Drilldown now includes two new visualization options to help you better understand the distribution of your metrics: Donut charts and a World Map view.
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
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.
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.
You can now use User Journeys in the Warehouse Native version of Statsig to visualize the most common paths users take through your product.
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
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
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.
You can now filter entire dashboards using behavioral cohorts—alongside existing property-based filters.
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
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.”
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.
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 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!
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.
Server Core is a full rewrite of our Server SDKs based on a shared, performance-focused Rust library. Today, we're launching the Rust library itself as a standalone, brand-new Rust SDK.
So far, we've launched Node, Java, PHP, Python, and Elixir Core SDKs. Each of those depends on the Rust core, which today we're launching as its own SDK. Given its usage across numerous languages, we've invested in performance optimization of this Rust library, with substantial improvements vs. our original Rust SDK. In addition, Rust Core has new-to-Statsig features like Parameter Stores, Contextual Multi-Armed Bandits, and more.
Rust core is available on crates - read the docs to get started!
Quantifying your impact is critical. Statsig's Insights page provides you a clear view of how experiments impact a metric of interest. It allows you to focus on a single metric, identify which experiments are driving it most and estimate the aggregated impact. You can filter down to a team, or a tag. This is particularly useful to understand your team’s impact or set a reasonable goal for a future period.
You navigate to the Insights section on the Statsig console, or the insight tab for each metric, to check it out. Learn more here.
Want to monitor new sign-ups? Want to be alerted if feature performance is slow or a page fails to load? Topline Metric Alerts allow you to monitor a metric's topline value, independent of any Feature Gate or Experiment Rollout.
To configure a Topline Alert, head to Analytics -> Topline Alerts tab where you can find all your Topline Alerts and configure new ones. Choose the event you want to alert on and the aggregation you're most interested in, then craft a notification message and configure alert subscribers. To learn more, check out our docs here!
Note: this feature is in Beta and not broadly available (yet!) Please reach out to us if you'd like to be opted in for early access.
We're excited to start rolling out support for Informed Bayesian. This new feature allows you to integrate meaningful priors into Bayesian analyses, leading to faster insights and smarter decision making.
Reach out in Slack to opt your team in to this.