In Excel or Google Sheets, customer lifetime value 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)), where each SUM covers one column of monthly figures. On the sample data it returns ₹1,856.
LTV = ARPU × Gross margin ÷ Monthly churn rate
What you need
Customer lifetime value (LTV or CLV) estimates the gross profit a customer brings over the whole time they stay. 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)
- 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)) 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) and drag the fill handle down to row 7.
- Format the results as currency 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),"").
The formulas
=SUM(AF2:AF7) / SUM(Z2:Z7) * (SUM(B2:B7) - SUM(C2:C7)) / SUM(B2:B7) / (SUM(Y2:Y7) / SUM(X2:X7))=AF2 / Z2 * (B2 - C2) / B2 / (Y2 / X2)What you should get
Run against the sample data, the code above returns these values:
| Month | revenue | cogs | customers_start | customers_churned | customers_end | mrr_end | ltv |
|---|---|---|---|---|---|---|---|
| 2026-01 | 4,12,000 | 2,26,600 | 800 | 40 | 880 | 1,95,500 | ₹1,999 |
| 2026-02 | 4,38,500 | 2,36,800 | 880 | 44 | 971 | 2,12,900 | ₹2,017 |
| 2026-03 | 4,71,200 | 2,59,200 | 971 | 52 | 1,069 | 2,30,000 | ₹1,808 |
| 2026-04 | 4,55,800 | 2,55,200 | 1,069 | 61 | 1,136 | 2,41,400 | ₹1,639 |
| 2026-05 | 5,02,300 | 2,71,200 | 1,136 | 57 | 1,239 | 2,60,400 | ₹1,927 |
| 2026-06 | 5,48,900 | 2,90,900 | 1,239 | 66 | 1,348 | 2,80,600 | ₹1,837 |
| Whole period | 28,28,700 | 15,39,900 | 6,095 | 320 | 6,643 | 14,20,800 | ₹1,856 |
₹14,20,800 ÷ 6,643 × (₹28,28,700 − ₹15,39,900) ÷ ₹28,28,700 ÷ (320 ÷ 6,095) = ₹1,856
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 customer lifetime value misleads
- Use gross profit, not revenue. LTV in revenue terms overstates what you can spend on acquisition.
- Small changes in churn swing LTV a lot because churn is in the denominator. Treat the figure as a range.
- For non-subscription brands, use average order value × purchase frequency × expected years × gross margin instead.
The full explanation, with a calculator, is on the Customer lifetime value (LTV) page.
Same metric, other tools
Related metrics in Excel and Google Sheets
- How to calculate LTV to CAC ratio in Excel and Google Sheets
- How to calculate customer acquisition cost in Excel and Google Sheets
- How to calculate average revenue per user in Excel and Google Sheets
- How to calculate customer churn rate in Excel and Google Sheets
- How to calculate CAC payback period in Excel and Google Sheets
Learn more about Excel
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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