Estimating Individual Customer Lifetime Values with R: The CLVTools Package

2026-02-10

AI Summary

This work proposes CLVTools, an open-source R package for customer lifetime value (CLV) modeling that addresses key challenges such as sparse transaction data and prediction horizons exceeding the observation window. Built upon probabilistic generative models—including Pareto/NBD and Gamma-Gamma—the toolkit integrates maximum likelihood estimation with Bayesian inference, and supports both time-invariant and time-varying covariates, parameter regularization, and equality constraints. Designed for robustness and computational efficiency, CLVTools delivers accurate individual-level CLV predictions even with limited data, while maintaining scalability to large datasets. By enhancing both predictive precision and data frugality, the package offers a practical and extensible solution for marketing decision-making.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Reasoning under Uncertainty: Causality

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• Customer Lifetime Value
• Sparse Data
• Long-term Prediction
• Probabilistic Models
• Purchase Histories

Innovation

• CLVTools
• probabilistic CLV models
• Pareto/NBD
• covariates integration
• parameter regularization