Automotive Customer Satisfaction Data Analysis Using Logistic Regression

2008-01-1468

04/14/2008

Event
SAE World Congress & Exhibition
Authors Abstract
Content
It is standard practice in the automotive industry to use the Customer Satisfaction (CS) metric, defined as the percentage of “high satisfaction” ratings, i.e. the percentage of customers who rate a vehicle feature either 9 or 10 on a 10 point scale. Based on the observation that this is equivalent to a transformation from discrete to binary, this paper introduces logistic regression as a natural choice for statistical analysis of CS data. The methodology proposed in this paper uses penalised maximum likelihood for model fitting and the Akaike Information Criterion (AIC) for model selection. AIC is also used for optimal selection of the shrinkage parameter. The paper also shows how this methodology can be used to identify factors associated with low customer satisfaction.
Meta TagsDetails
DOI
https://doi.org/10.4271/2008-01-1468
Pages
8
Citation
Grove, D., Campean, F., Zeppenfeld, J., Dixon, N. et al., "Automotive Customer Satisfaction Data Analysis Using Logistic Regression," SAE Technical Paper 2008-01-1468, 2008, https://doi.org/10.4271/2008-01-1468.
Additional Details
Publisher
Published
Apr 14, 2008
Product Code
2008-01-1468
Content Type
Technical Paper
Language
English