{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"}],"enrichment":{"capability":"Lifetimes models customer behavior and predicts lifetime value, churn, and repeat purchase likelihood using probabilistic survival-analysis methods on transaction history.","skillfed_tags":["customer-analytics","survival-analysis","churn-prediction"],"use_cases":["Predict which customers are likely to churn based on their purchase history and engagement patterns.","Estimate the lifetime value of customers to prioritize retention and marketing spend.","Forecast repeat purchase frequency and timing for subscription or e-commerce businesses.","Segment customers into cohorts by predicted value for targeted retention campaigns.","Analyze user engagement on websites or apps to identify inactive or at-risk users."],"what_it_does":"Lifetimes is a Python library for modeling and predicting customer lifetime value (CLV) and churn using probabilistic methods grounded in survival analysis. It implements models like BG/NBD and Pareto/NBD that estimate how often a customer will return and their long-term value based on historical transaction data (frequency, recency, and age). The library treats customer engagement as a \"birth-death\" process: users are alive while active and may die (churn) after inactivity.\n\nThe package depends on numpy, scipy, pandas, autograd, and dill for numerical computation and serialization. It is designed for business analytics workflows where you have transaction histories and want to segment customers by predicted value or identify likely churners. The library was last released in 2020 and its repository is now archived, meaning no active maintenance or updates are occurring.","worth_installing":"Yes, if you need CLV modeling and can accept an unmaintained package. The library is stable for its core use case (BG/NBD and Pareto/NBD models) and has no known vulnerabilities. However, verify dependency compatibility with your Python version first, and consider whether a maintained fork or alternative exists for your specific use case, given the project's archived status since 2024."},"id":"lifetimes","links":{"html":"https://skillfed.io/packages/lifetimes","md":"https://skillfed.io/packages/lifetimes.md","pypi":"https://pypi.org/project/lifetimes/"},"maintenance":{"status":"abandoned"},"meta":{"latest_release":"2020-07-06","license_spdx":null,"license_treatment":"permissive","name":"Lifetimes","python_support":"unspecified","summary":"Measure customer lifetime value in Python"},"popularity":{"monthly_downloads":255230,"position":8482,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.11.3"}