Measure customer lifetime value in Python
Project description
Measuring users is hard. Lifetimes makes it easy.
Introduction
Lifetimes can be used to analyze your users based on a few assumption:
Users interact with you when they are “alive”.
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.
Applications
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
Specific Application: Customer Lifetime Value
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. Lifetimes is a Python library to calculate CLV for you.
Installation
pip install lifetimes
Requirements are only Numpy, Scipy, Pandas, Dill (and optionally-but-seriously matplotlib).
Documentation and tutorials
Questions? Comments? Requests?
Please create an issue in the lifetimes repository.
More Information
Roberto Medri did a nice presentation on CLV at Etsy.
R implementation is called BTYD (for, Buy ’Til You Die).
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