It’s pretty easy to figure out how much a given player has spent on your game during the course of a set period of time. However, it’s much more complicated to figure out that player’s actual value to your community (and your bottom line). After all, you had to spend a certain amount to get that player to your game to begin with – marketing and promotions aren’t free!
That’s where calculating player lifetime value or LTV comes in. The most basic definition of LTV is that player lifetime value equals the amount of revenue earned from an individual player throughout their lifetime – or the profit they will generate for you from their first moment playing your game until the last.
We’ve talked about calculating player lifetime value before, and the standard simple equation holds true – the return on investment (ROI) of a game can be derived or at least modeled based on the player lifetime spend versus the amount spent on player acquisition. There are a variety of ways to accomplish this, of course, ranging from the simple and quick to the more complex but more accurate formulas that require significant amounts of data.
Modeling Versus Calculating
As long as a game and its community is currently active, any player lifetime value calculations are models, not exact numbers. After all, many of the players you’re considering are currently actively registered and perhaps playing, so the exact player LTV number is a constantly moving target.
But you can certainly model it out and predict the player lifetime value in several different ways, and they range from quick and easy to complex models that require several months or even half a year for you to collect the data that will deliver these valuable learnings.
The Quick And Dirty Method Of Calculating Player Lifetime Value
The simplest and easiest way to quickly assess your game’s overall player LTV is to divide the total revenue by the total number of registered players; you can calculate it for a given time period this way as well. This method isn’t that accurate, since it doesn’t totally take into account the players who’ve registered but haven’t generated any revenue yet, but it’s a nice quick and dirty assessment tool. And when an outside factor(s) results in significant changes to your game and/or its community, this method is by far the easiest way to assess how it will affect your player LTV.
Calculating Lifetime Value With ARPU
As you know, APRU – or ARPDAU – is a game’s daily revenue divided by the number of active users. To use this method to calculate LTV, start by defining what an inactive player is using retention values; chances are you can set this value to a week, ten day, or two week period depending on your game’s historical performance and the complexity of your game. Then take a look at the number of users who expire on a given day, and average that with the number of days from a player’s first visit to the current date (or whatever date you’re considering the end point for the purpose of this exercise). This is your player lifetime value.
Then in order to get the LTV, you divide Daily Revenue by the number of Daily Active Users. Then multiply it by the Lifetime and then you have your LTV.
Defining A Player’s Lifetime: Why You Should Keep It Short And Sweet
Of course, modeling out the player lifetime value involves determining the length of time that is considered a lifetime for the purpose of the model. This is necessary because as previously mentioned, you can’t accurately calculate the true LTV of an active game (since the player lifetimes aren’t over). However, you can decide to calculate the value based on a set period of time and extrapolate from there, as we described above.
It’s also important to keep in mind the fact that longer “lifetime” definitions tend to capture some inaccuracies; namely, the fact that the longer you stretch out what you consider to be a lifetime, the more likely you are to collect some inactive users in your count. Keeping the amount of time that you consider a lifetime relatively short can actually lead to more accurate result, since you’ll be including the users who are definitely active in that period of time.
For instance, if you took all your game’s active users over the course of a year or a couple of months, there will be dropoffs over that course of time. On the other hand, if you limit the amount of time to a week or so, you’ll have a more accurate picture of the number of users you have that are actually active, and therefore a more accurate assessment of your game’s player lifetime value. And in general, the further out you try and predict the future, the greater the risk of your predictions being inaccurate.
Getting Complicated With It: Creating The Curve
Quick and dirty calculations are all well and good – and of course, sometimes that’s all you have time for. However, if you want to get into more complex modeling, there’s a number of of ways to do that. One of them is detailed in this Gamedonia article and creating that kind of complicated calculation can provide some serious insight in your game’s community and profitability.
This kind of modeling also tends to require data from at least 180 days or half a year, so it’s obviously not going to be something you can do accurately in the first couple months of your game’s existence. That said, the level of accuracy gained by this method makes it a worthwhile endeavor, plus you can glean a retention forecast as well the ability to calculate LTV by cohort.
Determining Player Lifetime Value By Cohort
Defining your game’s player lifetime value on its own is obviously important, but if you want to take it to the next level you can divide your players into segments or cohorts and compare the player LTVs in that way.
You can define your game’s cohorts in multiple ways. Perhaps the most obvious in terms of calculating LTV is going by daily active cohorts, or users who joined your game on the same day (further detailed in this article). You could also organize cohorts by users who joined your game through a certain ad campaign or promotional effort, by country or geographic location, or by the type of device used.
Virality, Outside Factors And Player LTV
All of these methods are well and good, but they don’t take into account the many outside factors that can affect LTV, such as the virality of your game, an unpredicted surge in visitors due to an unexpected promotion (or the unexpected success of a promotion), significant changes to your game that users respond incredibly well to (or incredibly badly to), and other organic lifts that you couldn’t possibly predict with existing data.
One of the things that might have the most significant affect on your player LTV is the virality (or lack thereof) of your game. Virality in this sense can be defined as your game’s ability to attract new users for free or nearly free, via social media or direct recommended – organic word of mouth. These users obviously cost less to acquire but may result in similar amounts of revenue as their fellow players who came to the game via paid ads or other more expensive channel. However, viral users may not be as carefully targeted and the ability to determine LTV for viral users versus users otherwise acquired might be a valuable calculation.
Of course, when these things happen, it’s important to reassess your player LTV and future modeling accordingly. This is when those quick and dirty or simple calculations are extra handy; after all, you’re not going to be able to do the more complex modeling referenced above and chances are that you don’t have the time for that anyways! However, the quick assessments are invaluable when it comes to seeing how outside factors affect your player LTV and then you can react and make your next moves from there.
The Hidden Benefits Of Defining Player Lifetime Value: Insights And Confidence
One thing that you probably aren’t thinking of when you determine player lifetime value (regardless of the method that you use) is the unique insight you’ll get into nearly everything else about your game. After all, once you know how much a player is worth, you can start to truly understand how all of your efforts work together to create a profitable game.
There’s a reason why LTV is often referred to as the “king” of app metrics and it’s not just because the name sounds good in a presentation! (Note there are also some very cool Excel tricks for modeling LTV in the linked article).
Plus, it might give you some unique confidence knowing how much your players are worth! And that’s pretty invaluable as you proceed to market to new users and grow your game’s community. And sometimes the intangible benefits like this one are the most motivating part of digging in your analytics and data to begin with.