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On the recordRead 11 Oct 202619 facts
lifetimes
On the record
- 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 · read 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 · read 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 · read 11 Oct 2026
- Installation
- The documented installation command is `pip install lifetimes`.lifetimes.readthedocs.io · read 11 Oct 2026
- License
- The GitHub repository identifies the project as MIT licensed.github.com · read 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 · read 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 · read 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 · read 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 · read 11 Oct 2026
- Product type
- Lifetimes is a Python library for calculating customer lifetime value.lifetimes.readthedocs.io · read 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 · read 11 Oct 2026
- Purchase prediction
- The BG/NBD model predicts expected purchases from customer frequency, recency, and age data.lifetimes.readthedocs.io · read 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 · read 11 Oct 2026
- Successor
- The repository points users to PyMC-Marketing as a successor project.github.com · read 11 Oct 2026
- Support
- The documentation directs questions, comments, and requests to issues in the Lifetimes repository.lifetimes.readthedocs.io · read 11 Oct 2026
- Use cases
- The documentation describes predicting repeat visits, patient returns, app churn, repeat purchases, and customer lifetime value.lifetimes.readthedocs.io · read 11 Oct 2026
- Visualization
- The library includes plots for purchase counts, expected purchases, active probability, customer history, and transaction-rate heterogeneity.lifetimes.readthedocs.io · read 4 Oct 2026
- Visualizations
- The library includes plots for purchase predictions, probability a customer remains active, transaction histories, and model comparisons.lifetimes.readthedocs.io · read 11 Oct 2026
