Not logged in.
Quick Search - Contribution
Contribution Details
| Type | Conference Presentation |
| Scope | Discipline-based scholarship |
| Title | Estimating individual Customer Lifetime Values with R: The CLVTools Package |
| Organization Unit | |
| Authors |
|
| Presentation Type | keynote |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
|
| Event Title | useR! |
| Event Type | conference |
| Event Location | Brisbane, Australia |
| Event Start Date | July 10 - 2018 |
| Event End Date | July 13 - 2018 |
| Abstract Text | Valuing customers is key to any firm. Customer lifetime value (CLV) is the central metric for valuing customers. It describes the long-term economic value of customers and gives managers an idea of how customers will evolve over time. To model CLVs in continuous non-contractual business settings such as retailers, probabilistic customer attrition models are the preferred choice in literature and practice. Our R package CLVTools provides an efficient and easy to use implementation frameworks for probabilistic customer attrition models. Building up on the learnings of other implementations, we adopt S4 classes to allow constructing rich and rather complex models that nevertheless still are easy to apply for the end user. In addition, the package includes recent model extensions, such as the option to consider contextual factors, that are not available in other packages. This article will focus on both, the theory of the underlying statistical framework as well as about the practical application using real world data. |
| Export |
BibTeX
|