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 Atfalls within the window for every org. There is no historical backfill of older accounts.Note:
Plan Typecan change over time for the same org (e.g., COMMUNITY → SECOPS after an upgrade). There are 179 such transitions in the dataset.
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
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)