique values across the experiment window.

The Count Distinct metric is a sketch based metric that relies on probabilistic HyperLogLog++ (HLL++). We chose this approximation method to optimize for efficiency and speed. Visit our docs page to learn more about our implementation.

It’s available starting today.

🗺️ Explore Multi-Dimension Group By

For Statsig WHN customers, you can now explore your experiment results in greater detail by grouping the results by up to three user properties.

With Multi-Dimensional Group By, you can break down results by combinations like country × device × plan all in one view.

This expands your ability to find interesting insights and data trends at a greater granularity.   

Try it now in the Explore tab of your experiments.

🚀 AI-Powered Experiment Summary

We know writing an experimentation report is everyone’s favorite activity. For those who don’t, it just got easier with Statsig.

With AI-Powered Experiment Summaries, Statsig automatically turns your experiment data into a clear, human-readable summary. The AI summary works to understand the purpose of the experiment, make a ship recommendation, and highlight the most relevant metric observations.

This feature is available now for all Statsig customers. To enable, go to Setting → Statsig AI → AI Summary.

🕵️ AI Knowledge Bank Search [Beta]

For our WHN customers, we’re excited to announce the beta launch of this AI-powered search capability.

With AI-Powered Search, you can now search your repository of experiments using natural language. Try asking questions like “find me experiments that impacted retention the most” or “have we tested using AI for support”. The new search feature will then return three of the best-matching experiments to your question.

If you’re interested in becoming an early tester of this feature, please reach out to us via Slack or through your account manager!

Github AI Integration

We’ve launched a new GitHub AI Integration that connects Statsig directly to your codebase. This is a foundational capability that powers a growing set of AI features across Statsig console.

Why this matters

Through Github connection, Statsig understands where flags, experiments, and metrics live in your code. We then build a Knowledge Graph which maps relationship between code and Statsig entities. Once GitHub is connected, Statsig becomes "code-aware." This unlocks workflows that weren’t possible before, tying product insights directly back to the code that shipped them:

1. Contextual Descriptions: AI-generated descriptions that understand code context behind each metric, flag, and experiment. Quickly understand what each entity in Statsig does and why it exists.

2. Stale Gate Cleanup: One-click workflow to generate PRs to remove stale feature flags directly from Statsig console

3. Metric Explorer Co-pilot (Coming Soon): Describe what you want to analyze in plain English and Statsig creates a chart with the right events, metrics, and breakdowns.

Getting started

To use this integration, navigate to Settings -> Integrations -> Github App page in your Statsig console. Authorize with your Github credentials and install the app on your desired repos. Please contact your Github Org admin to install Statsig App to use the above features! Read more here: https://docs.statsig.com/integrations/github-ai-integration

Contextual AI Descriptions

Bring your code context to automatically generate human-readable descriptions for feature gates, experiments, metrics, and events. Pre-requisite: This feature is powered by Statsig's new Github AI Integration.

Why this is valuable

We have observed that many Statsig users have empty descriptions for their entities (feature gates, metrics, experiments, etc.) in Statsig. This is despite users knowing that good descriptions are super useful in understanding the purpose of any entity.

Statsig AI can understand the meaning behind each feature gate, experiment, event, and metric from the code references that power these entities. As a result:

• Anyone viewing a gate or experiment can quickly understand what it does and why it exists

• In Metrics Explorer, users can see the semantic meaning of events and metrics, not just raw names

This dramatically improves self-serve understanding for PMs, engineers, and new team members.

How this works

• If you empty description, Statsig will automatically pre-fill description fields with AI-generated context.

• If you already have a description, Statsig will show an AI suggestion that can be more richer in context.

AI Stale Gate Cleanup

Until now, Statsig only detected feature gates that were no longer active and marked them "stale." With new Github AI Integration, you can directly generate a pull request to remove the dead code from Statsig UI in one click.

Why This Is Valuable

Cleaning up dead flags is usually painful and gets deprioritized. This turns it into a one-click workflow:

• Click “remove from code”

• Review the generated PR

• Approve and merge

Teams reduce flag debt without risky manual cleanup.

Getting Started

Connect Statsig to your Github Org account to enable AI-powered stale gate code removal.

🔎 Global Filters in Funnels

Funnels now support Global Filters. Global Filters apply across the entire funnel, so you can set shared constraints once instead of repeating them across steps.

What You Can Do Now

• Apply a single set of filters to all steps in a funnel

• Filter funnel results using additional artifacts, including:

  • Experiments (for example, only users in a specific experiment or variant)

  • Segments

  • Holdouts

  • ID list based segments

How It Works

• Use the Global Filters section in the funnel builder to define who should be included in the funnel analysis.

• Global Filters apply to every step in the funnel.

• Example use cases:

  • Experiment analysis: Show funnel conversion for users in Variant B of an experiment.

  • Segmented funnel: Restrict the funnel to a specific Segment (for example, “Power users”).

  • Holdout-aware reporting: Exclude users in a Holdout to avoid mixing test and control populations.

  • Targeted cohorts: Use an ID list based Segment to analyze conversion for a specific account list.

Impact on Your Analysis

• Keep funnel results consistent across steps by applying shared constraints once

• Reduce setup time when iterating on funnel definitions

• Make it easier to compare funnels across experiments, segments, and holdouts without reworking step logic

Global Filters make it faster to build funnels that match the exact population you want to analyze, especially when your audience is defined by experiments, segments, or holdouts.

📊 Session Analytics on Warehouse Native

You can now run Session Analytics on Warehouse Native data, as long as you are also logging events with the Statsig SDK. This is a new capability for Warehouse Native and does not replace any existing session analytics workflows.

What You Can Do Now

• Analyze user sessions directly in Warehouse Native

• Use Metric Drilldown charts to answer session questions like:

  • How many daily sessions are occurring?

  • What is the median (p50) session duration?

  • How does session duration vary by browser, device, or other properties?

How It Works

A session is defined as a period of user activity followed by at least 30 minutes of inactivity. Session Analytics uses a special statsig::session_end event, which includes a property for session duration in seconds. You can use this event in Metric Drilldown charts to slice, group, and compare session metrics.

This is supported only for customers who:

• Use Warehouse Native

• Log events with the Statsig SDK (so statsig::session_end is available)

Impact on Your Analysis

• Run session-level metrics alongside your warehouse-native metrics without needing a separate session pipeline

• Measure engagement using session counts and duration with the same segmentation and breakdowns you already use in Metric Drilldown charts

If you are already on Warehouse Native and logging with the SDK, you can start using Session Analytics right away.

🧭 Warehouse Explorer

Overview

Warehouse Explorer makes it easy to bring warehouse data into Statsig, without needing to know the “right” table upfront. Previously, if you wanted to analyze warehouse data in Statsig, you had to know the name, location, and schema of the table and configure a metric source for it. Now, Metrics Explorer includes a visual browser for your warehouse projects, folders, and tables so you can discover what you need first, then add it for analysis. This is a new capability and is only available for Warehouse Native customers.

What You Can Do Now

• Browse your warehouse projects, folders, and tables directly in Statsig

• Click into any table to quickly understand what’s inside:

  • Column names

  • Sample values for each column

  • Per-value row distribution metadata (what percent of rows each value represents)

• Bring a table into Statsig for analysis in a few clicks:

  • Name the source

  • Provide light configuration (for example, whether it’s an event-based table)

• Build self-serve visualizations from warehouse data faster, like:

  • Funnels from GA4 tables

  • Exploring Stripe data without manual setup work

How It Works

1. Open Metrics Explorer and browse through your warehouse projects and folders.

2. Select a table to preview its schema, sample values, and distribution metadata.

3. When you find the table you want, name it and complete a small amount of configuration (for example, mark whether it’s event-based).

4. Start analyzing the table in Statsig right away.

Impact on Your Analysis

• Faster time to first chart because you can discover tables inside Statsig instead of tracking them down elsewhere

• More self-serve exploration for teams with lots of warehouse datasets

• Easier setup for common workflows like funnels and business-data analysis

Warehouse Explorer is available today for all Warehouse Native Analytics customers.