Multicollinearity in Customer Satisfacton

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Multicollinearity in Customer Satisfaction Research
Jay L.Weiner, Ph.D. Senior Vice President, Director of Marketing Sciences Jane Tang Vice President, Marketing Sciences

Editorial Board
Leigh Admirand Julie Busch Tim Keiningham

Design and Production
Roland Clifford Barbara Day

About Ipsos Loyalty
Ipsos Loyalty is a global, specialized practice dedicated to helping companies improve business performance through customer satisfaction management, customer relationship management, and employee climate management. Ipsos Loyalty provides a state-of-the-art approach to customer-driven business performance through a modular suite of innovative research tools that provides an integrated framework to identify complex global business solutions. Ipsos Loyalty is an Ipsos company, a leading global survey-based market research group. To learn more, visit www.ipsosloyalty.com.

About the Contributors
Jay Weiner, Ph.D., Senior Vice President, Marketing Sciences Jay consults with many leading corporations on marketing and market research issues. He specializes in applying advanced methods to help companies make better marketing and business decisions both domestically and globally. Jay has expertise in pricing, segmentation, customer and employee loyalty, conjoint analysis, and discrete choice analysis, in addition to solid multivariate statistical skills. Jay has his doctorate in marketing research from the University of Texas at Arlington, as well as an MBA in finance and commercial banking and a BBA in marketing. Jay has published and presented numerous papers on conjoint, choice, and pricing research in conference proceedings. Jane Tang, Vice President, Marketing Sciences Jane provides expertise in analytical and methodological market research using various statistical techniques, from the basic univariate procedure to advanced multivariate. She is known for her research on analytical techniques and adaptation of techniques to the market research environment. Recently, her efforts have been concentrated in apex adjustment, Hierarchical Bayes models, discrete choice models, segmentation, and database marketing. She also serves as a sampling consultant for many project teams. Jane has a B.Sc. and a M.Sc. in statistics from the University of Manitoba. She is also a graduate of the Sampling Program for Survey Statisticians from the Survey Research Center at the University of Michigan.

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Multicollinearity in Customer Satisfaction Research • © 2005, Ipsos • June 20 05

Abstract
This paper examines the strengths and weaknesses of four commonly used tools for modeling customer satisfaction data. Most customer satisfaction (CSAT) studies are plagued with multicollinearity, meaning that several of the independent causal variables are highly correlated, resulting in output that may cloak true drivers of satisfaction or dissatisfaction. When compounded by the fact that most CSAT studies are tracking studies, there is a significant challenge on how to model the data and deliver stable, actionable results to clients. As researchers and consultants, we must be sure that differences in results from one wave to the next are true differences in the market and not just, say, the result of a small number of respondents checking 8 instead of 7 on the last wave of a questionnaire. The six traditional CSAT modeling techniques compared in this paper are: 1. Ordinary Least Squares 2. Shapley Value Regression 3. Penalty & Reward Analysis 4. Kruskal’s Relative Importance 5. Partial Least Squares 6. Logistic Regression The comparison begins with results that show the relative impact of multicollinearity on each technique, using a simulated data set.Then, results based on bootstrap samples pulled from this data set show the relative stability of the various techniques. Finally, a case study demonstrates how the various methods perform with a real data set.

Introduction
In customer satisfaction (CSAT) studies, we often...
tracking img