> For the complete documentation index, see [llms.txt](https://userflux.gitbook.io/userflux-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://userflux.gitbook.io/userflux-docs/feature-guides/analytics/insights/series.md).

# Series

## Overview

Series lets you chart any metric (event totals, unique users, sums, averages) over time so you can spot trends, seasonality and release impacts. It’s the view you’ll open when the question starts with “**How is X changing over days, weeks or months?**”

***

## Common Use Cases

* **Feature Usage Over Time**: Track how often a specific feature or event is used (e.g. “Button Clicked” event) each day or week to understand engagement trends.
* **User Activity Trends**: Monitor key user metrics like daily active users, sign-ups per day, or purchases per week to see growth, retention, or seasonal patterns.
* **Comparing Metrics**: Plot multiple events together – for example, compare daily sign-ups vs. daily upgrades on the same chart to see how one metric correlates with another.
* **Release Impact**: Before-and-after analysis for a product change or marketing campaign (e.g. track weekly usage before and after a new feature launch).
* **Goal Tracking**: Measure progress toward targets over time, such as reaching a certain number of monthly active users or transactions in a quarter.

***

## Interface Walkthrough

| Area                      | What to do                                                                                                     |
| ------------------------- | -------------------------------------------------------------------------------------------------------------- |
| **Datasets A, B, …**      | Pick event(s) → choose metric (*Total count*, *Unique users*, etc.) → optional per‑dataset filters and rename. |
| **Global Filters**        | Rules that apply to *all* datasets (e.g. Country = US).                                                        |
| **Date Range & Interval** | Select window (“Last 30 days”, “Custom”) and group by day / week / month; set the timezone.                    |
| **Breakdown by**          | Split a dataset into multiple series by a property (Plan, Device, Country).                                    |
| **Run / Save**            | Execute the query; save it for later.                                                                          |

***

## Quick Example (sign‑ups vs purchases, last 30 days)

1. Dataset A → event Sign Up, metric *Total count*.
2. Dataset B → event Purchase Completed, metric *Total count*.
3. Date Range → *Last 30 days*, grouped by *day*.
4. (Optional) Global filter Country = “AU”
5. Run → two lines appear—daily sign‑ups and purchases. Hover to inspect exact numbers;


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