Customer lifetime value (CLV) describes a customer's long-term economic value for a business. This metric is widely used in marketing, for example, to select customers for a marketing campaign. However, modeling CLV is challenging. When relying on customers' purchase histories, the input data is sparse. Additionally, given its long-term focus, prediction horizons are often longer than estimation periods. Probabilistic models are able to overcome these challenges and, thus, are a popular option among researchers and practitioners. The latter also appreciate their applicability for both small and big data as well as their robust predictive performance without any fine-tuning requirements. Their popularity is due to three characteristics: data parsimony, scalability, and predictive accuracy. The R package CLVTools provides an efficient and user-friendly implementation framework to apply key probabilistic models such as the Pareto/NBD and Gamma-Gamma model. Further, it provides access to the latest model extensions to include time-invariant and time-varying covariates, parameter regularization, and equality constraints. This article gives an overview of the fundamental ideas of these statistical models and illustrates their application to derive CLV predictions for existing and new customers.

翻译：客户终身价值（CLV）描述了客户对企业的长期经济价值。该指标在市场营销中被广泛使用，例如用于选择营销活动的目标客户。然而，CLV建模具有挑战性。当依赖客户的购买历史时，输入数据往往稀疏。此外，由于其长期性特点，预测周期通常长于估计周期。概率模型能够克服这些挑战，因此成为研究者和从业者的常用选择。从业者尤其看重其在小数据和大数据场景下的适用性，以及无需精细调参即可实现的稳健预测性能。这类模型的流行源于三个特征：数据简约性、可扩展性和预测准确性。R包CLVTools提供了一个高效且用户友好的实现框架，可用于应用关键概率模型（如Pareto/NBD模型和Gamma-Gamma模型）。此外，该包还支持最新的模型扩展功能，包括时不变与时变协变量、参数正则化及等式约束。本文概述了这些统计模型的基本原理，并演示了如何应用它们来推导现有客户与新客户的CLV预测。