This is the 3rd part in a series of 6 posts about life time value. Here is a summary post explaining all the different calculators.
The methods explained in previous posts assume you are starting with a fresh new app. The life time value modeling methods used in posts #2 and #5 for example assumes you only have data on a limited number of days. The method described here is for estimating marketing LTV. Typically when you start marketing your app you already have 6 months of data in one segment (typically on organic traffic) and you are trying to estimate the CLV in a new segment.
- Training Data from existing segment:
- ARPU for existing segment users – day-1 to day-7
- LTV for existing segment users – 180 days
- New Segment data:
- ARPU for new segment users – day-1 to day-7
- Customer Life time Value for the new segment (Marketing LTV) presented as a number and in a Gauge
This model assumes that the ratio between the day-7 revenue of segment-A and the day-7 revenue in segment-B will reflect the ratio in the LTV. For example if you have a new traffic source that has $0.5 ARPU in the first 7 days and you are normally seeing $1 in the first 7 days then the LTV from the new source will be half your normal LTV. This is very intuitive and is actually supported by much more advanced models. The calculation therefore has 2 steps:
- Figure out the ratio between the day-7 revenue numbers
- Apply the same ratio to the LTV
- One of the most accurate models around
- Requires 180 days of data from existing segments
More methods to calculate life time value
Here is a similar method that can be applied even if you have partial data from the existing segment
If you have time and a spreadsheet you can also fully model out the revenue function
Calculating LTV for ad-supported apps can be specifically difficult due to reporting limitations by the ad-networks. Check out SOOMLA Traceback – Ad LTV as a Service.