Your status: not signed in. [ Multicollinearity in Customer Satisfaction Research
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:
- Ordinary Least Squares
- Shapley Value Regression
- Penalty & Reward Analysis
- Kruskal’s Relative Importance
- Partial Least Squares
- 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.
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