Statsig Release Notes

Abort long-running queries from Metrics Explorer to reduce warehouse load and avoid unnecessary compute usage.

What You Can Do Now

• Prevent long-running queries from tying up warehouse resources
• Avoid accidental full-dataset scans
• Limit the cost impact of exploratory queries

How It Works

Metrics Explorer queries can be canceled after 5 seconds when running on supported warehouse integrations (BigQuery, Databricks, Snowflake, and Athena). Query cancellation currently applies to individual charts and does not yet extend to dashboards.

Impact on Your Analysis

Cancel Queries let you interrupt a run, refine the query, and try again immediately. This reduces unnecessary warehouse usage while keeping exploratory workflows fast and responsive.

What You Can Do Now

• Understand product stickiness out of the box
• Separate growth driven by new vs sustained engagement
• Spot churn and reactivation patterns at a glance

How It Works

Select an event, define a unique unit, and choose a time interval. Lifecycle Charts automatically classify activity by one of four lifecycle states:

• New:
  Active in the current interval with no prior activity within the lookback window (up to one year)
• Resurrected:
  Active in the current interval, not active in the previous interval, but had activity earlier in the lookback window
• Recurring:
  Active in both the current and immediately previous interval, indicating continued engagement.
• Dormant:
  Active in the previous interval but inactive in the current one, highlighting potential churn

Impact on Your Analysis

Lifecycle Charts reveal why usage changes by showing shifts in engagement composition over time. Teams can distinguish growth from retention changes, identify drop-off earlier, and understand product stickiness without building custom retention analyses.

Check out our docs for more information.

What You Can Do Now

• Generate dashboards from an API request
• Add time series, rich text, and categorical widgets
• Integrate dashboard creation into workflows powered by tools like Codex Skills

Impact on Your Analysis

Dashboards can now be managed at scale through code. Teams can automate setup to save time and integrate it with the tools they already use. The console remains available for exploration and refinement.

Private Beta

This feature is currently in private beta for Pro and Enterprise customers.
If you'd like access, reach out over Slack.

Add structure to dashboards by organizing widgets into focused sections. Dashboard Pages help teams separate workflows and context so related signals live together.

What You Can Do Now

• Navigate dashboards with clearer context
• Group related widgets into dedicated views
• Keep dashboards performant as they scale

How It Works

Add pages inside a dashboard to organize widgets into distinct sections while keeping everything in one place.

Impact on Your Analysis

Loading fewer widgets at once improves dashboard performance and responsiveness. Teams can move between workflows faster while working with large or complex dashboards

Stay informed on key metrics through scheduled dashboard reports. Dashboard Subscriptions deliver a PDF snapshot of your dashboard directly to Slack or email on a cadence you choose.

What You Can Do Now

• Receive automated dashboard snapshots in Slack or email
• Schedule recurring updates for teams or stakeholders
• Keep visibility on important metrics without manually checking dashboards

How It Works

From any dashboard, open the “…” menu and select Add Dashboard Subscription. Configure the delivery schedule and subscribed audience. Statsig generates a PDF snapshot at the scheduled time and delivers a read-only version of the dashboard via Slack or email.

Impact on Your Analysis

Dashboard Subscriptions makes it easier for teams to monitor ongoing trends asynchronously. Stakeholders receive recurring updates as dashboards update.

🎚️ Switchback Enhancements for WHN

We rolling out an improved Switchback Experimentation model to WHN customers. The new Switchback experiment utilizes a regression-based analysis method that replaces our previous bootstrapping approach. This update brings greater flexibility and analytical power, including the ability to break down results by pre-computed dimensions, more configurable burn-in/out periods, and improved scheduling and clustering.

What is a Switchback experiment?

By alternating treatments over time for the same units, switchbacks help control for interference and capture more realistic system-level effects. Use a switchback experiment when you can’t reliably randomize at the user level—typically because treatments affect shared systems or environments (e.g., marketplaces, pricing, routing, or infrastructure).

How do I enabled the enhanced features?

Cutting over to the new Switchback model is a breaking change, and we’ll work closely with customers running legacy switchback experiments to plan a smooth migration. For customers who haven’t previously used switchback experiments in Statsig, the feature will be rolled out in the coming days. If you’re interested in learning more or getting started, feel free to reach out via Slack or your account manager.

Inline Power Analysis

• Run and view power analysis results without switching to another page
• Set and iterate on target MDEs and experiment duration
• Instantly see recommended experiment duration based on your inputs
• Quickly access results anytime—results are saved and visible on the setup page

This makes it easier to align on realistic expectations before you launch, ensuring your experiments are both efficient and statistically sound.

Inline Power Analysis is rolling out in the coming days to all of our Cloud customers.

We’ve added metrics and metric source tools to the Statsig MCP, so your agents can now easily read and analyze Statsig metrics metadata from within their workflows.

What You Can Do Now

• List metrics and metric sources in your Statsig project
• Retrieve metric definitions

This makes it much easier for MCP-powered workflows to find the right metric, inspect how it’s defined, and understand which metric sources are available before creating, updating, and analyzing experiments and gates.

Why This Matters

Before this, the Statsig MCP supported adding existing metrics to gates and experiments, but had less visibility into the metrics layer itself. Now with these tools, agents can reason about Statsig metric definitions and sources directly, making it easier to discover the right metrics, understand their definitions, and set up experiments with confidence.

Try It Out

If you have the Statsig MCP set up, try the below example prompts and workflows to explore the new metrics functionality:

• "What metrics do we have related to user retention? Pull their definitions and suggest which would work best for a 7-day activation experiment."
• "Pull up the definition for the metric [metric name]"
• (For Warehouse Native projects) "What metric sources do we have configured?"

To set up the Statsig MCP server and explore all the capabilities it supports today, see our docs page.

We’ve enhanced our variance reduction methodology in Cloud by automatically applying Stratification to better account for users with missing pre-experiment data.

How it Works

Users are grouped into strata based on available pre-experiment information. Treatment effects are then calculated within each group before being aggregated into a single estimate and applying standard difference-in-means and variance calculations.

Impact on your Analysis

This improvement retains more users in analysis while still applying variance reduction wherever pre-experiment data exists. The result is tighter confidence intervals for more reliable decision-making.