# Lifetime Value (LTV) | Definition

## What is lifetime value (LTV)?

LTV is a prediction of the net profit attributed to an ongoing relationship between customer and product. By providing a running estimate on how much a particular consumer is likely to spend on that app, LTV helps set marketing budgets and ensures that companies pursue the most effective users.

If a company can predict a user’s lifetime value successfully, it provides marketers with a much better base on which to make decisions – helping a company maximize the effectiveness of its advertising spending. The term itself is also known as customer lifetime value (CLV or CLTV) and lifetime customer value (LCV).

## How is LTV calculated?

LTV is calculated by finding out the average churn and average spend of a user over the course of a specific period to predict their overall spend in an app. Tapdaq, a mobile advertising network, created a simple equation for calculating LTV: LTV = ARPU x 1/Churn

The formula calculates a user’s lifetime value by predicting how much money they’ll make in a set period (the ARPU, or Average Revenue Per User) and by how well they return (1/churn). With this formula, you can attempt to predict how much a user will be worth throughout their time spent on an application.

## Why is LTV more important than other metrics, like ARPU?

By itself, a metric like ARPU only tells you how much a user is worth over a set amount of time. By combining it with retention, LTV gives a marketer a rough model that can be used to predict the user’s future value. This can show that a user delivers more value in the long-term than average revenue, which enables the marketer to increase the budget to their user acquisition campaigns, giving them a better chance of acquiring more valuable users.

For example, say an app has a monthly ARPU of \$5. If the marketer stops calculating there, they will assume that their cost-per-impression (or CPI) could only reach \$4.99 before spend stops being profitable – restricting acquisition options.

However, if the marketer is also able to calculate that their churn rate is 30%, they can find out their LTV: as a result, the marketer discovers that they can spend up to \$16.65 on acquiring a user: significantly expanding their acquisition options.

## What are the main challenges when it comes to calculating LTV?

The biggest challenge to calculating LTV is that it’s a forecasting metric, and not set in stone. Essentially, LTV shifts and changes as user behavior evolves. This means that the equation used above, while a useful model to show how LTV is used, is too simplistic to be used accurately for mobile marketing purposes.

As an example, say that 20 out of 100 app users retain for three months but their ARPU suddenly changes in the final month due to an unsuccessful app update. In this instance, the LTV used to calculate the cost of marketing to them was forecast as too high, and could change future LTV. Most LTV calculations contain an extra layer of mathematics focused on forecasting – making calculations more complicated.

The other major difficulty when it comes to calculating LTV is applying the value to individual users. As most apps are capable of generating a user base of thousands of people, personalizing marketing spend to each user’s LTV is challenging. Marketers using LTV must split their users into cohorts to establish an overall LTV for particular types of behavior, as this allows them to apply LTV to marketing spend much more successfully.

## How can a marketer begin to calculate LTV accurately for their app?

There are three steps to accurately calculating LTV:

1. The marketer needs to make sure that their in-app analytics correctly measure how much revenue users make over time, and how long they retain for. This means setting up a proper tracking and analytics infrastructure that ensures that all revenue generating actions (like watching an ad or purchasing an in-app purchase) are recorded and assigned to users, as well as making sure that user retention is being calculated as accurately as possible.

2. The marketer needs to group users into cohorts to provide a flexible picture of user LTV.

3. The marketer needs to decide how they will be calculating LTV. Practical constraints, including the accuracy of an app analytics setup, and the resource that can be committed to calculating it, will inevitably limit what a marketer can or can’t do.