Customer acquisition cost (CAC) is how much you spend on marketing to win one new customer: marketing spend in a period divided by the number of new customers in the same period.
CAC = Marketing spend ÷ New customers acquired
On the sample data below, six months come to ₹771.89.
Why it matters
CAC decides whether growth is affordable. Compared with how much a customer is worth over time (lifetime value), it tells you whether each new customer makes or loses money.
Worked example
The table shows the columns this metric needs from six months of a made-up subscription brand. Add each column up over the six months, then put the totals into the formula:
| Month | Marketing spend | New subscribers |
|---|---|---|
| 2026-01 | ₹96,000 | 120 |
| 2026-02 | ₹1,02,000 | 135 |
| 2026-03 | ₹1,18,000 | 150 |
| 2026-04 | ₹99,000 | 128 |
| 2026-05 | ₹1,24,000 | 160 |
| 2026-06 | ₹1,31,000 | 175 |
| Total | ₹6,70,000 | 868 |
₹6,70,000 ÷ 868 = ₹771.89
Add up first, then divide. Averaging the six monthly figures gives a slightly different answer, because it counts a small month the same as a large one.
Month by month
| Month | Customer acquisition cost |
|---|---|
| 2026-01 | ₹800.00 |
| 2026-02 | ₹755.56 |
| 2026-03 | ₹786.67 |
| 2026-04 | ₹773.44 |
| 2026-05 | ₹775.00 |
| 2026-06 | ₹748.57 |
CAC calculator
How to read it
Blended CAC (all spend over all new customers) is easy and honest. Paid CAC (paid spend over customers from paid channels) is useful but depends on attribution you may not trust.
Common mistakes
- Count only new customers. Dividing by all orders gives cost per order, which is much lower and flatters the ads.
- Decide whether salaries, agencies and tools count as spend. Including them gives a fully loaded CAC.
- Spend and the customers it brings fall in different months. Compare over a quarter if your sales cycle is long.
Calculate customer acquisition cost in Excel, SQL or Python
The quickest version, for the sample sheet laid out as in the CSV (headers in row 1, months in rows 2 to 7):
=SUM(G2:G7) / SUM(W2:W7)- Customer acquisition cost in Excel and Google Sheets — per-month formula and zero-safe version
- Customer acquisition cost in SQL — query by month and for the period
- Customer acquisition cost in Python (pandas) — pandas script with expected output
Related metrics
- LTV to CAC ratio (LTV:CAC)
- CAC payback period
- Cost per order (CPO)
- Marketing efficiency ratio (MER)
- Customer lifetime value (LTV)
See all business metrics, or the statistics methods that sit behind them.
Questions people ask
- What is customer acquisition cost?
- Customer acquisition cost (CAC) is how much you spend on marketing to win one new customer: marketing spend in a period divided by the number of new customers in the same period.
- How do you calculate customer acquisition cost?
- CAC = Marketing spend ÷ New customers acquired. For example, with six months of sample data: ₹6,70,000 ÷ 868 = ₹771.89.
- What is the CAC formula in Excel?
- With the sample data laid out as in the downloadable CSV, =SUM(G2:G7) / SUM(W2:W7) gives the figure for the whole period. For one month, use =G2 / W2 and fill it down.
- What is a good customer acquisition cost?
- There is no figure that is right for every business: it depends on your products, prices and channels. Blended CAC (all spend over all new customers) is easy and honest. Paid CAC (paid spend over customers from paid channels) is useful but depends on attribution you may not trust.
Check how stable it is
A metric moves for ordinary reasons as well as real ones. These methods separate the two: the spread shows what a normal month looks like, the moving average smooths it, the z-score flags a month that is out of line and the trend line shows the direction.
Guides
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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