# Case Study 1 — Acquisition Analysis

**Estimated time:** 30 min

## Background

CrowdSec Console is the web interface used by security teams to manage their CrowdSec deployments. Organizations register with a plan tier and accumulate daily activity on the platform. Understanding registration volume, engagement, and upgrade conversion is key to guiding growth decisions.

## Dataset

**File:** `../datasets/console_users_acquisition.csv.gz`

This is a **daily time-series** dataset: each row represents one organization on one calendar day, covering **2025-01-01 → 2025-06-01** (~22 weeks, 16 146 unique organizations).

### Field reference

| Column | Type | Description |
|---|---|---|
| `Date` | date | Calendar day of the observation (`YYYY-MM-DD`) |
| `Organization ID` | string (UUID) | Unique identifier for the organization |
| `Plan Type` | categorical | Subscription tier on that day: `COMMUNITY` (free, 99%), `SECOPS` (paid mid-tier), `ENTERPRISE` (paid top-tier) |
| `Industry Sector` | categorical | Self-reported industry (14 categories + ~2.4% missing). Examples: `PERSONAL_USE`, `IT_AND_SERVICES`, `HOSTING`, `MSSP`, `EDUCATION`, … |
| `Org Created At` | timestamp (ISO8601) | When the organization first registered — use this as the **acquisition date** |
| `N Signals` | integer | Security signals generated by this org on this day |
| `N Active Engines` | integer | CrowdSec engines actively sending telemetry on this day |
| `N Attached Engines` | integer | Engines registered (attached) to the org, active or not |
| `N Cti Queries` | integer | CTI (Cyber Threat Intelligence) API queries made on this day |
| `N Users Logged In` | integer | Number of users from this org who logged in on this day |

> **Important:** The dataset covers a **6-month observation window** (2025-01-01 → 2025-06-01) and contains **only organizations acquired during this period** — i.e., `Org Created At` falls within the window for every org. There is no historical backfill of older accounts.
>
> **Note:** `Plan Type` can change over time for the same org (e.g., COMMUNITY → SECOPS after an upgrade). There are 179 such transitions in the dataset.

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## Challenges

Analyze the dataset in order to extract key information that would help monitoring core marketing and product efficiency in terms of:
* acquisition : the number of new accounts created in number and quality
* engagement : how much the console use the features
* conversion : number of paying users
* churn : users leaving the console

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## Expected Output

- A document with analysis mixing charts, data and text. Be creative! You can use the tool of your choice (google doc, jupyter notebook, ... as you see fit)