# What is lifetime value (LTV)?

What is lifetime value (LTV)?

## What is lifetime value?

Lifetime value (LTV) is a marketing metric that reveals the revenue a business can expect to make from a single group of customers.

Marketers calculate LTV so they can allocate budgets more efficiently and make sure they pursue the most spend-happy users. Calculating lifetime value provides marketers with a much better base on which to make decisions—helping companies maximize the effectiveness of their advertising spend. The term itself is also known as customer lifetime value (CLV or CLTV), and lifetime customer value (LCV).

## How to calculate LTV: What to know for apps

You’ll need to know two key figures before calculating lifetime value:

1. The average revenue per user (ARPU) over a specific time period

If you have 1,000 app users and 500 of them spent \$200 in the past year, and the other 500 spent \$800 in the same period, your ARPU is \$500.

Example calculation:

(500 users  x \$200)+(500 users x \$800) = \$500,000

\$500,000/1,000 users = \$500 ARPU

2. The churn rate

Take the number of users lost at the end of the period, and divide it by the number of users you had at the start. Multiply the figure by 100 to give you your churn rate.

Example calculation:
100 lost users / 1,000 users at the start x 100 = 10% churn

## Lifetime value formula

The lifetime value formula is the result of dividing your ARPU figure by your churn rate.

### LTV formula in action

Now we know that LTV = ARPU / churn rate, we can look at an example calculation. Using the above figures and LTV formula, the calculation would look like this:

\$500 / 10% = \$5,000

The lifetime value formula provides an estimate of how much money will be spent by users in a set period (the ARPU) and by how well they could return (/churn%). With this formula, you can start to predict how much a user will be worth throughout their relationship with your app.

## Why is LTV an important metric?

Using other metrics to reflect user value is all well and good, but something like ARPU only tells you how much a user was/is potentially worth over a set period. By combining it with retention—or a lack of retention in churn—LTV gives marketers a rough model to predict a user’s future value.

Armed with this data, marketers can increase user acquisition campaign budgets, as they have an estimate of how much revenue a user or group will deliver in the long term compared to the average, giving them a better chance of acquiring more valuable users.

To better explain this, we’ll take a look at another example app. Let’s say the monthly ARPU is \$5. If the marketer stops their analysis there, they’ll assume their cost-per-impression could only reach \$4.99 before spend stops being profitable, massively restricting their acquisition options.

If the marketer is also able to calculate their churn rate, let’s say 30%, they can find out the lifetime value:

LTV = \$5.00 / 30%

LTV = \$16.66

This tells them they can spend up to \$16.65 on acquiring a user before they cross the no-longer-profitable line, significantly expanding their acquisition options.

## What to know when calculating LTV

The biggest challenge when using LTV to calculate spend is that it’s a forecasting metric, and based on data that can fluctuate. This means, like with any forecasting, you have to allow room for errors and gaps between forecasts and actual results. In mobile marketing, these gaps occur when user behavior changes. Some of these changes are out of a marketer or app developer's control, but here’s what you can do to avoid calculations being too far out.

1. Ensure data accuracy: The more accurate your data is, the less likely you are to have a flawed calculation with misleading figures. In particular, make sure your in-app analytics correctly measure how much revenue users produce over time, and how long they retain for. This means setting up a proper tracking and analytics infrastructure that ensures all revenue generating actions (like watching an ad or making 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. Keep time periods short: Measuring LTV over a longer period does give you a larger data set to base your forecasting on, but you’re also opening yourself up to a greater number of external factors that can derail your efforts, e.g. new trends and competitor app releases.
3. Review and refine figures: No matter how long your campaigns are, it’s important to regularly revisit your LTV calculations to make sure you’re considering factors like new data and changes in customer behavior. Keep LTV figures dynamic so that your budgets don’t become static and lead to overspending or missing a key opportunity.
4. Set priority segments for customers: As most apps are capable of generating a user base of thousands of people, personalizing marketing spend to each user’s LTV is challenging. Segmenting users into cohorts will allow you to set lifetime values for your priority audience groups, or for particular behaviors that you monitor, allowing you to apply LTV to marketing spend much more efficiently.

## Lifetime value and Adjust

Over time, you’ll see LTV change, and it will eventually show you your break-even: the moment you start making a profit from your users.

Segmenting users into cohorts is crucial when it comes to understanding the behavioral trends of your app’s audience. In the Adjust dashboard, you can create your own cohorts and see the lifetime value for each one. You can segment users by install date or reattribution to track changes over time and see how and where different campaigns attract different users. You can even analyze actions taken within the app to further refine your groups, giving you another layer of granularity when calculating lifetime value per cohort, and Adjust will automatically calculate LTV to show when paid campaigns become profitable.

Datascape takes it a step further, unifying all your app data for clear insights and faster decision making. Don't just track active users—understand their true value with in-app spend, session counts, and behavior analysis. To find out more about how Adjust can help you analyze a host of data points accurately and at speed, request a demo today.

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