function calculateChurnSQL() {
var start = document.getElementById('customersStart').value;
var end = document.getElementById('customersEnd').value;
var newCust = document.getElementById('newCustomers').value;
var tableName = document.getElementById('tableName').value;
// Default table name if empty
if (!tableName || tableName.trim() === "") {
tableName = "users";
}
// Validate numbers
if (start === "" || end === "" || newCust === "") {
alert("Please fill in all numerical fields (Start, End, and New Customers).");
return;
}
var s = parseFloat(start);
var e = parseFloat(end);
var n = parseFloat(newCust);
if (isNaN(s) || isNaN(e) || isNaN(n)) {
alert("Please enter valid numbers.");
return;
}
if (s <= 0) {
alert("Start customers must be greater than 0 to calculate a rate.");
return;
}
// Logic: End = Start + New – Lost
// Therefore: Lost = Start + New – End
var lost = s + n – e;
// Edge case: Negative lost (means data input error, end count is impossibly high)
if (lost 10) {
churnElem.className = "metric-value bad";
retElem.className = "metric-value bad";
} else {
churnElem.className = "metric-value good";
retElem.className = "metric-value good";
}
// Generate SQL Snippet
// This simulates a standard query to calculate churn based on typical 'status' and 'created_at' columns
var sql = "– SQL Query to calculate Churn Rate\n";
sql += "SELECT \n";
sql += " COUNT(CASE WHEN status = 'cancelled' AND cancellation_date BETWEEN '2023-01-01' AND '2023-01-31' THEN 1 END) AS customers_lost,\n";
sql += " COUNT(CASE WHEN created_at = '2023-01-01') THEN 1 END) AS start_customers,\n";
sql += " (COUNT(CASE WHEN status = 'cancelled' AND cancellation_date BETWEEN '2023-01-01' AND '2023-01-31' THEN 1 END) * 100.0) / \n";
sql += " NULLIF(COUNT(CASE WHEN created_at = '2023-01-01') THEN 1 END), 0) AS churn_rate\n";
sql += "FROM " + tableName + ";";
document.getElementById('sqlOutput').textContent = sql;
document.getElementById('churnResults').style.display = "block";
}
How to Calculate Churn Rate in SQL
Churn rate is a critical business metric that measures the percentage of customers who stop using your product or service during a specific time frame. While the calculator above provides a quick manual way to determine your churn percentage based on aggregate numbers, data analysts often need to compute this dynamically directly from the database using SQL.
The Churn Rate Formula
Before writing the SQL query, it is essential to understand the logic used. The standard formula for calculating churn rate is:
Churn Rate = (Customers Lost during Period / Customers at Start of Period) × 100
Where "Customers Lost" can be derived if you know your starting count, ending count, and new acquisitions: Lost = Start + New – End.
SQL Logic for Churn Calculation
To calculate churn rate using SQL, you typically need a table (e.g., subscriptions or users) containing at least:
A unique User ID.
A Start Date (signup date).
A Status (active vs. cancelled).
A End Date (cancellation date, if applicable).
Step-by-Step SQL Approach
Depending on your database dialect (PostgreSQL, MySQL, SQL Server), the syntax varies slightly, but the logic remains the same. Here is how you structure the query:
Define the Period: Determine the start and end dates for your analysis (e.g., the last month).
Count Start Customers: Count distinct users who were active strictly before the period began.
Count Lost Customers: Count distinct users who cancelled their service strictly within the period.
Compute the Ratio: Divide lost customers by start customers and multiply by 100.
Example SQL Query
If you are using PostgreSQL or MySQL, a common query to calculate the monthly churn rate looks like this:
SELECT
-- 1. Count users active at the very start of the month
COUNT(CASE
WHEN signup_date = '2023-10-01')
THEN 1 END) AS start_count,
-- 2. Count users who churned within the month
COUNT(CASE
WHEN cancellation_date >= '2023-10-01'
AND cancellation_date = '2023-10-01'
AND cancellation_date <= '2023-10-31'
THEN 1 END) * 100.0)
/
NULLIF(COUNT(CASE
WHEN signup_date = '2023-10-01')
THEN 1 END), 0) AS churn_rate_percentage
FROM users;
Common Pitfalls in SQL Churn Calculation
When writing these queries, ensure you handle the following edge cases:
Division by Zero: Always use NULLIF or a CASE statement in the denominator to prevent SQL errors if you have zero customers at the start.
Date Boundaries: Be careful with >= vs >. A customer cancelling on the first second of the month might be considered lost in the previous month or the current one depending on your business logic.
New Customers: Do not include customers acquired during the month in the "Start Count". Doing so dilutes the churn rate artificially.
Use the tool above to verify your SQL logic by comparing the manual calculation results with the output of your database queries.