In Excel or Google Sheets, LTV to CAC ratio for a whole period is SUM(AF2:AF7) / SUM(Z2:Z7) * (SUM(B2:B7) - SUM(C2:C7)) / SUM(B2:B7) / (SUM(Y2:Y7) / SUM(X2:X7)) / (SUM(G2:G7) / SUM(W2:W7)), where each SUM covers one column of monthly figures. On the sample data it returns 2.40×.
LTV:CAC = Customer lifetime value ÷ Customer acquisition cost
What you need
The LTV to CAC ratio compares what a customer is worth over their lifetime with what it cost to acquire them: lifetime value divided by customer acquisition cost. You need these columns, here from the sample file monthly.csv:
- Revenue (net of discounts): column B (revenue)
- Cost of goods sold: column C (cogs)
- Marketing spend: column G (marketing_spend)
- New subscribers: column W (new_customers)
- Subscribers at start of month: column X (customers_start)
- Subscribers who cancelled: column Y (customers_churned)
- Subscribers at end of month: column Z (customers_end)
- MRR at end of month: column AF (mrr_end)
Step by step
- Download monthly.csv below and open it: in Excel use File → Open; in Google Sheets use File → Import → Upload. The headers land in row 1 and the six months in rows 2 to 7.
- Click any empty cell and enter =SUM(AF2:AF7) / SUM(Z2:Z7) * (SUM(B2:B7) - SUM(C2:C7)) / SUM(B2:B7) / (SUM(Y2:Y7) / SUM(X2:X7)) / (SUM(G2:G7) / SUM(W2:W7)) for the whole six months. It adds up each column first and then does the arithmetic once, which is the correct way to combine months.
- For each month, click AQ2, enter =AF2 / Z2 * (B2 - C2) / B2 / (Y2 / X2) / (G2 / W2) and drag the fill handle down to row 7.
- Format the results as a number with two decimals.
- If a month can have zero in the denominator, wrap the formula so it shows a blank instead of #DIV/0!: =IFERROR(AF2 / Z2 * (B2 - C2) / B2 / (Y2 / X2) / (G2 / W2),"").
The formulas
=SUM(AF2:AF7) / SUM(Z2:Z7) * (SUM(B2:B7) - SUM(C2:C7)) / SUM(B2:B7) / (SUM(Y2:Y7) / SUM(X2:X7)) / (SUM(G2:G7) / SUM(W2:W7))=AF2 / Z2 * (B2 - C2) / B2 / (Y2 / X2) / (G2 / W2)What you should get
Run against the sample data, the code above returns these values:
| Month | revenue | cogs | marketing_spend | new_customers | customers_start | customers_churned | customers_end | mrr_end | ltv_to_cac |
|---|---|---|---|---|---|---|---|---|---|
| 2026-01 | 4,12,000 | 2,26,600 | 96,000 | 120 | 800 | 40 | 880 | 1,95,500 | 2.50× |
| 2026-02 | 4,38,500 | 2,36,800 | 1,02,000 | 135 | 880 | 44 | 971 | 2,12,900 | 2.67× |
| 2026-03 | 4,71,200 | 2,59,200 | 1,18,000 | 150 | 971 | 52 | 1,069 | 2,30,000 | 2.30× |
| 2026-04 | 4,55,800 | 2,55,200 | 99,000 | 128 | 1,069 | 61 | 1,136 | 2,41,400 | 2.12× |
| 2026-05 | 5,02,300 | 2,71,200 | 1,24,000 | 160 | 1,136 | 57 | 1,239 | 2,60,400 | 2.49× |
| 2026-06 | 5,48,900 | 2,90,900 | 1,31,000 | 175 | 1,239 | 66 | 1,348 | 2,80,600 | 2.45× |
| Whole period | 28,28,700 | 15,39,900 | 6,70,000 | 868 | 6,095 | 320 | 6,643 | 14,20,800 | 2.40× |
₹14,20,800 ÷ 6,643 × (₹28,28,700 − ₹15,39,900) ÷ ₹28,28,700 ÷ (320 ÷ 6,095) ÷ (₹6,70,000 ÷ 868) = 2.40×
Notes for Excel
- Google Sheets accepts the same formulas. If your spreadsheet locale uses a comma as the decimal separator, Sheets and Excel expect semicolons between function arguments instead of commas.
- Averaging the monthly results with AVERAGE() gives a different and usually wrong answer for the period, because every month counts the same however large it was.
- Excel tables (Insert → Table) let you write the same formula with column names instead of letters, which is easier to read and survives inserted columns.
Where LTV to CAC ratio misleads
- Both inputs are estimates, and LTV is very sensitive to churn. Report the ratio with the assumptions behind it.
- Use the same customer definition and period for both sides.
- A blended ratio can hide a channel whose customers churn fast. Calculate it per channel when you can.
The full explanation, with a calculator, is on the LTV to CAC ratio (LTV:CAC) page.
Same metric, other tools
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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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