A novel approach to predicting customer lifetime value in B2B SaaS companies

Abstract In this paper, we propose a flexible machine learning framework to predict customer lifetime value (CLV) in the Business-to-Business (B2B) Software-as-a-Service (SaaS) setting. The substantive and modeling challenges that surface in this context relate to more nuanced customer relationships, highly heterogeneous populations, multiple product offerings, and temporal data constraints. To tackle these issues, we treat the CLV estimation as a lump sum prediction problem across multiple products and develop a hierarchical ensembled CLV model. Lump sum prediction enables the use of a wide range of supervised machine learning techniques, which provide additional flexibility, richer features and exhibit an improvement over more conventional forecasting methods. The hierarchical approach is well suited to constrained temporal data and a customer segment model ensembling strategy is introduced as a hyperparameter model-tuning step. The proposed model framework is implemented on data from a B2B SaaS company and empirical results demonstrate its advantages in tackling a practical CLV prediction problem over simpler heuristics and traditional CLV approaches. Finally, several business applications are described where CLV predictions are employed to optimize marketing spend, ROI, and drive critical managerial insights in this context.