# Retention Insights

**Retention** insights plot classic cohort retention: users or other entities who did a **starting event** in a period, then whether they performed a **returning event** again in later time buckets. Use them to see whether new signups, activated users, or any cohort keep engaging over days or weeks.

**Best for:**

- Onboarding and activation ("did they come back after signup?")
- Habit and repeat usage ("after first purchase, when do they return?")
- Comparing cohorts side by side with **Segmented by** filters

**Example questions:**

- "What share of signups used the product again on day 1, 7, and 30?"
- "After a user's first key action, how does return behavior differ by plan or channel?"

## Creating Retention Insights

1. Open **Insights** and create or edit a chart.

2. In the insight type control, choose **Retention**. The control is labeled "Retention insight" when selected.

3. Configure **Starting event**: the action that puts users into the cohort, for example sign up or first purchase.

4. Configure **Returning event**: the action that counts as "retained", often the same event as the starting event or a deeper engagement event.

5. Set **Measured as**:

   - **Return on or after**: a user counts as retained in a bucket if they performed the returning event on that day or any later bucket in the window.
   - **Return on**: retained only if they performed the returning event in that specific bucket.

6. Set **Calculated by**:

   - **24-hour windows**: periods are aligned in 24-hour windows from the cohort start.
   - **Strict calendar day**: periods follow calendar days, which can differ from rolling 24-hour logic.

7. Optionally add **Segmented by** segments to compare multiple cohort curves on the same chart.

8. Use **Query variables** when your chart defines them, such as date range or interval, so the preview matches the period you care about.

The preview shows a cohort retention visualization. Use the **Results** tab for the table and **SQL** to inspect the generated queries.

![Retention insight preview: cohort retention curve over day offsets](/screenshots/charts-retentionchart--single-cohort.png)

*Retention insight preview: cohort retention curve over day offsets*

## How this differs from segmentation

[Segmentation insights](/docs/query-data/product-analytics/segmentation.md) compare metrics and breakdowns over time. **Retention** insights are optimized for **cohort × offset** curves from exactly two events plus retention-specific measurement rules. Use segmentation when you are comparing groups on a single metric; use retention when the question is explicitly "of people who did X, what percentage did Y again after N days?"

## Practical notes

- You must select **both** starting and returning events before the chart can run; the UI prompts you until both are set.
- Retention uses the same event-step builder as funnels, so you can filter each side of the definition by properties, traits, and segments.
- Numbers may not match other analytics products exactly, because **Measured as**, **Calculated by**, timezone, and event definitions all affect cohort math. Trends and relative comparisons should still be reliable when definitions are held constant.

## Related

- [Funnel insights](/docs/query-data/product-analytics/funnel.md): sequential steps and conversion, not cohort return over offsets
- [Segmentation insights](/docs/query-data/product-analytics/segmentation.md): time series and breakdowns
- [Product analytics: track](/docs/ingest-data/product-analytics/track.md): instrumenting the events retention depends on