BTYD is the successor to the 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 model implementations are presently in an alpha stage of development.
BTYD can be used to analyze your users based on the following assumptions:
- Users interact with you when they are active, or "alive"
- 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
pip install btydPlease refer to the Contributing Guide before creating any Pull Requests.
Official Documentation of pre-fork Lifetimes library
Please create an issue in the BTYD repository.
- 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", Marketing Science, 24 (2), 275-84.
- R implementation is called BTYDplus.
- Bruce Hardie's website, especially his notes, is full of useful and essential explanations, many of which are featured in this library.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent