One of the biggest challenges for mobile app developers is to figure out their LTV. Quick search on Google will give you multiple results. Mostly these results are complex to understand. The reason for that is that modeling the LTV is hard especially when you have little data about how users behave in your game. We created 6 calculators to reflect different approaches to the subject. You are welcome to try them and see what fits you.
1 – The Naive method for CLV
This is the most basic method for doing the LTV calculations. Basically ARPDAU x Lifespan.
The Naive calculator below implements this basic calculation.
2 – Modeling The Lifetime Function (Simple)
The main problem with the naive calculator is that it doesn’t provide any way to calculate the expected lifetime. The way to do that is actually quite complex. Of course, one can wait and see but the entire idea is to know the lifetime value in advance.
To model out the lifespan of a user you need to use the data points you already collected in order to build the retention function of your app. Retention functions are normally power functions. If you want to fully model out your retention function use method number 5 below.
Alternatively our lifespan based calculator will give you the answer. It uses a slightly simpler model that you can implement yourself more easily. The power function is used to model future retention but the calculation is done through weighted average rather than integral. This means that calculating the CLV is reduced from spreadsheet magic to basic math without giving away much accuracy.
3 – LTV Calculation in a segment using historic data from other segments
Unless you just launched your game you will be able to use this method. It is in use by many gaming companies and especially by data driven ones. Here is the process implemented in the calculator:
- Wait until they have at least 180 days of ARPDAU data
- Calculate the 180 days LTV and 7 day revenue to date by simply summing up revenue to date and average it per user
- Use the Existing LTV to evaluate new cohorts by comparing the first 7-day revenue of the new cohort to the 7 day revenue in existing cohorts
Here is a step by step explanation of #3:
- Take your 7-day ARPU from the new cohort and divide it by your 7-day ARPU from the Master Chart
- Apply the received ratio to the Existing LTV
- This is your estimated LTV for the new cohort
Calculator #3 does this for you.
4 – LTV Calculation in a segment using partial historic data from other segments
This is a combination of #2 and #3. It allows you to leverage data from existing segments even if you don’t have 180 days of data. This calculator uses partial data from existing segments to calculate the LTV in a new segment.
- Wait until they have at least 90 days of ARPDAU data
- Use this data to build a Master Chart of revenue accumulation by day for average user
- Extrapolate the Master Chart beyond 90 days by measuring the churn ratio and applying this ratio beyond day-90 to get the 180 day LTV
- Use the Existing LTV to evaluate new cohorts by comparing the first 7-day revenue of the new cohort to the Master Chart
The partial segment data LTV calculator can be found here
5 – Fully modeling out the retention function with a spreadsheet
This method is mostly based on Eric’s Seufert’s lecture from GDC (Retention Approach). It assumes the retention function is a power function (y=a*x^b) and thatARPDAU is constant.
Here are more details about how to use the spreadsheet as well as download links.
6 – Modeling out the revenue function with a spreadsheet
This method is called The Revenue Approach in Eric’s Seufert lecture from GDC. It assumes the revenue function is a logarithmic function (y=c*ln(x)+b).
You can find the details and download links here.
When calculating your LTV, make sure you are including your ad revenue in the mix. If you need a tool to accurately report ad revenue and ad LTV in different segments, cohorts and traffic sources you should check out SOOMLA Traceback.