LTV (Lifetime Value) is an important metric for decision-making in both marketing and product management. But measuring LTV is a bit tricky and you can easily make mistakes when calculating it. Moreover, even articles that have found their way to the first page of Google search results contain mistakes when it comes to calculating LTV.

In this essay, I will discuss how to (not) calculate LTV, and how to avoid these common mistakes:

  • Calculating LTV based on revenue instead of gross profit.
  • Calculating LTV by using users’ Lifetime which is calculated as 1/churn or in any other way.
  • Calculating LTV based on the average number of user purchases.

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What is LTV (Lifetime Value)

The classic definition of LTV (Lifetime Value) is the gross profit an average user brings over the entire period of using a product.

In practice, LTV is usually calculated over a specified period after the user starts using the product, e.g., X days or months. For example, LTV for day 7 or LTV for month 12

The choice of the calculation period depends on the tasks one is trying to solve. For example, if a marketing team acquires users from ad networks and expects to recoup the investment in one year, then the one-year LTV will be crucial for them.

If we calculate LTV by say Day 0, 1, 2 and further on, then we will get an LTV curve in dynamics. The graph of LTV dynamics by day usually looks like this:

If we calculate LTV by say Day 0, 1, 2 and further on, then we will get an LTV curve in dynamics. The graph of LTV dynamics by day usually looks like this:

How to calculate LTV

Basics of calculating LTV in a nutshell.

We calculate LTV based on:

  • Gross profit
  • Cohort analysis
  • In practice, you should be able to predict LTV based on a few days of data collected from a new cohort.

Let’s take a closer look at each of the points above.

LTV is calculated based on gross profit, not revenue

Gross profit is the difference between a product’s revenue and all the variable costs that are directly associated with the product or service (COGS).

Here’s a simple rule of thumb for determining which expenses should be deducted from revenue when calculating the gross profit:

  • If expenses increase linearly with sales and revenue, then they must be deducted from the revenue.
  • If certain expenses do not increase as sales volumes grow, then they do not need to be deducted.

If your product is a mobile game in the App Store, then in order to calculate a gross profit from the amount of money paid by your users, you will need to deduct the Apple commission, the support team’s costs (salaries, software and other expenses). Meanwhile, you don’t need to deduce salaries for the development team. These are fixed costs, they do not grow in proportion to the game’s revenue.

If you are selling masks through an online store, then to calculate the gross profit, you will need to subtract the cost of masks, the shipping costs, the payment solution provider fees and other costs directly associated with the sale’s process.

How to calculate LTV with the help of cohort analysis:

  • We take a cohort of users and we calculate the gross profit for each user in dynamics by day from the moment of registration.
  • We calculate the gross profit for the entire cohort of users in dynamics by days from the moment of registration for each user.
  • Based on the previous result, we calculate the cumulative gross profit of the user cohort in dynamics by day. The gross profit for day N will be equal to the gross profit for day 0 to N.
  • We divide the cumulative gross profit by the number of users in the cohort and get the daily LTV dynamics of the user cohort.

An example of calculating LTV in Google Spreadsheets

An example of calculating LTV in Google Spreadsheets

Predicting LTV

A marketing team that expects to recoup the costs of paid traffic in one year cannot wait the entire year to decide whether their ad campaign is worth continuing or not.

The decision must be made much earlier than the data for calculating the actual value of LTV over the first 12 months becomes available. Therefore, it is important to be able to predict LTV based on several days’ worth of data collected from a new cohort of users.

In my experience, most teams quickly develop a set of simple rules (heuristics) that allow them to make such decisions. Based on the historical data, they can estimate what the ROI (or LTV) value should be for day 1, day 2 and the following days to achieve their goal. If a new ad campaign doesn’t meet these values, then they discontinue it. If the campaign results match or exceed the expected level, then the team continues running this campaign or scales it.

This approach won’t provide an ideal forecast, but it helps make decisions. Usually, as budgets grow, teams introduce more precise rules—often specific to some segments of users—based on countries, operating systems, and other factors. Many companies also develop more advanced tools and models for predicting LTV.

This approach won’t provide an ideal forecast, but it helps make decisions. Usually, as budgets grow, teams introduce more precise rules—often specific to some segments of users—based on countries, operating systems, and other factors. Many companies also develop more advanced tools and models for predicting LTV.