Development has transitioned to a new project repo: pymc-marketing.
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 variants are also supported.
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
BTYD installation requires Python 3.8 or 3.9:
pip install btydPlease refer to the Contributing Guide before creating any Pull Requests.
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.
- Gamma-Gamma Fader, Peter & G. S. Hardie, Bruce (2013). "The Gamma-Gamma Model of Monetary Value".
- 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.
- 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.