Forecasting the dynamics of revenue, audience, and other key metrics is an important process for any product that is in its growth phase. Having a good forecast helps to prioritize projects at the planning stage, and then helps to keep track of how quickly you are growing against the forecast, allowing you to spot problems as early as possible.

The very process of creating a forecasting model allows you to synchronize the team in terms of understanding the product’s growth model. It also provides a tool for assessing the impact of working on different areas of the model.

Today we will talk about building audience and revenue forecasts for your product using cohort analysis. We will also find out the pitfalls and difficulties of this process.

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Cohort analysis is the basis for forecasting audience, revenue, and other business metrics for your app

Cohort analysis is the basis for predicting the dynamics of the key metrics of your business.

This metric divides users into cohorts based on the month they started using the product.

This means that if you build the forecast of a certain metric for a specific cohort of users (those who started using the product in month X) then you can easily project the forecast to the entire product. Thus, the forecast of the entire product can be simplified by scaling it down to a single cohort, which is easier to study. Since the process will be identical among cohorts, the results can then be scaled up to the entire product.
Forecasting the dynamics of the key metrics for a cohort of users is relatively easy because you can obtain it by analyzing historical data on older cohorts.

Now that we’ve discussed the basic idea behind building metric forecasts for a product, we can get into the different aspects of the process:

  • Understanding the relation between the metrics (audience numbers, retention, revenue, etc.) of individual cohorts and the entire product
  • Predicting the dynamics of revenue, audience and other metrics for a specific cohort of users
  • Predicting the size of the cohorts that will come to the product in the future
  • Using Google Sheet templates to predict revenue, audience numbers, and other metrics
  • Understanding the nuances, pitfalls and difficulties within the process of building the forecast.