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https://github.com/ColtAllen/btyd
Buy Till You Die and Customer Lifetime Value statistical models in Python.
https://github.com/ColtAllen/btyd
bayesian buy-til-you-die customer-lifetime-value data-science python
Last synced: 3 months ago
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Buy Till You Die and Customer Lifetime Value statistical models in Python.
- Host: GitHub
- URL: https://github.com/ColtAllen/btyd
- Owner: ColtAllen
- License: apache-2.0
- Fork: true (CamDavidsonPilon/lifetimes)
- Created: 2022-02-08T16:22:30.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-05-14T19:57:56.000Z (6 months ago)
- Last Synced: 2024-07-15T23:16:48.846Z (4 months ago)
- Topics: bayesian, buy-til-you-die, customer-lifetime-value, data-science, python
- Language: Python
- Homepage: https://btyd.readthedocs.io/
- Size: 7.06 MB
- Stars: 113
- Watchers: 9
- Forks: 8
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.txt
- Code of conduct: CODE_OF_CONDUCT.md
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README
# BTYD
[](https://gist.github.com/cheerfulstoic/d107229326a01ff0f333a1d3476e068d)
[](https://badge.fury.io/py/btyd)
[](https://github.com/ColtAllen/btyd/blob/master/LICENSE.txt)
## READ FIRST: Project Status
Development has transitioned to a new project repo: [pymc-marketing](https://github.com/pymc-labs/pymc-marketing).
## Introduction
BTYD is the successor to the [Lifetimes](https://github.com/CamDavidsonPilon/lifetimes) library for implementing Buy Till You Die and Customer Lifetime Value statistical models in Python. All existing Lifetimes functionality is supported, and Bayesian [PyMC](https://github.com/pymc-devs) model variants are also supported.
BTYD can be used to analyze your users based on the following assumptions:
1. Users interact with you when they are active, or "alive"
2. Users under study may "die" or become inactive after some period of time
If this is too abstract, consider these applications:
- Predicting how often a visitor will return to your website. (Alive = visiting. Die = decided the website wasn't for them)
- Understanding how frequently a patient may return to a hospital. (Alive = visiting. Die = maybe the patient moved to a new city, or became deceased.)
- Predicting individuals who have churned from an app using only their usage history. (Alive = logins. Die = removed the app)
- Predicting repeat purchases from a customer. (Alive = actively purchasing. Die = became disinterested with your product)
- Predicting the lifetime value of your customers
## Installation
BTYD installation requires Python 3.8 or 3.9:
```bash
pip install btyd
```
## Contributing
Please refer to the [Contributing Guide](https://github.com/ColtAllen/btyd/blob/master/CONTRIBUTING.md) before creating any *Pull Requests*.
## Documentation and Tutorials
[User Guide](https://btyd.readthedocs.io/en/latest/User%20Guide.html)
## Questions? Comments? Requests?
Please create an issue in the [BTYD repository](https://github.com/ColtAllen/btyd/issues).
## Supported Models
- **BG/NBD** Fader, Peter S., Bruce G.S. Hardie, and Ka Lok Lee (2005a),
["Counting Your Customers the Easy Way: An Alternative to the
Pareto/NBD Model"](http://brucehardie.com/papers/018/fader_et_al_mksc_05.pdf), Marketing Science, 24 (2), 275-84.
- **Gamma-Gamma** Fader, Peter & G. S. Hardie, Bruce (2013). ["The Gamma-Gamma Model of Monetary Value"](http://www.brucehardie.com/notes/025/gamma_gamma.pdf).
- **Modified BG/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.
## Additional Information
1. R implementation is called [BTYDplus](https://github.com/mplatzer/BTYDplus).
1. [Bruce Hardie's website](http://brucehardie.com/), especially his notes, is full of useful and essential explanations, many of which are featured in this library.