Advanced cohort model for enhanced revenue forecasting
In the fast-paced SaaS video game industry, Kabam’s FP&A team struggled to forecast revenue accurately for highly successful games. These exceptional games exhibited revenue patterns that defied conventional modeling approaches. The team sought to develop a forecasting model that could capture these unique patterns and improve the accuracy of revenue projections.
Sven conducted an in-depth analysis of game data to decompose ARPU and revenue into distinct drivers that each could be projected using statistical models. He designed a user-friendly model interface that required financial analysts to input only a concise set of assumptions. The model leveraged a library of benchmark performance metrics to generate evidence-based projections, assess risk, and predict performance ranges. By incorporating cohort behavior at various stages of maturity, the model added depth and reliability to revenue forecasts.
The new model significantly improved the integrity and accuracy of revenue forecasting, reducing reliance on subjective judgments. It streamlined the forecasting process, cutting effort from hours to minutes by eliminating cumbersome spreadsheet management. This initiative demonstrated the power of Finance BI in transforming complex forecasting challenges into actionable, data-driven insights, supporting more confident and informed strategic decisions.
EXPERTISE:
Data analysis
Financial modeling
Strategy planning
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