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➕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!
Extending Templates to Dynamic Configs
Over the last couple of months, we have seen an influx in the usage of our Dynamic Configs product. We heard from our customers that they would like to create templates for Dynamic Configs that can be re-used by your team or organization. Templates have always existed on Statsig for Feature Gates and Experiments, and now we have extended this feature to Dynamic Configs as well!
Quick context on Dynamic Configs
Dynamic Configs is a tool used to change application settings in real-time without requiring a restart or redeployment of the application. This allows developers to control operational settings like performance tuning or scaling resources or other configurations on the fly.
Templates
Templates enable you to create a blueprint for Dynamic Configs to enable standardization and reusability across your project. Templates can help enforce a standard practice, or make it easy for new configs to get up & running. Templates can be enforced at the Org (via Organization Settings and Role-based Access Controls) or at the Team-level.
Introducing Open-Beta SCIM Support on Statsig
We're excited to announce the beta launch of SCIM (System for Cross-domain Identity Management) support on Statsig! This initial release focuses on seamless integration with Okta for efficient user provisioning and role assignment into your Statsig projects. For more information visit, Statsig Docs.
This is an Enterprise-only feature. If you would like to enroll in the open beta and enable SCIM for your organization, please reach out to us!
Key Features
User Provisioning: Automatically create and manage user accounts in Statsig based on Okta identities
Role Assignment: Easily assign and manage user roles through Okta, ensuring consistent access controls
Benefits:
Streamlined User Management: Simplify the onboarding process with automated account creation and updates.
Enhanced Security: Centralized identity governance reduces the risk of unauthorized access by ensuring accurate role assignments.
Improved Efficiency: Save time and reduce errors with automated workflows, allowing your team to focus on higher-priority tasks.
Scalability: Easily manage user identities as your organization grows, without the hassle of manual interventions.
This enhancement streamlines user management and improves security by centralizing identity governance. Stay tuned for more updates as we expand SCIM support in the future!
Warehouse Native Compute Transparency Dashboard
Statsig Warehouse Native now lets you get a birds eye view across the compute time experiment analysis incurs in your warehouse. Break this down by experiment, metric source or type of query to find what to optimize.
Common customers we've designed the dashboard to be able to address include
What Metric Sources take the most compute time (useful to focus optimization effort here; see tips here)
What is the split of compute time between full loads vs incremental loads vs custom queries?
How is compute time distributed across experiments? (useful to make sure value realized and compute costs incurred are roughly aligned)
You can find this dashboard in the Left Nav under Analytics -> Dashboards -> Pipeline Overview
This is built using Statsig Product Analytics - you can customize any of these charts, or build new ones yourself. A favorite is to add in your average compute cost, so you can turn slot time per experiment into $ cost per experiment.
🥇 First-Value metrics
Alongside latest value metrics, we’re also announcing First-Value metrics. These allow you to see the value from the first record the user logged while exposed to an experiment. Imagine being able to track first purchase value, first subscription plan price, or first-time time-to-load on a new page.
Learn more in our documentation
🔍 Focused Analysis with Top Group Limits
Use Case
Imagine you’re analyzing user behavior across segments like browser types, referral sources, or search terms. Applying a group-by might return a long list of groups, many with minimal impact on your metrics. This volume of data can make it difficult to focus on the most significant segments.
Why It’s Important
By setting a limit on the number of groups displayed, you can reduce clutter and concentrate on the segments that matter most. This helps you avoid distractions from less impactful data points and enables you to focus on meaningful insights that can inform your decisions.
What It Does
When applying a group-by in your charts, you can now specify a limit on the number of groups returned. The groups are sorted by the highest value of the metric you’re analyzing, so you’ll see only the top-performing segments. To use the feature click the "..." in the group-by section and select "Add Group-By Limit"
Power Analysis attached to Experiments
Power Analysis is critical input into experiment durations when you care about trustworthy experiments. When you perform Power Analysis for an experiment, the analysis is now automatically attached to the experiment and available to other reviewers. When you start Power Analysis for an experiment, we'll prepopulate any Primary Metrics you've already configured on the experiment.
This feature is rolling out to Statsig Cloud and Warehouse Native customers over the next week.
Experiment Setup Screen
Starting Power Analysis from an Experiment
🪵🪄 Log transforms
We’re adding the ability to log-transform sum and count metrics, and measure the average change to unit-level logged values.
Log Transforms are useful when you want to understand if user behavior has generally changed. If a metric is very head-driven, even with winsorization and CUPED the metric movement will generally driven by power users.
Logs are multiplicative, so a user going from spending $1.00 to spending $1.10 is the same “metric lift” as another going from $100 to $110. This means that what they measure is closer to shifts in relative distribution, rather than topline value.
Because of this divorce from “business value,” log metrics are usually not good evaluation criteria for ship decisions, but alongside evaluation metrics, they can easily provide rich context on the change in the distribution of your population.
By default, the transform is the Natural Log, but you can specify a custom base if desired.
Learn more in our documentation.
⌚💰📊 Latest value metrics
We launched latest value metrics for user Statuses in March this year, and just extended support to numerical metrics. This will be useful for teams that want to track how experiments impact the “state” of their userbase.
You could already track subscription status, but now you can track users’ current balance, lifetime spend, or LTV - without duplicating the data across multiple different days. Each day in the pulse time-series will reflect the latest value as of that day.
Learn more in our documentation.
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