Estimating Individual Customer Lifetime Values with R: The CLVTools Package

TL;DR

This paper introduces the R package CLVTools, which implements probabilistic models like Pareto/NBD and Gamma-Gamma for estimating customer lifetime value, addressing data sparsity and prediction horizon challenges.

Contribution

The paper presents a user-friendly R package that facilitates applying advanced probabilistic models for CLV estimation, including recent extensions with covariates and regularization.

Findings

Efficient implementation of CLV models in R

Inclusion of covariates and model extensions

Improved CLV prediction accuracy

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.