👋 This codebase has moved to "maintenance-mode". I won't be adding new features, improvements, or even answering issues in this codebase (but perhaps the occasional bug fix). Why? I don't use lifetimes anymore, nor do I keep up with the literature around RFM.

A project has emerged as a successor to lifetimes, PyMC-Lab/PyMC-Marketing, please check it out!

Lifetimes can be used to analyze your users based on a few assumption:

1. Users interact with you when they are "alive".
2. Users under study may "die" after some period of time.

I've quoted "alive" and "die" as these are the most abstract terms: feel free to use your own definition of "alive" and "die" (they are used similarly to "birth" and "death" in survival analysis). Whenever we have individuals repeating occurrences, we can use Lifetimes to help understand user behaviour.

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

As emphasized by P. Fader and B. Hardie, understanding and acting on customer lifetime value (CLV) is the most important part of your business's sales efforts. And (apparently) everyone is doing it wrong (Prof. Fader's Video Lecture). Lifetimes is a Python library to calculate CLV for you.

pip install lifetimes

Please refer to the Contributing Guide before creating any Pull Requests. It will make life easier for everyone.

Official documentation

Please create an issue in the lifetimes repository.

1. Probably, the seminal article of Non-Contractual CLV is Counting Your Customers: Who Are They and What Will They Do Next?, by David C. Schmittlein, Donald G. Morrison and Richard Colombo. Despite it being paid, it is worth the read. The relevant information will eventually end up in this library's documentation though.
2. The other (more recent) paper is “Counting Your Customers” the Easy Way: An Alternative to the Pareto/NBD Model, by Peter Fader, Bruce Hardie and Ka Lok Lee.

1. Roberto Medri did a nice presentation on CLV at Etsy.
2. Papers, lots of papers.
3. R implementation is called BTYD (Buy 'Til You Die).
4. Bruce Hardie's Website, especially his notes, is full of useful and essential explanations, many of which are featured in this library.