clvkit

6.1easy start · #14 of 37
in Customer Lifetime Value Software

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clvkit is ranked #14 of 37 in customer lifetime value software on The Geeks Club.

Compared on customer lifetime value software

Predictive CLV: Yes
Cohort analysis: Yes
Forecast horizon: configurable
Data refresh: not stated

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The record

Purpose: clvkit provides buy-till-you-die models for estimating customer lifetime value in non-contractual businesses.
Core models: Its models include BG/NBD, MBG/NBD for customers who may never return after their first purchase, and Gamma-Gamma for spend per transaction.
CLV and cohorts: The library composes purchase and spend models to estimate discounted lifetime value and provides cohort retention, revenue, and survival analysis.
Input and output: It accepts transaction logs with customer ID, date, and amount, and prediction results can be converted to pandas DataFrames or JSON and plotted.
Python requirements: Installation is available with uv or pip and requires Python 3.11 or later.
Dependencies: The project lists numpy, scipy, pandas, and matplotlib as its four runtime dependencies, with Dask available as an optional extra for summarizing large logs.
Validation: The README says CI checks that BG/NBD and Gamma-Gamma fits reproduce published estimates and runs those checks on pushes, pull requests, and before releases.
Limits: The stated model scope excludes covariates, Bayesian fitting, and additional model families.
Large logs: The README reports that Dask becomes faster than pandas at around four million transactions, while its default in-process scheduler used more peak memory in the measured benchmarks.
License: The repository identifies its license as BSD-3-Clause.
Maintainer: The README says the author is building customer-analytics work on top of clvkit and intends the library to remain installable and correct.

Where it ranks on The Geeks Club

Best Customer Lifetime Value Software in 2026#14 of 37

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Sources

github.com/nasserboan/clvkit· checked 4 Oct 2026