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lifetimes

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What lifetimes is

lifetimes is an open-source Python library for calculating customer lifetime value from repeat activity. It can analyze activity patterns to predict website visits, hospital returns, app churn, repeat purchases, and customer lifetime value. Its BG/NBD model uses customer frequency, recency, and age data to predict expected purchases. Utilities convert transaction-level records into those summary measures, and the library can estimate the probability that a customer remains active, then show that probability in a matrix.

For value estimates, the Gamma-Gamma submodel estimates customer lifetime value under the assumption that monetary value is independent of purchase frequency. Its visualizations cover purchase counts, expected purchases, active probability, customer history, and differences in transaction rates. The documentation also describes assessing a fitted model by comparing simulations with actual purchases and checking predictions against a holdout period. Predictive customer lifetime value and profitability metrics are supported, with a configurable forecast horizon. Install it with `pip install lifetimes`. The project repository is archived and read-only; its maintainers say they will not add features or improvements or answer issues. The archived repository points to PyMC-Marketing as a successor project.

Who it suits

lifetimes suits Python users who want to analyze repeat transactions, forecast expected purchases, or estimate customer lifetime value. It can also serve analysts studying customer activity patterns such as churn, repeat visits, or purchases, provided they are comfortable using a project whose repository is archived.

What it does well

BG/NBD predicts expected purchases from frequency, recency, and age.
Gamma-Gamma estimates customer lifetime value.
Estimates the probability a customer remains active.
Utilities summarize transaction records for modeling.
Includes plots for purchase behavior and customer history.
Forecast horizon is configurable.

What to know first

The repository is archived and read-only.
Maintainers say they will not add features or answer issues.

Getting started

Open the lifetimes documentation website.
Install the library with `pip install lifetimes`.
Prepare transaction records with the library's frequency, recency, and age utilities.
Fit a model and assess its predictions against actual purchases or a holdout period.

Verdict

Choose lifetimes if you need a Python library for repeat-activity analysis, purchase predictions, or customer lifetime value estimates. Its models and plots cover several steps of that work. Look elsewhere if you need ongoing feature development or issue responses from the maintainers.

Questions

How much does lifetimes cost?

It is free and open source.

Is there a free plan or trial?

A free plan is listed; there is no free trial.

What platform does it support?

Its listed platform is API.

What does lifetimes predict?

Its BG/NBD model predicts expected purchases, and it can estimate whether a customer remains active. Its Gamma-Gamma submodel estimates customer lifetime value.

Is the project still maintained?

The repository was archived on June 28, 2024, and is read-only. Its maintainers say they will not add features or improvements or answer issues.

What is named as its successor?

The archived repository identifies PyMC-Marketing as a successor project.

Facts from lifetimes's own pages

Customer lifetime value: The Gamma-Gamma submodel estimates average customer value, and the docs demonstrate combining it with a purchase model to estimate lifetime value. · lifetimes.readthedocs.io, 11 Oct 2026
Customer status: The library can estimate a customer's probability of still being active and visualize it in a probability matrix. · lifetimes.readthedocs.io, 4 Oct 2026
Data preparation: Utility functions transform transaction-level records into customer summary data and split data into calibration and holdout periods. · lifetimes.readthedocs.io, 11 Oct 2026
Installation: The documented installation command is `pip install lifetimes`. · lifetimes.readthedocs.io, 11 Oct 2026
License: The GitHub repository identifies the project as MIT licensed. · github.com, 11 Oct 2026
Lifetime value: The Gamma-Gamma submodel estimates customer lifetime value and assumes monetary value is independent of purchase frequency. · lifetimes.readthedocs.io, 4 Oct 2026
Maintenance status: The project repository was archived on June 28, 2024, and is read-only; its maintainers say they will not add features, improvements, or answer issues. · github.com, 4 Oct 2026
Model assessment: The documentation shows comparing fitted-model simulations with actual purchases and checking predictions against a holdout period. · lifetimes.readthedocs.io, 4 Oct 2026
Model assumption: The Gamma-Gamma model assumes monetary value and purchase frequency are unrelated, and the documentation recommends checking their correlation before using it. · lifetimes.readthedocs.io, 11 Oct 2026
Product type: Lifetimes is a Python library for calculating customer lifetime value. · lifetimes.readthedocs.io, 4 Oct 2026
Project status: The repository was archived on June 28, 2024, and its maintainers say they will not add features or answer issues there. · github.com, 11 Oct 2026
Purchase prediction: The BG/NBD model predicts expected purchases from customer frequency, recency, and age data. · lifetimes.readthedocs.io, 4 Oct 2026
Repeat purchase model: The quickstart demonstrates the BG/NBD model to estimate expected purchases from customer frequency, recency, and age data. · lifetimes.readthedocs.io, 11 Oct 2026
Successor: The repository points users to PyMC-Marketing as a successor project. · github.com, 11 Oct 2026
Support: The documentation directs questions, comments, and requests to issues in the Lifetimes repository. · lifetimes.readthedocs.io, 11 Oct 2026
Use cases: The documentation describes predicting repeat visits, patient returns, app churn, repeat purchases, and customer lifetime value. · lifetimes.readthedocs.io, 11 Oct 2026
Visualization: The library includes plots for purchase counts, expected purchases, active probability, customer history, and transaction-rate heterogeneity. · lifetimes.readthedocs.io, 4 Oct 2026
Visualizations: The library includes plots for purchase predictions, probability a customer remains active, transaction histories, and model comparisons. · lifetimes.readthedocs.io, 11 Oct 2026
What it is: Lifetimes is a Python library for analyzing repeat customer activity and calculating customer lifetime value. · lifetimes.readthedocs.io, 11 Oct 2026

Where lifetimes stands on Freedom251

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