Introduction

As mobile games continue to grow in popularity, it’s becoming increasingly important for developers to understand the concept of lifetime value (LTV) and how to compute it accurately. LTV refers to the total amount of revenue that a user is likely to generate over the course of their relationship with your game.

Method 1: Cohort Analysis

One of the most common methods for calculating LTV is cohort analysis. This involves grouping users based on their behavior, such as when they first installed the game or how often they play.

By analyzing the revenue generated by each cohort, developers can estimate the total amount of revenue that each user will generate over time.

Example

Let’s say you have a mobile game with two groups of users: Group A and Group B. Group A consists of casual players who install the game and play it occasionally, while Group B consists of more dedicated players who play the game daily.

By analyzing the revenue generated by each group, you can estimate the LTV for each user as follows:

Group A LTV (Total revenue from Group A) / (Number of users in Group A) x Average session length x Session frequency

Group B LTV (Total revenue from Group B) / (Number of users in Group B) x Average session length x Session frequency x Retention rate

Method 2: Customer Lifetime Value (CLV) Model

Another way to calculate LTV is by using the customer lifetime value (CLV) model. This involves estimating the total amount of revenue that a user will generate over the course of their relationship with your game, as well as the average amount of time they are likely to spend playing the game each day or week.

Example

Let’s say you have a mobile game with an average session length of 30 minutes and an average retention rate of 90%. By using the CLV model, you can estimate the LTV for each user as follows:

(Average daily revenue) x ((1 – Retention rate) / Retention rate) x Average session length x Session frequency x Customer acquisition cost (CAC)

Method 3: Predictive Analytics

Predictive analytics is another method for calculating LTV that involves using machine learning algorithms to predict how much revenue a user is likely to generate over time. This can be based on a variety of factors, such as the user’s demographics, behavior in the game, and past purchases.

Real-Life Examples

Now that we’ve discussed the different methods for calculating LTV let’s look at some real-life examples to illustrate how they can be used effectively in mobile gaming:

Example 1: Cohort Analysis

Let’s say you have a mobile game with two groups of users: Group A and Group B. Group A consists of casual players who install the game and play it occasionally, while Group B consists of more dedicated players who play the game daily.

By analyzing the revenue generated by each cohort, you can estimate the LTV for each user as follows:

Group A LTV (Total revenue from Group A) / (Number of users in Group A) x Average session length x Session frequency

Group B LTV (Total revenue from Group B) / (Number of users in Group B) x Average session length x Session frequency x Retention rate

Example 2: Customer Lifetime Value (CLV) Model

Let’s say you have a mobile game with an average session length of 30 minutes and an average retention rate of 90%. By using the CLV model, you can estimate the LTV for each user as follows:

(Average daily revenue) x ((1 – Retention rate) / Retention rate) x Average session length x Session frequency x Customer acquisition cost (CAC)

Example 3: Predictive Analytics

Let’s say you have a mobile game with a large number of users who have made in-app purchases. By using predictive analytics, you can estimate the LTV for each user based on their past purchases and behavior in the game, as well as other relevant factors such as age and location.