## The Adjust Mobile Measurement Glossary

#### Definition

Once a company can calculate retention and track in-app events successfully, it’s possible to calculate one of the most important metrics in mobile marketing: lifetime value, or LTV.

LTV is a prediction of the net profit attributed to an ongoing future relationship between customer and product. By providing a running estimate on how much a particular consumer is likely to spend when using an app, LTV can be used to help set marketing budgets and ensure that companies use them to 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 Lifetime Value 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, which is summarized as: 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 does, 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 percent then they can find out that their LTV is:

``````                        \$5 x 1/0.3 = \$16.66
``````

As a result, the marketer discovers that they can spend up to \$16.65 on acquiring a user: significantly expanding available acquisition options.

### What Are the Main Challenges When it Comes to Calculating Lifetime Value?

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 rapidly becomes a challenge.

Marketers using LTV must split their users into accurate groups (commonly called cohorts) to try 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? First, 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.

Second, the marketer needs to group users into cohorts to provide a flexible picture of user LTV.

Third, 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.