Greetings from the AdsAdvisor team! In this series of articles, we’re sharing deeper insights about our service. In this first installment, we’ll focus on the unique aspects of calculating revenue and monetization forecasts for Android. We’ll also explore how cohorts are formed, how forecast coefficients are updated, and some of the key considerations that shape the forecasting process on Google’s platform.

First some background: forecasts for in-app purchases (IAP) and advertising monetization are calculated using a similar approach – both models rely on installs and their associated payments.

Notably, our forecasting model intentionally excludes in-game metrics, as these can vary widely across projects and influence forecasts in a different way. Instead, we adhere to the principle that "money predicts money."

How we calculate forecasts for Android

Let’s start by breaking down our process for performing Android forecasts for IAP and advertising monetization. To accurately compute forecast coefficients, we analyze a defined number of installs and purchases within specific cohorts. We categorize these cohorts by platform, traffic type, and country. (If a particular category lacks sufficient data, we merge it with a larger category to ensure robust analysis.)

The training data derives from a predetermined past period, the size and parameters of which can be adjusted during the integration and setup phase. For entirely new projects, we utilize whatever data is currently available.

If desired, we can also apply coefficients from similar projects in the studio’s portfolio. Once the project accumulates enough of its own data, we transition to forecasting based on that tailored dataset.

Forecast coefficients are updated every two weeks and remain fixed for the following two weeks. Importantly, users gain access to updated coefficients one week before implementation, allowing time for analysis and adjustments.

We can also set coefficients at the client’s request, which may be necessary if the project undergoes significant changes in its internal mechanics.

Typically, we do not recalculate forecasts based on optimization types, though we can accommodate client requests. However, adding smaller sections can negatively impact accuracy, which is somewhat paradoxical. This happens because reducing the amount of training data for the section might diminish the accuracy gained from considering the optimization type.

Notably, when calculating forecasts, we only include complete days, which significantly stabilizes the forecast's behavior in the early days of each cohort's life. This approach helps eliminate systematic underestimation during those initial days, a crucial factor for decisions regarding scaling or reducing purchases.

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Forecasting for Android involves careful segmentation of data, regular updates to forecast coefficients, and strategic handling of smaller cohorts. By focusing on metrics like complete days and merging insufficient data categories, this approach aims to minimize underestimation and allow for more informed scaling decisions. Armed with these Android-specific insights, teams can refine their monetization strategies and pave the way for stable revenue growth.

In the second part of our article, we will continue to talk about forecasts, but we’ll turn our focus to the iOS platform.