Predictions
Key metrics forecast
Уou can see widgets with the forecast for following metrics:
- Gross (Daily, Weekly and Monthly)
- Daily Active users
- Daily ARPU
- Retention Day 1 and Day 7 (Daily, Weekly, Monthly)
The forecast will be built for 7 days if there is data for more than 3 months available.
The forecast will be built for 14 days if there is data for more than 6 months available.
Please note that the chart with monthly grouping is shown only if there is enough data to create forecast for the whole month. It means that if it is March now, the forecast will be shown only after the 17th of March (17 days of collected actual data plus 14 days of forecast will give in total the whole month).
LTV Forecast
This tool gives you a fairly precise LTV forecast powered by Machine Learning. Training data for the machine learning algorithms are payments and other activities of users who use your app for at least 3 days. Later on, after we acquire more data about cohorts, the model is recalculated and the forecast accuracy improves even more. It happens on the 7th and 10th days of the cohort’s ‘life’ in the app. We calculate user LTV on their 7, 14, 30, and 60 days since app install.
Why use LTV:
- Traffic quality evaluation. When purchasing traffic, it should be considered that you need to keep the cost per install (CPI) less than LTV, otherwise purchasing such traffic will only lead to losses.
- Finding out the exact time when you will recoup acquisition costs.
- Finding out how much revenue you will earn from existing users and planning your budget.
Requesting the report
By default, the functionality is available for all apps. If you would like to get a report, navigate to your project, click the ‘Forecast’ tab, open the ‘LTV Forecast’ report, and request model building. Notice that if you want us to build an accurate model, your app has to meet certain requirements:
- The app is integrated with devtodev for at least 90 days.
- The MAU is 30k or more
- Payment events are integrated.
- Custom events are integrated.
By days from install
In the top right corner, you need to specify a date range. It will limit the dates of users’ first visit to the app (keep in mind that we can calculate LTV only after three days since install, so the latest three calendar days will be unavailable).
The chart below represents revenue per average active user after 1, 2, ..., N days from the first visit (Predicted LTV) and the amount of money they have brought in till now (Actual LTV). For the Actual LTV calculation, we take into account only the income earned for the number of days all the selected cohorts have ‘lived’ in the app. Therefore, when on the 31st day you build a report on users acquired from the 1st to the 10th day, you get the Actual LTV data for 20 days because the cohort that came to the project on the 10th day has ‘lived’ for 20 days only.
‘Overview’ above the graph and the table below it display the traffic sources, the amount of money spent on their acquisition, the income earned, and the expected total revenue from all the cohorts after 60 days in the project.
In case you need to select only a specific user segment, you can apply a wide range of filters on each report page, and build the LTV forecast only for your target audience.
By calendar dates
The second page of the report shows how the Predicted LTV metrics are changing over time. Here for any selected N-days Predicted LTV metric you can see the deviation between the Predicted LTV and the Actual LTV for every cohort according to their install dates.
For the latest N-days, we can’t calculate the actual LTV, so we use gray candlesticks to show predicted data and the dashed blue line to show the actual data.
When you move the pointer over a cohort, you can see the intervals of predicted lifetime values (from min to max), and the actual LTV.
Detailed stats
The third ‘Detailed stats’ page is represented by a table that shows all the necessary information about your traffic and its profitability. For every group in the table, you can see the actual and predicted N-days LTV and the ROI for N-days.
Data can be grouped using up to three levels by Countries, Sources, Channels, Campaigns, Platforms, and Languages. For example, if you want to see the progress of your Ad campaigns in terms of traffic sources and then examine how well it goes in every country, you can set Group by Campaigns, Sources, and then by Countries in the report settings.
Keep in mind that the more groupings you choose, the fewer users you will have in each group and the less precise your forecast becomes.
The table can be exported as a .csv file by clicking the ‘Export’ button in the top right corner.
Last modified 5mo ago
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