🔎 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.

🧩 Segment Filters on Dashboards

We’ve expanded Global Dashboard Filters so you can filter a dashboard using ID List based Segments. This is an additional filter option. Existing global filtering (property filters and other criteria) continues to work the same way.

What You Can Do Now

• Apply a Segment filter at the dashboard level where the segment is defined by an ID list

• Combine an ID List Segment filter with your existing global property filters

• Keep every chart on the dashboard scoped to the same audience without reapplying filters chart-by-chart

How It Works

• Create or select an ID List based Segment (a segment defined by a fixed list of IDs, like user IDs, account IDs, or device IDs)

• In your dashboard’s Global Dashboard Filters, choose that segment as a filter

• The segment filter applies to all charts on the dashboard, alongside any other global filters you’ve set

Example: set the global filter to the segment “Enterprise accounts (ID list)” to ensure every chart reflects only those accounts.

Impact on Your Analysis

• Use dashboards to answer questions about a specific, known set of users or accounts (for example, a customer list, beta cohort, or internal test group)

• Reduce chart-to-chart inconsistencies caused by manually recreating the same ID-based audience filter

• Iterate faster when you need to swap the audience across the entire dashboard (for example, compare two different customer lists)

🧭 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.

🔐 OAuth Support for Statsig MCP Server

We’ve upgraded authentication for the Statsig MCP Server to support OAuth — supplementing the previous key-based authentication flow. This brings a more secure, scalable, and standards-aligned approach to connecting your MCP tooling with Statsig.

Why this matters

OAuth makes it easier and safer for teams to integrate the Statsig MCP Server with their development workflows and in more tools. It enables clearer permission boundaries, smoother onboarding and persistent sessions, and better alignment with modern enterprise security practices.

Getting started

Follow the updated setup instructions in our docs to enable OAuth for your MCP Server connection. No changes are required to your existing Statsig feature flags or experimentation setup — just update your authentication method to take advantage of the new flow.

Learn more in the docs here: https://docs.statsig.com/integrations/mcp

🕵 Traces Explorer now in Beta

Traces show how a single request moves through your system, one step at a time. Statsig new lets you explore those spans alongside experiments and feature rollouts.

Traces Explorer = faster root cause. You no longer need to jump between tools to understand slow or failing requests. Statsig brings traces, logs, metrics, and alerting into one place for critical launches.

Bring observability into your product decision loop.

Traces Explorer (Beta) is available for Cloud customers. View trace setup instructions here.

Traces Explorer

🔌 Reverse Power

What is Reverse Power?

It shows the smallest effect size your test had power to detect based on actual sample size and standard error of the control group (it does not depend on the observed effect size of the experiment). It’s a great tool to leverage retrospectively near the end of an experiment to make more informed decisions.

How is it used?

• Helps you reflect on what your experiment actually could detect

• Helps you with iteration decisions like extending the experiment, rerunning with greater sample, or re-evaluating the experiment design

Getting started

Toggle it on/off anytime in Settings → Product Configuration → Experimentation. To learn more about Reverse Power see our docs here.

🕳️ Results in Lower Environment

For our Cloud users, we’ve added support for viewing cumulative exposure and metric results for experiments enabled in lower environments. Helping users catch issues early and ship experiments with confidence.

This makes it easier to verify that:

• Users are being bucketed correctly

• Your metrics are logging as expected

To enable this, go to the experiment setup page, select Enable for Environments, run tests in your lower environment, and view real exposure and metric data before launching to production.

For more information on the new lower environment testing features see the docs here.

🧏‍♀️ Recent Queries in Logs Explorer

Debugging usually means retracing your steps. You try something, flip tabs, run a new search, and then can’t remember the exact query that actually pointed you in the right direction. It breaks your flow and slows everything down.

Recent Queries fixes that. Logs Explorer now shows your last five searches right in the search menu. No more digging through tabs or guessing what you ran before. Jump back into earlier investigation paths, compare results in seconds, and pick up your workflow without losing momentum.

A small change with a big payoff for staying in the zone.

🚨 Topline Alerts on Metrics

Topline Alerts work best when they track the exact metric your team relies on. Until now, Topline Alerts could only be created using events, which meant rebuilding the metric logic each time. This increases the potential of duplicated definitions, drift over time, and confusion about what the “real” definition should be defined as.

This update fixes that. You can now create Topline Alerts using existing metrics in your Metrics Catalog. The alert uses the metric’s definition exactly as it is, so everything stays consistent and aligned. No rewriting. No re-creating logic. No guessing which version is correct.

It keeps your metrics clean, keeps ownership clear, and removes the risk of definitions drifting as teams grow.

🍋 Metric Source Freshness

Statsig WHN users can now quickly check the freshness of their Experiment Metrics Source data directly from the experiment results page. The data displays the most recent timestamp found in a Metric Source during results computation, helping users identify potential issues in their data pipeline that could delay results calculation.