Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/287328 
Year of Publication: 
2021
Citation: 
[Journal:] Journal of Business Economics [ISSN:] 1861-8928 [Volume:] 92 [Issue:] 4 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2021 [Pages:] 707-733
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
The Pareto/NBD model is one of the best-known models in customer base analysis. Extant literature has brought up three different Markov Chain Monte Carlo (MCMC) procedures for parameter estimation of this model. Nevertheless, three main research gaps remain. Firstly, the issue of hyper parameter sensitivity for these procedures has been disregarded even though this is crucial when dealing with small sample sizes. Secondly, present research lacks a performance comparison between the different MCMC procedures as well as with Maximum Likelihood Estimates (MLE). Thirdly, existing minimal data set requirements for this model neglect MCMC estimation procedures as they only refer to MLE. To tackle these gaps, we perform two extensive simulation studies. We demonstrate that the algorithms differ in their sensitivity towards the hyper distributions and identify one algorithm that outperforms the other procedures in all respects. In addition, we provide deeper insights into individual level forecasts when using MCMC and enhance extant data set limitation guidelines by considering not only the cohort size but also the length of the calibration period.
Subjects: 
Customer base analysis
Pareto/NBD model
Markov Chain Monte Carlo
JEL: 
M31
C11
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.