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Activity Number:
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364
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Type:
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Contributed
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Date/Time:
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Tuesday, August 4, 2009 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics and Marketing
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| Abstract - #305533 |
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Title:
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Quantile Regression as a Tool for Identifying Drivers of Satisfaction and Dissatisfaction
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Author(s):
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Jorge A. Alejandro*+ and Kurt A. Pflughoeft
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Companies:
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Market Probe and Market Probe
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Address:
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2655 N Mayfair Rd, Milwaukee, WI, 53226,
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Keywords:
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Satisfiers ; Dissatisfiers ; Quantile Regression ; Relative Importance ; Attitudinal Drivers ; Overall Customer Satisfaction
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Abstract:
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Drivers of customer satisfaction are a key tool for management in guiding and improving product offerings. Many statistical techniques can be used to determine how satisfaction scores for product attributes "drive" overall satisfaction (see the Multi-Attribute Attitude model.) Most driver estimation techniques assume a linear relationship where high satisfaction scores on product attributes lead to high overall satisfaction scores and vice versa. However, the relationship may vary across not only different levels of satisfaction but also among attribute types (see Kano models). In this research, the use of quantile regression is applied to a case study to determine how drivers of dissatisfaction may vary from drivers of satisfaction. The results of the quantile regression are compared to other commonly used driver techniques in market research.
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