Probabilistic Models for Assessing and Predicting your Customer Base.

BTYDplus

The BTYDplus R package provides advanced statistical methods to describe and predict customer's purchase behavior. 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, MBG/NBD, BG/CNBD-k, MBG/CNBD-k, Pareto/NBD (HB), Pareto/NBD (Abe) and Pareto/GGG.

Installation

# install.packages("devtools")
devtools::install_github("mplatzer/BTYDplus", dependencies=TRUE)
library(BTYDplus)

Getting Started

demo("cdnow")        # Demonstration of fitting various models to the CDNow dataset
demo("mbg-cnbd-k")   # Demonstration of MBG/CNBD-k model with grocery dataset
demo("pareto-abe")   # Demonstration of Abe's Pareto/NBD variant with CDNow dataset
demo("pareto-ggg")   # Demonstration of Pareto/NBD (HB) & Pareto/GGG model with grocery dataset

Implemented Models

These R source files extend the functionality of the BTYD package by providing functions for parameter estimation and scoring for NBD, MBG/NBD, BG/CNBD-k, MBG/CNBD-k, Pareto/NBD (HB), Pareto/NBD (Abe) and Pareto/GGG.

• NBD Ehrenberg, Andrew SC. "The pattern of consumer purchases." Applied Statistics (1959): 26-41. \doi{10.2307/2985810}
• MBG/NBD Batislam, E.P., M. Denizel, A. Filiztekin. 2007. Empirical validation and comparison of models for customer base analysis. International Journal of Research in Marketing 24(3) 201–209. \doi{10.1016/j.ijresmar.2006.12.005}
• (M)BG/CNBD-k Reutterer, T., Platzer, M., & Schroeder, N. (2020). "Leveraging purchase regularity for predicting customer behavior the easy way." International Journal of Research in Marketing. \doi{10.1016/j.ijresmar.2020.09.002}
• Pareto/NBD (HB) Ma, Shao-Hui, and Jin-Lan Liu. "The MCMC approach for solving the Pareto/NBD model and possible extensions." Natural Computation, 2007. ICNC 2007. Third International Conference on. Vol. 2. IEEE, 2007. \doi{10.1109/ICNC.2007.728}
• Pareto/NBD (Abe) Abe, Makoto. "Counting your customers one by one: A hierarchical Bayes extension to the Pareto/NBD model." Marketing Science 28.3 (2009): 541-553. \doi{10.1287/mksc.1090.0502}
• Pareto/GGG Platzer, Michael, and Thomas Reutterer. "Ticking Away the Moments: Timing Regularity Helps to Better Predict Customer Activity." Marketing Science (2016). \doi{10.1287/mksc.2015.0963}

Contributions

We certainly welcome all feedback and contributions to this package! Please use GitHub Issues for filing bug reports and feature requests, and provide your contributions in the form of Pull Requests. See also these general guidelines to contribute to Open Source projects on GitHub.