How to calculate click-to-open rate in SQL

What you need

Click-to-open rate (CTOR) is the percentage of people who opened an email and then clicked a link in it: unique clicks divided by unique opens, times 100. You need these columns, here from the sample file monthly.csv:

• email_opened: emails opened
• email_clicked: emails clicked

CREATE TABLE monthly (
  month TEXT,          -- 'YYYY-MM'
  email_opened INTEGER,
  email_clicked INTEGER
);

Step by step

1. Load monthly.csv into a table called monthly. The table only needs the month column and the columns below.
2. Run the first query to get one row per month.
3. Run the whole-period query. It adds up each column first and divides once; averaging the monthly results gives a different, usually wrong, answer.

The queries

SELECT
  month,
  ROUND(100.0 * email_clicked / NULLIF(email_opened, 0), 2) AS ctor
FROM monthly
ORDER BY month;

SELECT ROUND(100.0 * SUM(email_clicked) / NULLIF(SUM(email_opened), 0), 2) AS ctor
FROM monthly;

What you should get

Run against the sample data, the code above returns these values (in percent):

6,650 ÷ 53,130 × 100 = 12.52%

Notes for SQL

• Integer columns divide as integers in PostgreSQL, SQLite and SQL Server, so 3 / 4 gives 0. Multiplying by 1.0 (or 100.0) first makes the division decimal.
• NULLIF(x, 0) turns a zero denominator into NULL, so the query returns NULL for that row instead of failing with a division-by-zero error.
• Tested on SQLite and DuckDB. SUM, NULLIF and ROUND work the same in PostgreSQL, MySQL and BigQuery; SQL Server uses TOP instead of LIMIT where a query has one.

Where click-to-open rate misleads

• Use unique counts on both sides.
• Low opens can make CTOR jumpy; check the counts behind it.
• It does not measure revenue; follow clicks through to orders where you can.

The full explanation, with a calculator, is on the Click-to-open rate (CTOR) page.

Same metric, other tools

• Click-to-open rate in Excel and Google Sheets
• Click-to-open rate in Python (pandas)

Related metrics in SQL

• How to calculate email open rate in SQL
• How to calculate email click-through rate in SQL

Learn more about SQL

• Python and SQL for data analysis, side by side — 3 min read
• How to find out what changed in your sales data — 2 min read

About the data on this page

The figures come from a small made-up dataset, printed above in full so you can check every number by hand. Download it as monthly.csv. The results are computed from those rows when the page is built, and the Excel formulas, SQL and Python on these pages are run against the same rows before publishing, so the code and the answers agree.

To get these numbers from your own data without writing formulas, try AnalyzeIt: ask in plain words and it shows the working behind every figure.