Automotive Customer Satisfaction Data Analysis Using Logistic Regression 2008-01-1468
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.
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. Download Citation
Author(s):
Dan Grove, Felician Campean, Janet Zeppenfeld, Neil Dixon, Simon Robinson
Affiliated:
University of Bradford, UK, Jaguar Cars, UK
Pages: 8
Event:
SAE World Congress & Exhibition
ISSN:
0148-7191
e-ISSN:
2688-3627
Also in:
CAD/CAM/CAE Technology, 2008-SP-2172
Related Topics:
Statistical analysis
Fittings
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