The Revenue Lifetime Value (LTV) chart type shows how well you are monetizing your users. In order for data to show up on this chart, you need to call
logRevenueV2() in conjunction with the provided
Revenue interface if you are using our SDKs. If you are tracking in-app purchases (IAPs), we recommend using our revenue authentication system. The difference between this chart and the Revenue Analysis chart is that revenue data in this section is organized around the start date of new users.
The breakdown of the data is based on days after each user's start date. For example, 'Day 1' means one day (24 hours) after a user's start date and 'Day 5' means five days (120 hours) after a user's start date. Thus, if a new user joined on August 22nd, 'Day 1' refers to August 23rd and 'Day 5' refers to August 27th. Users that are new on the same day are placed into the same user cohort and the Revenue LTV chart looks at cohorts of users.
As such, each data point averages all of the Day Nth metrics from the new users within the selected timeframe.
Table of Contents
- Revenue LTV Metrics
- Chart Control Panel
- Chart Interpretation
- Data Table
Note: Amplitude currently does not support currency conversion. The revenue value you send us is what we will aggregate and display. If you receive revenue in different currencies, we recommend normalizing to a single currency before you send us revenue data.
Revenue LTV Metrics
The Revenue LTV chart allows you to view relevant revenue metrics for your project's performance. The metrics you can measure are:
- Total Revenue: Shows the total revenue through Day N divided by the number of users in the cohort
- New Paying Users: Shows the number of users who have logged revenue for the first time on the Nth day after their cohort's start date.
- ARPU (Average Revenue per User, also known as the LTV): A cohort's cumulative "Total Revenue" through N days divided by the number of users in the cohort.
- ARPPU (Average Revenue per Paying User): A cohort's cumulative "Total Revenue" through N days divided by the number of paying users in the cohort.
Chart Control Panel
The right panel in the Revenue Analysis chart control panel functions analogous to the right panel in an Event Segmentation chart. You can read more about how to compare user segments here.
Segmenting on Revenue Properties
You can segment on revenue events by specific revenue properties in the left panel of the chart control panel by using the "+where" function. Note that you can also segment revenue events by those properties in an Event Segmentation chart. The revenue properties are stored as event properties with a '$' sign prefixed.
More details on the properties are outlined in the below table.
|price||Double||Required: The price of the products purchased (can be negative).
Note: revenue = quantity * price
|productId||String||Required: An identifier for the product (we recommend something like the Google Play Store product ID).||null|
|quantity||Integer||Required: The quantity of products purchased. Defaults to one if not specified.
Note: revenue = quantity * price
|revenueType||String||Optional: The type of revenue (e.g. tax, refund, income).||null|
|eventProperties||Object||Optional: An object of event properties to include in the revenue event.
Note: You will only be able to segment on these properties in the Events Segmentation tab.
In the image below, we have segmented out all events where '$price' = '9.99'. Note that these properties must be explicitly sent by you via our SDKs when you log revenue events.
You can set up and interpret any Revenue LTV chart easily as the UI allows you to read the parameters like a sentence. For example, the following chart shows you a visualization of all revenue events performed by your users, measured by the average revenue per user daily in the last 30 days. More specifically, you can hover over the data points to see the actual amount. Here, we can see that the average Day 3 ARPU for new users in the past 30 days is $0.45.
You can use the date picker to choose a more specific time range to analyze your data within, and you can also switch between "Last", "Between", and "Since". You also have the option to view data in daily, weekly, or monthly units by toggling between the different options in the dropdown menu next to the date picker.
If you are looking at multiple segments in your chart, you can manually select and deselect each segment by hovering over the segment name in the bar below the chart and removing it or by clicking the "+" button to add it back. Finally, you can hover over any data point in the chart to view the actual number that makes up that point.
The data table below the chart shows a detailed breakdown of the data by each start date group and day buckets. Note: Values with an asterisk (*) are still being computed. You may see a decline in the tail of the Lifetime Value trendline. This is because if a day column has both fully computed data and data that is incomplete, the trendline will only take the average of the fully computed data for that column.
Lastly, you can download the data table by clicking the "Share" icon in the top right of the page and choosing "Download as CSV".