https://github.com/mplatzer/BTYDplus
R package for Customer Behavior Analysis
https://github.com/mplatzer/BTYDplus
crm customer-behavior marketing-science predictive-analytics rstats
Last synced: 4 months ago
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R package for Customer Behavior Analysis
- Host: GitHub
- URL: https://github.com/mplatzer/BTYDplus
- Owner: mplatzer
- License: gpl-3.0
- Created: 2014-05-15T17:00:07.000Z (almost 11 years ago)
- Default Branch: master
- Last Pushed: 2024-04-08T20:49:19.000Z (about 1 year ago)
- Last Synced: 2024-04-14T06:55:24.510Z (about 1 year ago)
- Topics: crm, customer-behavior, marketing-science, predictive-analytics, rstats
- Language: R
- Homepage: https://cran.r-project.org/web/packages/BTYDplus
- Size: 929 KB
- Stars: 180
- Watchers: 35
- Forks: 49
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS
- License: LICENSE.md
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README
# BTYDplus
[](https://travis-ci.org/mplatzer/BTYDplus)
[](https://www.gnu.org/licenses/gpl-3.0.html)
[](https://CRAN.R-project.org/package=BTYDplus)
The BTYDplus [R](https://www.r-project.org/) 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](https://cran.r-project.org/package=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](https://github.com/mplatzer/BTYDplus/issues) for filing bug reports and feature requests, and provide your contributions in the form of [Pull Requests](https://help.github.com/articles/about-pull-requests/). See also [these general guidelines](https://guides.github.com/activities/contributing-to-open-source/#contributing) to contribute to Open Source projects on GitHub.