Arguably, the most important metric for a downloadable product monetizing with passive income (via ads, recurring memberships, etc.) is its lifetime value or LTV. The lifetime value represents how much revenue the average user generates over the lifetime usage of that product. For instance, if you run a subscription-based website with monthly billing, a user’s LTV would be $30 if you had the following metrics:
- Average membership length: 3 months
- Membership cost per month: $10
- LTV: $30
What the LTV tells you is that if you were to acquire 100 users today, you could expect to generate $3,000 from those users. The LTV of your users is a necessary metric to know, especially if you’re doing any type of media buy (pay-per-click, pay-per-acquisition, pay-per-install, etc.). Knowing your LTV can help you to properly manage your campaigns and ensure their profitability.
The difficulties inherent to estimating LTV
However, calculating an LTV is not easy. The difficulties inherent to this key performance indicator become evident when unique user information (activity by unique user and revenue by unique user) are not directly available, as is the case most of the time, due to both technological limitations and privacy policies restrictions.
If you cannot tie activity and revenue to a unique user or a group of unique users, and are unable to track those metrics over a period of time, calculating the LTV becomes quite complicated. Moreover, if your user base continues to grow when you attempt to calculate a LTV, you’ll face another dimension to the problem: isolating your estimate’s sample from new users who are themselves generating revenue.
These difficulties are not insurmountable. There are two accessible approaches to calculating LTV which are made possible with cohort tracking: the first one is revenue cohort tracking and the second one is daily user cohort tracking.
What is a cohort?
A cohort is a group of users who share something in common such as their date of first visit to a website or the date they installed an application. Cohorts allow you to segment your data into groups of users and to track them over a period of time. Cohorts are ideal for LTV calculations or estimates.
Revenue cohort tracking
Revenue cohort tracking is the ideal setup to calculate a LTV. To illustrate, let’s define a cohort by the date of installation and take revenue data from August 1 to 16, where August 1 is Day 1, August 2 is Day 2, August 3 is Day 3, and so forth.
At 16 days from the installation, our cohort of 1,000 users stopped generating any revenue. During the period of day 1 to day 16, our cohort generated a total of $847 and a LTV of $0.85. The LTV is calculated by simply dividing the total revenue for that cohort by the number of users in that cohort.
By running the calculations above on multiple cohorts (August 1, 2, 3, 4, 5, 6, 7, 8, 9, etc.) and weighting revenue generated by day against each cohort’s user base, we can easily and accurately calculate the LTV of a downloadable product monetizing with passive income.
Revenue cohort tracking is the easiest and simplest way to determine the LTV of your users. However, this approach might not be accessible to everyone and depends on the availability of cohort IDs in your revenue reports. Unfortunately, if you cannot pass a cohort ID from your application to your revenue system, then this approach is not suited for you.
Daily user cohort tracking
The second approach requires a bit more work, but is more accessible since revenue cohort tracking requires a sophisticated revenue reporting setup that few application marketers have today.
This approach can be broken down into three steps:
- Determine a revenue per daily user baseline
- Determine the average active days per user
- Calculate LTV
Determining a revenue per daily user baseline
This key performance indicator is rather simple to calculate. For a reporting period, simply take the total revenue and divide it by the total non-unique daily active users.
In the example above (August 1 – 5, 2013), our revenue per daily user is $0.35.
Determining the active days per user
Determining the active days per user will require cohort tracking, where each cohort is once again defined by the date of installation. Let’s look at the following table where cohort activity (active daily users) is reported by day from installation (August 1, 2013) and as a percentage of Day 1 active daily users.
The sum of all values in column ‘As % of Day 1′ represents the average active days per user, where 100% is 1 day. In this case, the active days per user is 2.42.
Calculate the LTV
Calculating the lifetime value from here is straightforward.
- Revenue per daily user: $0.35
- Active days per user: 2.42
- Lifetime value: $0.85
We’ve seen in this article 2 cohort analyses to calculate the LTV of a downloadable product monetizing with passive income. Cohort analyses can also be used for non-downloadable products. Cohorts can help us understand better our users and can open up actionable insights. But most importantly, having an accurate LTV for any type of product or service can tell you whether your campaigns are profitable.
Calculate Lifetime Value (LTV) with 2 Types of Cohort Analyses Excel Workbook
Type: Excel Workbook
Size: 24 KB
Description: Contains the data, tables and charts for this article. Explains how to calculate a downloadable product’s LTV by using 2 types of cohort analyses.