## How Lifecycle charts work

Lifecycle charts in Metrics Explorer help you understand how usage changes over time by classifying your unique units (for example user\_id) within each time interval based on whether they used an event recently, returned after a gap, continued to use the event, or churned.

{% figure %}
![Life Cycle chart interface in Metrics Explorer](/images/product-analytics/lifecycle/lifecycle_demo_v4.png)
{% /figure %}

### Use cases

* **Track new-user ramp after a launch:** See whether adoption is growing week over week after shipping a new feature.
* **Monitor churn and reactivation:** Understand whether users are falling off and whether they return.
* **Compare retention health across releases:** Spot changes in retention and reactivation patterns over time.

## Creating a lifecycle chart

### Step 1: Choose an event (or a compatible metric)

Decide whether to use a metric or an event. Lifecycle is designed for a single underlying event and count-style metrics composed of a single count-type event.

### Step 2: Choose your unit (unique units)

Select what you want to count uniquely (for example user\_id, stable\_id, or another unit). The chart reports how many unique units fall into each category for each time interval.

### Step 3: Choose your interval (granularity)

Pick an interval unit (day / week / month) and a number of intervals per bucket (1–48). Each bar on the chart represents one interval bucket, and the chart shows one data point per bucket across the selected date range (max 1 year).

{% figure %}
![Life Cycle chart interface in Metrics Explorer](/images/product-analytics/lifecycle/lifecycle_interval_selector.png)
{% /figure %}

### Step 4: (Optional) Add filters

Apply filters to focus on a specific segment (for example platform, country, app version, or a feature-related property).

## Interpreting your lifecycle chart

Each interval bucket classifies unique units into four categories (mutually exclusive):

**New:** Used during this interval and didn't use at any point earlier within the lookback window (up to 1 year before this interval).

**Resurrected:** Used during this interval, didn't use in the immediately previous interval, and used earlier within the lookback window.

**Recurring:** Used during this interval and the immediately previous interval.

**Dormant:** Used in the immediately previous interval, but not during this interval (Statsig displays this below the axis to make churn visually apparent).

### Reading the chart

**X-axis:** Time, grouped into your chosen interval buckets.

**Y-axis:** Count of unique units.

**Stacked bars:** Show how the composition of usage changes over time (new vs. resurrected vs. recurring), while the dormant component highlights drop-off between adjacent intervals.