added CUPED and outliers info

docs/platform-release-notes/2026/01.mdx

@@ -0,0 +1,66 @@
+# January 2026
+
+## Overview  
+This release deepens our investment in **metrics governance** while also bringing key performance improvements to the 
+experiment experience. With this update, managing, discovering, and using metrics becomes faster, more transparent, 
+and better integrated into your workflow.
+
+---
+
+## Metrics
+
+### Metric Catalog in Main Menu  
+The **metric catalog** is now accessible directly from the main menu, reflecting the central role metrics play across the platform.
+
+### Improved Metric Catalog Search  
+We've upgraded the metric list to a full **catalog** with enhanced search and filtering, matching the experience already available in experiment creation.  
+This makes it easier for:
+- **Metric owners** to manage and maintain their metrics  
+- **Experimenters** to find the right metrics for their experiments
+
+### Variance reduction with CUPED  
+**[CUPED (Controlled-experiment Using Pre-Experiment Data)](/docs/web-console-docs/goals-and-metrics/metrics/variance-reduction-cuped)** 
+is a well-known variance reduction technique that makes metrics more sensitive by leveraging pre-experiment data about users. 
+It allows you to detect smaller effects with the same sample size, or reach statistical significance faster with fewer users.
+
+In ABsmartly you can now : 
+- Easily enable CUPED for **new metrics**, or by creating a **new version** of an existing metric  
+- Choose a lookback period between 1 and 4 weeks
+- Enjoy a shorter time to decision
+
+### Metric Duplication  
+You can now **duplicate metrics** with a single action, no need to re-enter definitions by hand. 
+Ideal when creating a variation of an existing metric.
+
+### Draft & Active Status  
+Manage your metrics' lifecycle more clearly:  
+- All new metrics are created in **draft** by default  
+- **Draft** can be edited without restrictions
+- **Draft** metrics cannot be added to experiments
+- Once a metric is **made active** it becomes targetable and can be used in experiments
+- All existing metrics will be made **active** by default so you can keep using all your existing metrics
+
+This is the first step toward the upcoming **metric approval workflow**, 
+which will allow greater control over metric governance in the next release.
+
+---
+
+## Experiments
+
+### Faster Experiment Overview  
+We've added **caching** to improve the performance of the experiment overview page, 
+especially for experiments with large datasets.
+
+### Data Freshness Indicators  
+Each experiment now includes:
+- A **data freshness indicator** to help you understand how recent the data is  
+- A **force refresh button** so you can manually update results when needed
+
+### Graph Improvements  
+We've improved overall **graph responsiveness and rendering speed**, giving you a smoother experience when navigating and interpreting results.
+We've also improved the histogram graph so now the buckets match making comparison between variants much easier.
+
+---
+
+## Questions or Feedback?  
+We're always happy to help, so reach out if you have any questions or want to explore how to make the most of these new capabilities.

docs/web-console-docs/experiments/creating-an-experiment.mdx

@@ -219,6 +219,10 @@ Below are a few examples of what Primary, Secondary and Guardrail metrics could
 | **Health & Fitness App** | `Active Days Per Week`      | `Workout Completion Rate`,`App Session Length`                      |`App Uninstall Rate`,`App Crash Rate`,`Support Ticket Rate`  |
 
 
+:::note
+Cannot find your metric in the list? Make sure it is `active` as `draft` metrics cannot be added to experiments.
+:::
+
 ## Analysis
 
 In this step, you can choose and set up the analysis type you want to use.

docs/web-console-docs/goals-and-metrics/metrics/create.mdx

@@ -90,6 +90,11 @@ That timestamp is the reference point for all time-based filters.
 A time filter lets you include only the goal events that happen within a defined window after that first exposure. 
 This ensures your metric measures behavior in a controlled, meaningful period (for example, `purchases within the first hour` or `engagement in the first 7 days`).
 
+:::caution
+If your metric uses [CUPED](variance-reduction-cuped), the lookback window must incorporate the time filter.
+For example, if your metric measure `conversions achieved 2 weeks after exposure` then the CUPED lookback window must be at least 2 weeks.
+:::
+
 ### Outliers
 
 Outlier limits help control the influence of extreme metric values. 
@@ -114,6 +119,10 @@ This method filters values based on chosen quantiles.
 You define a lower quantile and an upper quantile (for example, 0.05 and 0.95). 
 Values below the lower quantile or above the upper quantile are capped to the limit.
 
+:::info
+Ouliers capping is done using the full experiment population, so all variants have the same capping limits.
+:::
+
 How it behaves with the sample purchase event:
 ```
 If you set:
@@ -142,6 +151,10 @@ The limits are calculated as:
 
 Any value outside these limits is capped to the value of the boundary.
 
+:::info
+Ouliers capping is done using the full experiment population so all variants have the same capping limits.
+:::
+
 Example with the sample purchase event:
 Suppose across all purchases:
 
@@ -329,6 +342,12 @@ There are five options:
 
 Replacement relations allow the metric to reflect up-to-date values when items are swapped or reissued.
 
+
+## Variance reduction
+
+If applicable, you can enable [CUPED](variance-reduction-cuped) for this metric to help reduce variance. 
+Enable [CUPED](variance-reduction-cuped) for this metric by checking the checkbox and choose a lookback period which matches the user behaviour.
+
 ## Format, scale and precisions
 
 This section controls how your metric’s **Value** and **Mean** are displayed in the results table. 

docs/web-console-docs/goals-and-metrics/metrics/metric-types/retention.mdx

@@ -40,6 +40,11 @@ Then the metric counts users who satisfy:
 - they completed the initial purchase
 - and they completed another purchase after 7 days, within the configured retention window
 
+
+:::caution
+If your metric uses [CUPED](../variance-reduction-cuped), the lookback window must be equal or larger than the retention period.
+:::
+
 **More examples**
 
 - `Checkout Recovery (24 hours)`:

docs/web-console-docs/goals-and-metrics/metrics/overview.mdx

@@ -59,7 +59,7 @@ Exploratory metrics are often used in post-analysis and are a great source of in
 
 ### Purpose
 A metrics can be described as a **business** metric, a **behavioural** metric or an **operational** metrics.
-Those attributes describe the purpose of the metric, what it is measuring.=
+Those attributes describe the purpose of the metric, what it is measuring.
 
 #### Business 
 In experimentation, **business** metrics refers to metrics measuring the impact of a change on a business KPI. Business metrics are often used as primary and/or guardrail metrics. 
@@ -150,7 +150,7 @@ Examples:
 
 Metrics are **version-controlled** to ensure that your experiment results remain stable, interpretable, and historically accurate. 
 When a metric definition changes, its meaning changes and that can impact how past and ongoing experiments would be understood. 
-To prevent this, changes to certain fields require a new version of the metric to be created.
+To prevent this, changes to certain fields of an `active` metric require a new version of the metric to be created.
 
 Versioning ensures that:
 
@@ -172,7 +172,7 @@ While past and current experiments can make use of an older version of a metric,
 Each metric contains configuration fields that play different roles in versioning. 
 To balance flexibility with historical accuracy, fields fall into three categories:
 
-### Editable and shared across all versions
+### Fields editable and shared across all versions
 
 These fields belong to the metric itself, not to a specific version.
 If you edit them, the change applies to every version of the metric.
@@ -204,6 +204,19 @@ Locked fields include:
 
 Locking these fields ensures that metrics remain stable and reproducible over time, and that historical experiment results never change unexpectedly.
 
+## Metric lifecycle
+
+When a new metric, or a new version of a metric, is created, it is automatically created as a `draft`. 
+`Draft` metrics cannot be added to experiments and first need to be made active to be discoverable by experimeters.
+
+While `draft` metrics can be edited at will, editing an `Active` metric is limited and will often require a new version of the metric to be created.
+
+A metric can be made active by clicking on the `Make Active` button on the metric's dashboard.
+
+:::info
+Metric builder can easily find all their draft metrics by selecting `draft` in the `Status` filter of the Metrics Catalog.
+:::
+
 ## Ownership & permissions
 
 `Metrics` are Managed-Assets and, as such, follow a specific [ownership model](/docs/web-console-docs/users-teams-permissions/ownership-and-permissions).

docs/web-console-docs/goals-and-metrics/metrics/variance-reduction-cuped.mdx

@@ -0,0 +1,122 @@
+---
+sidebar_position: 5
+---
+
+# Variance Reduction with CUPED
+
+## What is CUPED?
+
+CUPED (Controlled-experiment Using Pre-Experiment Data) is a variance reduction technique that makes metrics more sensitive by leveraging pre-experiment 
+data about users. It allows you to detect smaller effects with the same sample size, or reach statistical significance faster with fewer users.
+
+In A/B testing, users exhibit natural variability in their behavior before any treatment is applied. 
+Some users inherently spend more, engage more, or convert more than others. 
+This pre-existing variability creates statistical "noise" that makes it harder to detect the true effect of your changes. 
+CUPED reduces this noise by adjusting for users' baseline behavior, effectively isolating the treatment effect.
+
+## How CUPED Works
+
+CUPED uses a covariate—typically the same metric measured during a pre-experiment period—to adjust each user's post-experiment metric value. The adjustment accounts for how each user performed relative to the average before the experiment started.
+
+The core adjustment formula is:
+```
+Adjusted Metric = Raw Metric - θ × (Pre-experiment Metric - Average Pre-experiment Metric)
+```
+
+Where:
+- **Raw Metric**: The user's observed value during the experiment
+- **Pre-experiment Metric**: The same metric measured before the experiment
+- **θ (theta)**: An optimal coefficient estimated from pre-/post-experiment data (often Cov(pre, post) / Var(pre))
+
+The adjusted values maintain the same average (mean) as the raw values but have reduced variance, making treatment effects easier to detect.
+
+## When CUPED is Most Effective
+
+CUPED provides the greatest benefit when:
+
+1. **High correlation between pre and post metrics** (correlation ≥ 0.3)
+   - Revenue metrics typically show correlation of 0.5-0.7
+   - Engagement metrics often show correlation of 0.4-0.6
+   - Conversion metrics may show lower but still useful correlation
+
+2. **Sufficient pre-experiment data is available**
+   - Minimum: 7-14 days of historical data
+   - Recommended: 2-4 weeks for stable baseline estimates
+   - The pre-period should reflect normal user behavior
+   - In ABsmartly, you can choose between, 1, 2, 3 or 4 weeks with 2 weeks being the default
+
+3. **Metrics with high natural variance**
+   - Revenue per user (some users spend much more than others)
+   - Session counts (power users vs. casual users)
+   - Time-based engagement metrics
+
+## Practical Examples
+
+### Example 1: Revenue Optimization
+
+You are testing a new checkout flow where the primary metric is `revenue per user`.
+
+**Without CUPED:**
+- User A: Spent $100/month historically → Spends $110 during test
+- User B: Spent $20/month historically → Spends $25 during test
+- Both show increases, but is it the treatment or natural variance?
+
+**With CUPED:**
+The algorithm adjusts for their baseline spending patterns. 
+If both users increased proportionally beyond their historical baseline, CUPED isolates this treatment effect from their pre-existing spending behavior, 
+giving you higher confidence the change drove the increase.
+
+**Result:** You might detect the effect 30-40% faster or with 30-40% fewer users.
+
+### Example 2: Engagement Metrics
+
+Testing a new feed algorithm where your metric is `sessions per week`.
+
+**Without CUPED:**
+- High natural variance between power users (10+ sessions/week) and casual users (2 sessions/week)
+- Treatment effects are masked by this user heterogeneity
+- Requires 100,000 users to reach significance
+
+**With CUPED:**
+- Algorithm adjusts for each user's historical session frequency
+- Can detect the same effect with ~65,000 users
+- Or detect a smaller 2% improvement that would have been undetectable before
+
+### Metric Compatibility
+
+CUPED works best with:
+- **Continuous metrics**: Revenue, time spent, count metrics
+
+CUPED is less effective for:
+- Metrics without meaningful pre-experiment analogs
+- Completely novel user behaviors introduced by the treatment
+- Metrics where pre- and post-experiment correlation is very low
+
+### Statistical Validity
+
+- **Bias-free**: CUPED does not bias your estimates—it only reduces variance
+- **Conservative**: If pre-experiment data doesn't correlate, CUPED simply doesn't apply adjustment
+
+## Benefits of using CUPED
+
+1. **Faster decisions**: Reduce time to statistical significance by 30-50% on average
+2. **Cost efficiency**: Achieve the same statistical power with fewer users
+3. **Detect smaller effects**: Find wins that would otherwise remain hidden in the noise
+4. **Typically no downside**: CUPED is conservative; when correlation is weak, it usually offers little benefit but remains unbiased
+
+## CUPED and ABsmartly
+
+When creating a new metric or a new version of an existing metrics, you can enabled CUPED. 
+When CUPED is enabled for your metrics in ABsmartly:
+
+- Pre-experiment data already collected is automatically used 
+- The platform calculates optimal θ coefficients for each metric
+- Adjusted metrics are computed alongside raw metrics
+- Statistical significance calculations use the variance-reduced estimates
+- CUPED runs automatically in the background without requiring changes to your experiment setup or tracking implementation
+- When correlation is < 0.1, or when variance is greater than the threshold, ABsmartly uses the raw data
+
+## Further Reading
+
+- Original CUPED paper: [Deng et al., 2013 - "Improving the Sensitivity of Online Controlled Experiments by Utilizing Pre-Experiment Data"](https://exp-platform.com/Documents/2013-02-CUPED-ImprovingSensitivityOfControlledExperiments.pdf)
+- CUPED at booking.com: [Simon Jackson, 2018, "How Booking.com increases the power of online experiments with CUPED"](https://booking.ai/how-booking-com-increases-the-power-of-online-experiments-with-cuped-995d186fff1d)