In SQL, customer growth rate for the whole period is ROUND(100.0 * (SUM(customers_end) - SUM(customers_start)) / SUM(customers_start), 2) over the monthly table. On the sample data it returns 8.99.
Customer growth rate = (Customers at end − Customers at start) ÷ Customers at start × 100
What you need
Customer growth rate is how much your active customer base grew in a period, as a percentage of where it started: end count minus start count, divided by start count, times 100. You need these columns, here from the sample file monthly.csv:
- customers_start: subscribers at start of month
- customers_end: subscribers at end of month
CREATE TABLE monthly (
month TEXT, -- 'YYYY-MM'
customers_start INTEGER,
customers_end INTEGER
);Step by step
- Load monthly.csv into a table called monthly. The table only needs the month column and the columns below.
- Run the first query to get one row per month.
- 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 * (customers_end - customers_start) / NULLIF(customers_start, 0), 2) AS customer_growth_rate
FROM monthly
ORDER BY month;SELECT ROUND(100.0 * (SUM(customers_end) - SUM(customers_start)) / NULLIF(SUM(customers_start), 0), 2) AS customer_growth_rate
FROM monthly;What you should get
Run against the sample data, the code above returns these values (in percent):
| Month | customers_start | customers_end | customer_growth_rate |
|---|---|---|---|
| 2026-01 | 800 | 880 | 10.00% |
| 2026-02 | 880 | 971 | 10.34% |
| 2026-03 | 971 | 1,069 | 10.09% |
| 2026-04 | 1,069 | 1,136 | 6.27% |
| 2026-05 | 1,136 | 1,239 | 9.07% |
| 2026-06 | 1,239 | 1,348 | 8.80% |
| Whole period | 6,095 | 6,643 | 8.99% |
(6,643 − 6,095) ÷ 6,095 × 100 = 8.99%
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 customer growth rate misleads
- Net growth hides gross movement. 100 new and 90 lost looks the same as 10 new and none lost, but they are very different businesses.
- Define "active" clearly; growth in sign-ups is not growth in customers.
- Percentage growth naturally slows as the base grows. Look at absolute additions too.
The full explanation, with a calculator, is on the Customer growth rate page.
Same metric, other tools
Related metrics 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.
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