ShapKa 1.1.1

Key Satisfaction Drivers Analysis based on Shapley values and Kano model

ShapKa: an open source python library to identify customer satisfaction drivers based on Shapley values and Kano model

Installation

Use the following command to install the package:

pip install ShapKa

Usage

Use the following command for a key dissatisfaction drivers analysis (kda) :

import pandas as pd
from ShapKa.kanomodel import KanoModel

# Load data
df = pd.read_csv('data/example_03.csv')

# Define X and Y variables names
y_varname = 'Overall Satisfaction'
weight_varname = 'Weight'
X_varnames = df.columns.values.tolist()
X_varnames.remove(y_varname)
X_varnames.remove(weight_varname)

# Run analysis to identify key dissatisfiers
model = KanoModel(df,
                  y_varname, X_varnames,
                  analysis = 'kda',
                  y_dissat_upperbound = 6, y_sat_lowerbound = 9,
                  X_dissat_upperbound = 6, X_sat_lowerbound = 9,
                  weight_varname = weight_varname)

kda = model.key_drivers() ;kda

Here is the ouput :

Replace ‘kda’ by ‘kea’ in the analysis parameter if you want to identify key enhancers (kea) instead of key dissatisfiers

Documentation

• Documentation: https://shapleykano.readthedocs.io.

Credits

• The ShapKa package is based on the methodology developped by W. Michael Conklin, Ken Powaga and Stan Lipovetsky

• Some parts of the code are based on functions implemented in the Open Source Sage Mathematical Software

References

• Conklin, Michael & Powaga, Ken & Lipovetsky, Stan. (2004). Customer satisfaction analysis: Identification of key drivers. European Journal of Operational Research. 154. 819-827. 10.1016/S0377-2217(02)00877-9.

• Sage - Open Source Mathematical Software : https://github.com/sagemath/sage

History

1.1.1 (2020-06-03)

• Minor Bug Correction.

1.1.0 (2020-06-03)

• Add weighting data functionnality.

1.0.0 (2019-10-25)

• First release on PyPI.