Did you know that a 5% increase in the customer retention rate can increase profits by up to 95%? That’s a great result, and measuring customer LTV is a crucial step on the way towards it. But what is customer LTV? How to calculate and improve it? Is it possible to use machine learning for LTV prediction? We are here to answer these and many other questions, so keep reading to learn everything about how to calculate customer LTV, its advantages and how it can benefit from machine learning and data science.
Customer LTV: Definition
As always, let’s start with a quick definition. So, customer LTV, or Customer Lifetime Value if fully, is the total amount of money you are likely to get from an individual client over their lifetime. Or, as we talk about mobile apps and games, from every single user or player. With the help of this value, you can plan and then implement methods to retain your users, and, therefore, develop your business. Here are a few more reasons why you should measure and calculate your customer LTV:
- Customer LTV allows determining and tracking the success. If you compare LTV across diverse channels and marketing campaigns, you will understand which aspects you should accelerate and which to eliminate.
- Tracking LTV will give you an opportunity to determine your best customer personas. As a result, you will be able to develop better marketing and sales campaigns. In turn, these campaigns will lead to an increase in the number of downloads. Besides, if you use LTV for planning marketing budgets, you will avoid unnecessary expense, and spend your funds effectively.
- It is cheaper to retain existing users than to find and attract the new ones. When you measure your customer LTV and get to know your users better, you understand what exactly they are looking for and concentrate on meeting their needs. This is a much more efficient strategy than trying to please everyone.
- Focusing exactly on your loyal users and target audience can also save you from wasting time and effort on unpromising customers.
As you can see, measuring customer lifetime value can bring numerous benefits to you and your business. However, only 42% of companies are able to deal with this task. We want you to belong to this category, so check the formulas we prepared for you in the next paragraph.
How to Calculate LTV
So, let’s talk about LTV calculation for mobile games and apps. It’s not as complicated as it may seem to be, here are five steps to follow:
First of all, calculate for LTV the average purchase value. You can do this by dividing the total revenue of your game or app over a specific period of time by the number of purchases made in the course of the same time period.
Average purchase value = Total revenue / Number of purchases
Then move to the average purchase frequency rate. To calculate it, divide the number of purchases made over a certain period of time by the number of unique users who made purchases during that period.
Average purchase frequency rate = Number of purchases / Number of unique users
Now, thanks to the first two steps, you can calculate the User value. To do this, multiply the average purchase frequency rate by the average purchase value.
User value = Average purchase frequency rate x Average purchase value
The next step is the user lifespan. To calculate it, you need to average out the number of years a user continues making purchases in your app or game.
Finally, you have everything ready to calculate LTV — multiply the user value by the average user lifespan.
LTV = User value x Average user lifespan
After all the calculations, you will get a certain number. Let’s imagine that it is $2.75. If your CPI (Cost per Install) is something like $2.76 or more, you are doing something wrong. CPI is the average cost of acquiring a single user across all channels, and LTV must always be higher than this rate. In case it is not like this, you should improve your LTV as soon as possible — the next paragraph is exactly about it.
ROI = LTV / CPI
Apart from this, you can use LTV to predict ROI, or Return or Investment. To do this, divide the lifetime value by the cost per install.
How to Improve LTV
We have already talked about LTV importance in mobile apps, so you fully understand how crucial this measure is. If you are not satisfied with the results of your calculations, there are ways to improve customer LTV. Take a look at some of them:
- Try cross-selling and up-selling. Just remember, you shouldn’t aim only at the increase of your app’s revenue. Customer retention is also crucial, so keep this in mind when implementing the updates. By the way, if you are not really sure what cross-selling and up-selling are, take a look at this article.
- Expand the variety of products — offer your users something absolutely new. This strategy may motivate them to retain in the app or game.
- Make the onboarding process as simple as possible — give your users an opportunity to sign in via Facebook, Google or any other network. People don’t like spending too much time on creating a new account, and some of them may simply delete an app if there is no opportunity to sign in via a social network. So if your app still doesn’t have this feature, we highly recommend you to fix this.
- Don’t be too intrusive. Push notifications are a frequent component of mobile apps and games, so it is okay if you have them. But remember that their main mission is to inform users about something and encourage them to make in-app purchases, not to irritate. So review your push notifications and, if possible, improve them.
- Go for targeted marketing campaigns. Focus on those users who have been inactive for a while, but haven’t deleted the app yet. Attract their attention with a notification about an important update, offer them a bonus, etc. In this way, you will remind them about your application, and they may continue using it. Retaining users is cheaper than attracting new ones, remember?
How Machine Learning is Used for Calculate LTV and Prediction
AI development and Machine Learning can significantly simplify many things, and here is how you can use it in order to predict the lifetime value:
- Machine learning can help to optimize marketing budgets
- Machine learning can be used to cluster the best buyer personas
- Machine learning can be used to identify the best channels after the campaign is already started
- Machine learning can be used to predict customer churn
Yes, machine learning can help with all these things. However, as always in the case of machine learning, you will need a data set and a model. And the more data you collect, the more accurate and effective your model will be. Here we have already prepared a guide on using artificial intelligence and machine learning for predicting customer churn.
We have told you a lot about how to calculate customer LTV, so now you can start putting knowledge into practice. But if you still have any questions (for instance, about other ways of using machine learning for LTV prediction or LTV importance in mobile gaming), you are always welcome to ask them. Just get in touch with us, and we will do our best to help you.