clumpi [klˈʌmpάɪ]

Overview

A simple python package to calculate Clumpiness for RMFC analysis by Zhang, Bradlow & Small (2015). Easy use with clumpi.get_RFC()

Requirements

• Python
• pandas
• numpy
• matplotlib

works well with Google Colab.

Installation

Simply use pip install for install this from PyPI:

pip install clumpi

or you can from Git repo:

pip install git+https://github.com/jniimi/clumpi.git

(perhaps, git repo version may be newer.)

Dataset

Use your discrete time-series event data with ID and time.

• Create DataFrame that records only the point in time when the event occurred in the time series data.
• The name of the variables can be anything.

Check out our sample dataset for further details.

df = clumpi.load_sample_data()
display(df)

Usage

Log to Clumpiness

Use the function clumpi.get_RFC() to calculate. Specify following arguments.

• id: a column name indicating user

• t: a column name indicating discrete time (e.g., a day in a month), t starts from 1 (Not 0).

• N: a maximum number of events during the period (e.g., # of days in a month)

• M (optional): a number of iterations for the simulation to calculate threshold (3000 for default)

• alpha (optional): a significance level for the test of regularity (0.05 for default)

• plot (optional): bool whether plot the frequency by users

  By specifying the argument plot=True, then the function displays the following scatter plot.

Simply Calculate H0

Use the function clumpi.calc_threshold() to calculate upper alpha % point in M times simulation.

All you need to specify are N, M, and alpha (See clumpi.get_RFC).

Acknoledgement

The simulation in this package is based on Appendix B by Zhang et al. (2015).

Zhang, Y., Bradlow, E. T., & Small, D. S. (2015). Predicting customer value using clumpiness: From RFM to RFMC. Marketing Science, 34(2), 195-208. https://doi.org/10.1287/mksc.2014.0873

Author and Citation

jniimi (@JvckAndersen)

@misc{niimi2024clumpi,
  author = {Junichiro Niimi},
  title = {clumpi: A Python package for calculating Clumpiness in RFMC analysis},
  year = {2024},
  publisher = {Zenodo},
  doi = {10.5281/zenodo.15202295},
  url = {https://github.com/jniimi/clumpi}
}