Cohort Revenue Forecasting Model

In the dynamic environment of a SaaS video game developer, the FP&A team grappled with the challenge of accurately forecasting revenue. Traditional statistical models fell short in predicting Average Revenue Per User (ARPU) for highly successful games, which defy conventional statistical models. The team sought to develop a model that could effectively capture the unique revenue dynamics of thriving games.

Sven conducted an extensive analysis of game data to decompose ARPU into drivers that could be projected with statistical models. He designed a model user-interface that allows financial analysts to input a select set of assumptions, which the model uses to generate revenue projections. This model leverages a library of benchmark performance metrics to support risk assessment and predict performance ranges.

The new model has significantly enhanced the integrity and accuracy of revenue forecasts. It provides evidence-based projections, eliminating the reliance on subjective judgments and gut feelings. Additionally, understanding the behavior of different user cohorts at various stages of maturity has added depth to the forecasts. The model reduced revenue forecasting effort from hours to minutes by eliminating cumbersome spreadsheet management.

EXPERTISE:

• Data analysis

• Financial modeling

• Strategy planning

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