Scientific Publishing Ltd.
This paper demonstrates a revenue forecasting model for Software-as-a-Service (SaaS) business models. Due to the highly predictable nature of subscriptions, a SaaS business can often project future revenue on the basis of a few key metrics. However, understanding and predicting the churn rate of the subscription base is critical to successful projections. The authors explain SaaS churn and demonstrate the use of critical variables in a predictive SaaS revenue model. The model allows a business to project future revenues based on historical and expected customer subscription behavior. The methodology combines research with the experience of a senior executive in a SaaS-driven business to build the predictive platform.