How to calculate LTV to CAC ratio in Python (pandas)

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:

• df["revenue"]: revenue (net of discounts)
• df["cogs"]: cost of goods sold
• df["marketing_spend"]: marketing spend
• df["new_customers"]: new subscribers
• df["customers_start"]: subscribers at start of month
• df["customers_churned"]: subscribers who cancelled
• df["customers_end"]: subscribers at end of month
• df["mrr_end"]: MRR at end of month

Step by step

1. Install pandas (pip install pandas) and save monthly.csv next to your script.
2. Run the script. The first print shows one row per month, the second the figure for the whole period.
3. To use your own data, rename your columns to match, or change the names in the code.

The code

import pandas as pd

df = pd.read_csv("monthly.csv")

# Each month
df["ltv_to_cac"] = df["mrr_end"] / df["customers_end"] * (df["revenue"] - df["cogs"]) / df["revenue"] / (df["customers_churned"] / df["customers_start"]) / (df["marketing_spend"] / df["new_customers"])
print(df[["month", "ltv_to_cac"]].round(2))

# Whole period: add up the parts first, then divide
ltv_to_cac_total = df["mrr_end"].sum() / df["customers_end"].sum() * (df["revenue"].sum() - df["cogs"].sum()) / df["revenue"].sum() / (df["customers_churned"].sum() / df["customers_start"].sum()) / (df["marketing_spend"].sum() / df["new_customers"].sum())
print(round(ltv_to_cac_total, 2))

What you should get

Run against the sample data, the code above returns these values:

₹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 Python

• Dividing by zero in pandas does not raise an error: it gives inf or NaN. Check with df.replace([float("inf"), -float("inf")], float("nan")) before averaging or plotting.
• For the whole period, sum the columns first and then divide. df[col].mean() of the monthly results weights a small month the same as a large one.
• pct_change(), groupby() and resample() extend the same idea to weekly data, product categories and daily rows.

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

• LTV to CAC ratio in Excel and Google Sheets
• LTV to CAC ratio in SQL

Related metrics in Python (pandas)

• How to calculate customer lifetime value in Python (pandas)
• How to calculate customer acquisition cost in Python (pandas)
• How to calculate CAC payback period in Python (pandas)

Learn more about Python

• 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.