⚙️ Custom Metrics in Funnels

Funnels become more powerful with the ability to use saved custom metrics as funnel steps. This integration eliminates the need to manually reconstruct complex event combinations or filtered events each time you build a funnel.

What You Can Do Now

• Use saved custom metrics as steps in your conversion funnels

• Apply filtered events and multi-event combinations consistently across your analyses

• Build funnels faster by using your existing metric definitions

• Maintain consistent event definitions across your team's funnel analyses

How It Works

• When creating a funnel step, you can now select from both raw events and your saved custom metrics

• Each custom metric maintains its original configuration, including filters and event combinations

• Changes to a custom metric automatically reflect in any funnel using it as a step

• Mix and match raw events and custom metrics within the same funnel

Impact on Your Analysis

Say you're tracking signup conversion and your "Completed Signup" step needs to capture multiple success events while excluding test accounts. Instead of rebuilding this logic for each funnel:

1. Use your saved custom metric that already has the correct configuration

2. Drop it directly into your funnel as a step

3. Trust that all your funnel analyses use consistent event definitions

This update reduces manual setup time and helps your team measure conversion points consistently across your analytics.

📊 Distribution Charts++

Distribution Charts now offer three specialized views to help you uncover patterns in your user behavior and event data, along with smarter automatic binning.

What You Can Do Now

• Analyze user engagement patterns with Per User Event Frequency distributions to see how often individual users perform specific actions

• Explore value patterns across events using Event Property Value distributions to understand the range and clustering of numeric properties

• Discover user-level patterns with Aggregated Property Value distributions, showing how property values sum or average per user over time

• Let the system automatically optimize your distribution bins, or take full control with custom binning

How It Works

• Per User Event Frequency shows you the spread of how often users perform an action, like revealing that most users share content 2-3 times per week while power users share 20+ times

• Event Property Value examines all instances of a numeric property across events, such as seeing the distribution of order values across all purchases

• Aggregated Property Value calculates either the sum or average of a property per user, helping you understand patterns like the distribution of total spend per customer

• Smart binning automatically creates 30 optimized buckets by default, or you can set custom bucket ranges for more precise analysis

Impact on Your Analysis These new distribution views help you answer critical questions about your product:

• Is your feature reaching broad adoption or mainly used by power users?

• What's the typical range for key metrics like transaction values or engagement counts?

• How do value patterns differ when looking at individual instances versus per-user aggregates?

The combination of flexible viewing options and intelligent binning makes it easier to find meaningful patterns in your data, whether you're analyzing user behavior, transaction patterns, or engagement metrics.

Differential Impact Detection on Cloud

This feature automatically flags when sub-populations respond very differently to an experiment. This is sometimes referred as Heterogeneous Effect Detection or Segments of Interest.

Overall results for an experiment can look "normal" even when there's a bug that causes crashes only on Firefox, or when feature performs very poorly only for new users. You can now configure these "Segments of Interest" and Statsig will automatically analyze and flag experiments where we detect differential impact. You will be able to see the analysis that resulted in this flag.

Learn about how this works or see how to turn this on in docs. This feature shipped on Statsig Warehouse Native last summer and is now available on Statsig Cloud too!

Results in your Warehouse

People running many experiments use Statsig's Meta-analysis tools. When they want to explore this dataset more directly, they've had access to it via the Console API. We're now adding the ability to have Statsig push the final results that are visualized in the Console, into your warehouse also.

This feature is gradually rolling out across Statsig Warehouse Native customers.

Java Server Core

Server Core is a full rewrite of our Server SDKs with a shared, performance-focused Rust library at the core - and bindings to each other language you'd want to use it in. Today, we're launching Java Server Core.

Server Core

Server Core leverages Rust's natural speed, but also benefits from being a single place that we can optimize our server SDKs' performance. Our initial benchmarking suggests that Server Core can evaluate configs 5-10x faster than native SDKs.

You can install Java Core today by adding the necessary packages to your build.gradle - see our docs to get started. In the coming months, we expect to ship Server Core across Node, Python, PHP, and more!

Interaction Detection on Experiments

We shipped Interaction Detection on Statsig Warehouse Native last year. We've now brought it to Statsig Cloud customers too.

What is Interaction Detection​

When you run overlapping experiments, it is possible for them to interfere with each other. Interaction Detection lets you pick two experiments and evaluate them for interaction. This helps you understand if people exposed to both experiments behave very differently from people who're exposed to either one of the experiments.

Should I worry about it?​

Our general guidance is to run overlapping experiments. People seeing your landing page should experience multiple experiments at the same time. Our experience is echoed by all avid experimenters (link). Teams expecting to run conflicting experiments are typically aware of this and can avoid conflicts by making experiments mutually exclusive via Layers (also referred to as Universes).

Read more in docs or the blog post.

Reimagining Console Settings: A Cleaner, Smarter Experience

We've completely redesigned our Console Settings to streamline how you manage your Statsig projects. The new architecture brings three major improvements:

Intuitive Navigation: Navigate effortlessly with our new left sidebar, putting every setting at your fingertips. No more hunting through nested menus.

Product-Centric Organization: Each Statsig product—Experimentation, Feature Gates, and Product Analytics—now has its dedicated configuration hub. Tailor each product's settings to your exact needs, all from one central location.

Hierarchical Control: Configure settings at Team, Project, or Organization level, ensuring consistency while maintaining flexibility. Perfect for enterprises managing multiple teams and projects.

This redesign is live now. Log in to explore the new experience.

Slicing by frequently used user properties

Statsig let's you slice results by user properties. Common examples of doing this include breaking down results by user's home country, subscription status or engagement level.

This typically requires running a custom query (from the Explore tab). You can now configure these properties to be pre-computed on the experiment setup page, under the advanced settings. It's also possible to configure team-level defaults for this - or pre-configure it on an experiment template.

This is now rolling out on Statsig Warehouse Native. See docs.

Reimagining Console Settings: A Cleaner, Smarter Experience

Our data science team noticed rising support tickets around warehouse data not appearing correctly in Statsig. Investigation revealed most issues stemmed from unclear error feedback and limited self-service capabilities, leading to unnecessary delays and support escalations.

Today, we're launching two key improvements:

Error Visibility: Consolidated error table across all data sources with clear, actionable messages and troubleshooting steps. A single view replaces the previous table-by-table navigation.

Self-Service Resolution: Step-by-step diagnosis flow lets users verify their connection setup and SQL queries, with immediate data re-ingestion once fixed.

These updates aim to help you discover any integration issues with your data warehouse connection and fix those issues without needing to depend on our internal support. We'll continue expanding these capabilities based on your feedback.

See all your data warehouse related errors in one place and diagnose them right from Statsig console.

👥 Filtering by User Dimensions in Custom Metrics

Today, we’re introducing the ability to filter by User dimensions in Custom Metrics on Statsig Cloud. Previously, you could filter by the Value of a metric, as well as any custom Metadata. Now, you can filter by both Statsig-populated User Object attributes (”User” → “Property”) as well as any Custom user attributes you’ve set in your User Object (”User” → “Custom Property”).

New Custom Metric filtering on a custom user attribute