clumpi 0.0.1

Calculate Clumpiness index by Zhang, Bradlow and Small (2015)

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

works well with Google Colab.

Installation

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

Dataset

Use your 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.

user_id	t
Ava	1
Ava	4
...	...
Jack	3
Jack	10
...	...

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 information for the arguments.

• id: a var name in df indicating user
• t: a var name in df indicating time
• N: total number of events can occur during the period
• M (optional): a number of iterations for the simulation to calculate threshold (3000 for default)
• alpha (optional): significance probability for the test of regularity (0.05 for default)

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

jniimi (@JvckAndersen)