Acquisition reports

This block of reports allows you to analyze user acquisition in your application. To start analysing user acquisition in your app, you need to:

• Integrate devtodev SDK into your application and you will be able to evaluate the quality of your traffic in devtodev.

• Integrate an attribution platform (we support Adjust, AppsFlyer, Tune, Branch, Kochava and Tenjin) using postback URLs from the Integration section (Settings -> 3rd party sources -> Attribution tracking). Please note that to do it, you must have an Admin or Owner role. Learn more about available integrations on the page below:

Acquisition reports:

• Acquisition Dashboard

• Monitoring

• Detailed stats

• LTV Prediction

Acquisition Dashboard

This dashboard contains acquisition data overview by traffic sources:

• General ratio between paid and organic traffic.

• Distribution of new users by countries.

• Cumulative ARPU for top 5 traffic sources (top is based on the number of users).

• More detailed information about top sources in a table view.

Click on the +🔎 icon to open a more detailed report.

Monitoring

This report allows to measure efficiency of the incoming traffic.

Select up to 5 different sources, campaigns or countries (depending on the tab) and click View result to monitor key metrics: Installs, Gross, ARPU, Paying conversion, etc.

You can also compare some financial and behavioral metrics for selected sources, such as Cumulative ARPU, Retention, ROI, Payback period, etc.

Detailed Stats

This table contains all basic info about traffic sources. You can add any metrics to compare by clicking the Metrics button.

If you are using the Custom Postback API or install referrer (SDK), you can select the necessary traffic sources in the filters.

Data can be grouped by country / campaign / publisher or other sub categories.

Click Filters to apply additional filters or segment.

Conversion in Event

You can also add a specific custom event to determine conversion into action. It allows you to assess the quality of traffic acquired from different sources. If users perform key actions in the app, it could be an indicator that they are interested in the app and the product is clear for them.

For example, you can examine the following conversion rates:

• Conversion to passing N levels in the game.

• Registration in the app, especially if it is recommended to continue working with the product.

• Entering body characteristics for a fitness app.

• Completion of the first lesson in an educational app.

LTV Prediction

This tool gives you a fairly precise LTV forecast powered by AI. The machine learning algorithms use payments and other activities of users who use your app for at least three days for training data.

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, open Smart View -> Aquisition -> LTV Prediction and request a forecast model. Notice that if you want us to build an accurate model, your app has to meet certain requirements:

• The app is integrated in devtodev for at least 90 days.

• MAU is 30k or more.

• Payment events are integrated.

• Custom events that describe user behavior in the app are integrated.

LTV forecast for day 360

After your D7, D14, D30 and D60 LTV models have run for 390 days for a particular project, you will get access to an LTV forecast for day 360. The forecast results will be displayed on all report tabs automatically.

If you have just connected a new project to devtodev and would like to get a day 360 LTV forecast, you need to request us to build a model as described above and wait for a year. This delay is because we need to collect one year of data about the project to generate a more accurate yearly forecast.

Or, if you have historical data for more than 390 days, you can upload it to devtodev using these instructions. Then, we can use the data to train our LTV forecasting model (and to build a forecast for day 360).

Detailed stats

The Detailed stats page 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-day LTV and the ROI for N days.

You can group data up to three levels by Countries, Channels, Traffic sources and Campaigns.

For example, if you want to check the progress of your ad campaigns in terms of traffic sources and then examine how well they perform in every country, you can set Group by Campaigns, Traffic 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.

You can save the table as a .csv file. Click on three dots in the top right corner and select Export to CSV.

By days from install

In the top right corner, you need to specify a time frame. 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 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 up to now (LTV Fact).

For the LTV Fact, we take into account only the income earned for the number of days all the selected cohorts have ‘lived’ in the app.

For example, when you build a report on the 31st day for users acquired from the 1st to the 10th day, you get the LTV Fact data for 20 days because the cohort that came to the project on the 10th day has ‘lived’ for 20 days only.

The Total info section 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 report shows how the Predicted LTV metrics are changing over time. For any selected N-day Predicted LTV metric, you can see the deviation between the Predicted LTV and the LTV Fact for each cohort according to their install dates.

For the latest N days, we cannot calculate the actual LTV, so the graph uses grey 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.