Probabilistic Models for Assessing and Predicting your Customer Base

Provides advanced statistical methods to describe and predict customers' purchase behavior in a non-contractual setting. It uses historic transaction records to fit a probabilistic model, which then allows to compute quantities of managerial interest on a cohort- as well as on a customer level (Customer Lifetime Value, Customer Equity, P(alive), etc.). This package complements the BTYD package by providing several additional buy-till-you-die models, that have been published in the marketing literature, but whose implementation are complex and non-trivial. These models are: NBD [Ehrenberg (1959) ], MBG/NBD [Batislam et al (2007) ], (M)BG/CNBD-k [Reutterer et al (2020) ], Pareto/NBD (HB) [Abe (2009) ] and Pareto/GGG [Platzer and Reutterer (2016) ].


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


1.2.0 by Michael Platzer, a year ago

Report a bug at

Browse source code at

Authors: Michael Platzer [aut, cre]

Documentation:   PDF Manual  

GPL-3 license

Imports Rcpp, BTYD, coda, data.table, mvtnorm, bayesm, stats, graphics

Suggests testthat, covr, knitr, rmarkdown, gsl, lintr

Linking to Rcpp

See at CRAN