Scenarios - Revenue analysis
Revenue analysis shows the payment revenue, payment conversion, and cost recovery that a batch of entities generates over time after the first event. You can divide cohorts by the first event, and then combine the payment event, the revenue metric, and cost data to view the revenue performance of each cohort on the day of the first event and on subsequent observation days.
Revenue analysis can answer questions such as:
- What is the day 7 or day 30 LTV of the users acquired each day?
- How do the payment amount and payment rate differ among users from different source channels?
- On which day do the LTV multiple and ROI of each channel reach their targets?
- Which initial dates have users with abnormal revenue performance that needs further investigation?
Quickly configure revenue analysis
Before you start the analysis, determine the entity. Different entities represent different revenue perspectives, for example, observing revenue by user, device ID, or business account. After you select one, the number of first event users, the number of paying users, and per-user metrics are all deduplicated and calculated by that entity.
Next, configure the cohort and the payment behavior:
- First Event: used to divide cohorts, such as registration. Each date row in the result table represents the batch of entities that completed the first event on that day.
- Payment Event: used to determine whether an entity has paid, such as a successful payment. The event selector supports search, event groups, favorites, and Only Verified.
Revenue Metric determines how the system calculates revenue. It consists of the payment event, a numeric property, and a calculation method, and can also be configured as a formula.
The calculation method can be Sum, Per User, accumulative sum, or accumulative sum user average.
To evaluate cost recovery, turn on Cost, and then select the cost event, a numeric property, and the calculation method. The cost data is used for the ROI metric.
Observation Days is the number of days to track after the first event occurs. If it's set to 30 days, the result table shows data from Day 0 to Day 30. Newer cohorts haven't gone through the full observation period yet, so later dates may have no data for now.
To narrow the scope of the analysis, you can filter data. Click the plus sign to the right of All events meet, and then set the property, the operator, and the value in turn.
You can also compare with group by, for example, to split cohort performance by Source Channel. Too many group-by items add dimensions to the results, so we recommend keeping just one key business property at first.
View analysis results
After you finish the configuration, click Calculate. The results area refreshes based on the current date range, observation days, filters, and group-by items.
Click the date in the upper-left corner of the results area to use Yesterday, Today, Last week, This week, Last month, This month, Last 7D, Recent 7D, Last 30 days, Recent 30D, Until Yesterday, Until Today, and Custom. You can also switch between Rolling Date, Exact Date, and the project's shortcut options. After you make a selection, click Apply and calculate again.
Click Metrics in the upper-right corner of the results area to choose from 10 result metrics:
- Revenue Metric: LTV, Revenue, Cum. Revenue, and Rolling LTV.
- Payment Conversion Metric: Paying Users, Cum. Payers, Payment Rate, and Cum. Payer Rate.
- Cost Recovery Metric: LTV Multiple and ROI.
Metrics (2/10) means that 2 of the 10 metrics are currently shown. In the example, LTV and LTV Multiple are selected, so each date cell shows the corresponding values in two rows, one above the other.
By default, revenue analysis shows the results in a table:
- Overview: summarizes the results of all cohorts in the current date range.
- Date rows: each represents the cohort that completed the first event on that day.
- Initial User: the number of entities in the cohort that completed the first event.
- Day 0, Day 1, Day 2, and so on: the metric values on the day the first event occurred and on day N afterward.
- Plus sign at the start of a row: after you configure group-by items, expand it to view the performance of different group values on that date.
When you analyze revenue growth or cost recovery trends, look across a row to see how the same cohort changes over the observation days. When you compare the quality of different acquisition dates, compare the date rows vertically. A recently formed cohort needs the corresponding number of days to pass before it has day N data. For example, a cohort formed today doesn't immediately have day 7 or day 30 results.