Product Updates

📊 Compare more Groups in Funnels

You can now compare up to 15 groups in funnel charts when using a group by, up from the previous limit of 5.

What You Can Do Now

• Select and compare up to 15 groups in a funnel analysis

• Use a new selector to control exactly how many groups to display

How It Works

Once you apply a group by (e.g., browser, country, experiment variant), a group count selector appears. Use it to choose how many top groups to include based on event volume.

Impact on Your Analysis

This gives you more flexibility to analyze performance across more segments—especially helpful for large experiments, multi-region launches, or platform-specific funnels.

Let us know how this works for your use case—we’re always looking to improve.

🌍 Donut Chart & World Map

Drilldown now includes two new visualization options to help you better understand the distribution of your metrics: Donut charts and a World Map view.

What You Can Do Now

• Use Donut charts to visualize proportional breakdowns of any event or metric

• Use the World Map to see event counts or metric values by country

• Apply these views to any Drilldown chart with a group-by

How It Works

In Drilldown, after selecting a metric and grouping it by a property (like country, browser, or device type), choose either the Donut or World Map view from the chart type selector. The map overlays values by country, while the donut shows relative proportions.

Impact on Your Analysis

These views make it easier to spot geographic trends, visualize dominant segments, or present clean summaries of how usage breaks down across key dimensions—especially useful for sharing or monitoring at a glance.

✋ Holdout Analysis in Product Analytics

You can now filter or break down any Product Analytics chart by holdout group, making it easier to measure the combined impact of multiple features.

What You Can Do Now

• Filter any chart to only include users in a specific holdout group

• Break down metrics by holdout status to compare behavior between held-out users and exposed users

How It Works

Holdouts are used to evaluate the aggregate effect of multiple features—not just individual experiments. A holdout group is a set of users who are intentionally excluded from a group of features or experiments to serve as a baseline. Now, you can use that same grouping to filter or break down any Product Analytics chart.

To apply, use the filter or group-by menu on a chart and select the relevant holdout.

Impact on Your Analysis

Holdout analysis helps you answer questions like:

• What’s the total impact of all features launched in the last quarter?

• Are users in the holdout group retaining or converting differently than exposed users?

It gives you a high-level view of product changes—beyond individual experiments—using the same familiar Product Analytics workflows.

🎯 Cohort Filters on Dashboards

You can now filter entire dashboards using behavioral cohorts—alongside existing property-based filters.

What You Can Do Now

• Apply a behavioral cohort as a filter to any dashboard

• Combine cohort filters with property filters for scoped, layered analysis

• Easily compare how different cohorts interact with your product across multiple charts

How It Works

From the dashboard filter panel, select a saved cohort to apply it globally. All charts on the dashboard will update to reflect data only for users in that cohort. You can still apply property filters in parallel.

This makes it simple to compare behaviors across user groups like “First-time Users,” “Power Users,” or “Users Who Churned Last Month.”

Impact on Your Analysis

Cohort filters unlock more targeted analysis across dashboards, allowing you to focus on the patterns and behaviors of specific user groups without editing each chart individually.

📊 New Aggregation: Count per User

You can now measure how frequently users (or other unit IDs) perform a specific event with the new Count per Useraggregation option.

What You Can Do Now

• Analyze the average, median, or percentile distribution of how often an event is performed per user (or per company, account, etc.)

• Select from: average (default), median, min, max, 75th, 90th, 95th, or 99th percentile

• Choose the unit ID to aggregate on—user ID, company ID, or any custom unique identifier

How It Works

When you select Count per User in Metric Drilldown charts, Statsig calculates how many times each unit ID performed the chosen event during the time window. You can then apply summary statistics like median or 95th percentile to understand the distribution across those users.

This aggregation only includes unit IDs that performed the event at least once in the time range—it doesn’t factor in users who did not perform the event.

Impact on Your Analysis

This gives you a more nuanced view of engagement patterns, helping you answer questions like:

• What’s the median number of times a user triggers a key action?

• How often do your most active users complete a workflow?

• How concentrated or spread out is usage of a particular feature?

Ideal for understanding usage depth, not just reach.

🧭 User Journeys on Warehouse Native

You can now use User Journeys in the Warehouse Native version of Statsig to visualize the most common paths users take through your product.

What You Can Do Now

• Build user journeys directly on your WHN setup

• Choose a source table, specify your event name column, and select a starting event

• Analyze the most frequent sequences of events after that starting point

How It Works

To get started, select the table where your events are stored, specify which column contains the event names, and choose the event that marks the beginning of the journey. Statsig will generate a path view showing the most common user flows from that point forward.

At this stage, User Journeys on WHN are designed for schemas where all events live in a single source table. We’re actively working to support setups with:

• One source table per event

• Multiple source tables each containing many events

Impact on Your Analysis

This feature gives you visibility into how users move through your product, where they drop off, and which paths are most common—directly within your warehouse environment.

⚙️ New Chart Options in Metrics Explorer

A new dedicated chart settings panel gives you more control over how charts are displayed—making it easier to fine tine your analysis data and how that data is visualized.

What You Can Do Now

From the gear icon in the top-right of any chart, you can now:

• Start Y-Axis at 0 for more consistent visual baselines

• Filter Out Bots to clean up automated or test traffic

• Include Non-Prod Data when needed for QA or staging checks

• Show Table/Legend Only to highlight key values without showing the full plot

• Split Charts by Metric (in Metric Drilldown only) to display each metric on its own chart—ideal for comparing metrics with different units or scales

How It Works

Click the gear icon to open the chart options panel. These settings are chart-specific and persist as part of the chart configuration. When using Drilldown, splitting by metric creates a stacked view—turning one chart into a mini dashboard.

Impact on Your Analysis

These controls help tailor each chart to its purpose—whether you’re cleaning up noisy data, presenting key takeaways, or exploring metrics with vastly different scales.

Statsig is proud to announce CURE, an advanced implementation of CUPED that allows customers to perform more complex regression adjustment using user-inputted categorical or numerical covariates.

This means users can run experiments even faster on Statsig, and extends the power of CUPED by making it available for new user experiments or metrics without any pre-exposure data available. This is compatible with simple adjustments to the regression - e.g. adding categorical covariates like region - or complex use cases like using predicted outcomes as a covariate.

Refer to our blog post for more information on CURE as well as the docs. This feature is currently available on Warehouse Native, and will be applied to experiments started after today. We'll be following up with CURE on Statsig Cloud.

📜 Helm Charts for Forward Proxy

Helm Charts are a simple way to deploy Kubernetes resources - like NPM, but for Kubernetes. Today we're releasing Helm Charts for the Statsig Forward Proxy - making it easy to add the proxy to your services. The Forward Proxy provides a centralized point of access for Statsig rulesets in your infrastructure resulting in lower cost, improved performance, and an extra layer of resilience. Helm charts make proxy deployment worlds easier - allowing you to deploy with simple commands like "helm install statsig-forward-proxy statsig/statsig-forward-proxy". You can also configure various options for Forward Proxy setup. Try out the Helm Chart today by visiting our Docs!

Gradual Experiment Rollouts

When you conclude an A/B test and decide to ship the winning variant, typically a 100% of the experiment's traffic shifts to the winning variant. Statsig now lets you ramp this gradually if you don't want a sudden, large shift like this.

Typical use case : When you're testing 5 variants; each gets 20% of traffic. When you make a ship decision, the winning variant goes from 20% of traffic to 100% of traffic. If you want to ramp traffic gradually to understand impact on CPU (or other resources), this feature lets you configure and schedule a multi-step, automated ramp.

Learn more here.