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G. Nie, W. Rowe, L. Zhang, Y. Tian and Y. Shi, “Credit Card Churn Forecasting by Logistic Regression and Decision Trees,” Expert Systems with Applications, Vol. 38, No. 12, 2011, pp. 15273-15285. doi:10.1016/j.eswa.2011.06.028

has been cited by the following article:

• TITLE: Forecasting and the Role of Churn in Software-as-a-Service Business Models

  AUTHORS: Anthony E. R. Sukow, Rebecca Grant

  KEYWORDS: SaaS; Churn; Software; Subscription; Revenue Forecasting

  JOURNAL NAME: iBusiness, Vol.5 No.1A, March 29, 2013

  ABSTRACT: This article 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.