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📡 IPV6 Support
Starting today (Dec 9th, 2024) Statsig will begin support for auto-resolving metadata from IPV6 domains on our client SDKs. Statsig has historically provided and used our own package (IP3Country) for resolution of IP addresses to country codes, which we've decided to stop relying on as IPV6 traffic continues to grow. Going forward, we'll leverage our load balancer's country resolution, which will provide more accurate IPV4 support, and fulsome IPV6 support.
Visit our docs on the transition for more info, or reach out to us in Slack!
Warehouse Native Experimentation on Athena
Athena is now a supported data warehouse for Warehouse Native Experimentation! We've unlocked the same capabilities available on Snowflake, BigQuery, Redshift and Databricks to Athena users too.
You can reuse existing events and metrics from Athena in experimental analysis. You can also use typical Statsig features - including Incremental reloads (to manage costs), Power Analysis using historic data, and even use features like Entity Properties to join categorical information about users and use them in analysis across experiments!
➡ Horizontal Bar Charts for Grouped Data
Your data tells clearer stories when you can see how different groups stack up. We've added horizontal bar charts in Metrics Explorer to make these comparisons easy and intuitive.
What You Can Do Now
Compare metrics across any business dimension (time periods, segments, categories)
Track usage patterns by user type, location, or platform
Spot trends in any grouped data, from engagement to transactions
How It Works Apply a Group By to your data, and select the horizontal bar chart option. The chart automatically adjusts to show your groups clearly.
Impact on Your Analysis This visualization makes it simple to:
Identify your top and bottom performers instantly
Handle longer label names easily
Share clear comparisons in your reports
Start turning your grouped data into visual insights today.
Autoscaling on Snowflake (Warehouse Native)
You can now connect multiple Snowflake warehouses to your account, enabling better query performance by automatically distributing query jobs across all available warehouses. To set it up, you can head over to Settings > Project > Data Connection, and select Set up additional Warehouses.
When you schedule multiple experiments to be loaded at the same time, Statsig will distribute these queries across the provided warehouses to reduce contention. Spreading queries across compute clusters can often be faster and cheaper(!) when contention causes queries to be backed up.
We have a beta of intelligent Autoscaling in works. Reach out in Slack if you'd like to try it!
Experiment Compute Summary
Following up from the Statsig project level compute summary, we've also added an experiment level compute summary - available in Experiment Diagnostics. Out of box it lets you look at compute utilization by job type or metric source. This is helpful to isolate situations where a low value metric is a disproportionate share of compute utilization. When you find this, look at our guide to optimize costs.
🅰️ Angular Support
Statsig's Javascript SDK now has out-of-the-box support for Angular with the release of our Angular bindings. While we've long helped customers setup Angular in our Slack Community, this release includes bindings and suggested patterns for both App Config and App Module integrations. Along with some angular-specific features like directives, this supports all of the bells and whistles you expect from Statsig's SDKs: feature flags, experiments, event logging, and more. Try it out, and let us know if you find any wrinkles as we roll out support. Get started with our Angular docs or simply run: npm install @statsig/angular-bindings
👁️ Updated Single Value Views in Metric Drilldown and Dashboards
Use Case When you need a quick, at-a-glance summary of a key metric, having a single, prominent value can provide immediate insight. Whether you’re monitoring yesterday’s user sign-ups or the total revenue over the past month, a headline figure helps you stay informed without diving into detailed charts.
Why It’s Important
Single Value views allow you to focus on the most critical data points instantly. This feature is especially useful on dashboards, where quick visibility into key metrics supports faster decision-making and keeps your team aligned on important performance indicators.
The Feature: What It Does
You can now directly select Single Value as a widget type when adding items to your Dashboards, making it easier to showcase key metrics prominently without additional configuration.
In addition, within Metric Drilldown, you can choose the Single Value view to display your metric as a headline figure. This feature offers:
Latest Full Data Point: View the most recent complete data point (e.g., yesterday’s total sales or user activities).
Overall Value for Time Range: See the cumulative or average value over the entire selected time range, providing a broader perspective on your metric.
Comparison Options: Select a comparison period to see absolute and percentage changes over time, helping you understand trends and growth.
By incorporating Single Value views into your dashboards and analyses, you can highlight essential metrics at a glance, enabling you and your team to stay updated with minimal effort.
🧐 SDK Observability Integrations
As Statsig comes to power your product's features, experiments, metrics and more, observing our SDK's performance can become increasingly important. Starting with our Python SDK, we've built an interface for you to consume your SDK's performance statistics and ingest them into your platform of choice (like Datadog).
Initial support will include the metrics:
statsig.sdk.initialization - which tracks SDK initialization durationstatsig.sdk.config_propagation_diff - which measures the difference between a config's update time and when it reaches your SDK, andstatsig.sdk.config_no_update - which tracks occurrences where there are no config updates.
If you'd prefer to not integrate with your SDKs, similar data is available in the Statsig console alongside your SDK Keys & Secrets. If you'd like to see support for this in another SDK, let us know in Slack!
➕Cumulative Sum Rollups in Metric Drilldown
Use Case
When analyzing event data, you often need to understand the cumulative impact of your metrics over time. For example:
“How many times has this feature ever been used?
“How many distinct people have ever used this feature?”
“What is the total revenue generated up to this point?”
Why It’s Important
Viewing metrics as a cumulative sum provides valuable insights into long-term trends and overall growth. It helps you track feature adoption, user engagement, and total impact over time, enabling more informed decision-making.
The Feature: What It Does
In Metric Drilldown, after selecting an event and choosing an aggregation method—such as Event Count, Uniques, Average of Property Value, etc.—you can now apply the Cumulative Sum option to your results. This feature accumulates your selected metric over time, providing a running total in your charts.
When the metric aggregation is set to Uniques, you have two options for calculating the cumulative sum:
Distinct Uniques
What it does: Counts each unique user or unit only once in the cumulative total, regardless of how many times they appear in subsequent time periods.
Use Case: Answers “How many distinct people have ever used this feature?” by providing a deduplicated cumulative count.
Total Uniques
What it does: Counts each occurrence of a user or unit every time they appear, allowing them to be counted multiple times in the cumulative total.
Use Case: Helps you understand “What is the total number of unique user engagements over time, including repeat users?” This provides insight into recurring user activity across different periods.
For other aggregation types:
Event Count: The cumulative sum shows the total number of events over time, helping you track overall engagement.
Average of Property Value: Accumulates average values over time, useful for metrics like cumulative revenue or total session duration.
Sum of Property Value: Accumulates the sum of a chosen property value from your events, useful for questions like "What is the total revenue generated up to this point?" or “What is the cumulative sum of this property over time?” by providing the total accumulated value.
By enabling the Cumulative Sum option, you can transform your metric analyses to capture total impact over time, providing a comprehensive view that supports deeper insights into your product’s performance.
Retention Metrics on Statsig Warehouse Native
Statsig Cloud launched with user accounting metrics - including retention. We’re now matching this capability with highly flexible Retention Metrics in Warehouse Native. For insight into why we think this matters, check out our blog post!
Retention metrics allow you to calculate the rolling daily retention from one event/user-day status to itself - or another, if desired. The time window retention is measured in is fully customizable - for example, you can measure the % of users that retain into the last 3 days of the next week, exactly 14 days from now, or any time in the next two weeks.
This allows you to directly track if features designed to make your product more interesting, enjoyable, or stickier over time are working, instead of trying to divine this from some combination of “DAU” and “users active at 7/14/28 days from exposure”.
This class of metrics is critical for growth teams focused on growing their userbase; Lenny’s Newsletter published a fantastic piece on how Duolingo used retention metrics to measure and drive long-term install and revenue growth.
Check out the docs, and try it out in Warehouse Native today!
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