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Product Stickiness DAU / MAU Engagement Ratio

Scenario

Venture capital investors benchmark software engagement using the DAU/MAU ratio: the number of distinct daily active users on a benchmark day ('2024-03-31') divided by the total monthly active users in that 30-day window.

Task
Analyze logins in `app_login_activity`. Calculate: 1. `dau`: Count of distinct `user_id`s active on `login_date = '2024-03-31'` 2. `mau`: Count of distinct `user_id`s active throughout the month (all records) 3. `stickiness_pct`: `ROUND(100.0 * dau / mau, 1)` Return in a single summary row.
Expected Output Format

Single row summary showing DAU, MAU, and stickiness percentage.

Input Tables & Sample Data0 tables
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About the “Product Stickiness DAU / MAU Engagement Ratio” SQL Problem

Business Scenario

Venture capital investors benchmark software engagement using the DAU/MAU ratio: the number of distinct daily active users on a benchmark day ('2024-03-31') divided by the total monthly active users in that 30-day window.

Expected Output & Criteria

Single row summary showing DAU, MAU, and stickiness percentage.

Database Table Schema (PostgreSQL DDL)

PostgreSQL 16 WASM

This challenge executes directly against the following relational schema in your browser:

CREATE TABLE app_login_activity (
        login_id INT PRIMARY KEY,
        user_id INT NOT NULL,
        login_date DATE NOT NULL
      );

Target SQL Clauses & Operators

COUNTROUND

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