clvkit vs CLVTools
clvkit
github.comCLVTools
github.comSide by side
| Figure | clvkit | CLVTools |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| Plans published | 0 | 1 |
| Spec score · Customer Lifetime Value Software | 7.1 (#30) | 8.4 (#3) |
| License | The package metadata identifies its license as BSD-3-Clause. | The package is licensed under GPL-3. |
| Maintainer | The README says the author is building customer-analytics work on top of clvkit and intends the library to remain installable and correct. | The package authors page identifies Patrick Bachmann as maintainer and author. |
| Models | The model roster is BG/NBD, MBG/NBD, and Gamma-Gamma, with CLV composing transaction and spend models. | It implements Pareto/NBD, BG/NBD, Gamma/Gompertz/NBD, and Gamma/Gamma models. |
| Purpose | clvkit is a Python library for estimating customer lifetime value and cohort retention from transaction logs for non-contractual businesses. | CLVTools is an R package for probabilistic customer attrition modeling in non-contractual settings. |
| Support | The project links to its GitHub Issues page for issue reporting. | The project invites bug reports through GitHub Issues and contributions through pull requests. |
Plans
In their own words
CLVTools
CLVTools is a free, GPL-3-licensed R package for probabilistic customer attrition modeling when purchases are not governed by contracts. It implements Pareto/NBD, BG/NBD, Gamma/Gompertz/NBD, and Gamma/Gamma models. Using transaction histories with customer IDs and purchase dates, plus prices in its example data, it predicts individual customer behavior such as expected future transactions and the probability that a customer remains active. Forecast horizons can be configured, supporting predictive customer lifetime value work, cohort analysis, and customer segmentation. Time-invariant covariates work across the models, while some models accept time-varying covariates. Extensions include correlated purchase and attrition processes, L2 regularization, and equality constraints for contextual-factor parameters. Stable installation is available from CRAN; a development version uses GitHub and devtools. Linux, macOS, and Windows are supported, though compiling from source requires the external GNU GSL C++ library. Documentation includes a walkthrough, reference manual, and vignettes.