Regression analysis is one tool for evaluating customer satisfaction measurement. Non-response is problematic for multiple regression analysis because most software discards all of a respondent’s data when it encounters a missing value. This article discusses options for coping with item non-response in regression runs, comparing run results based on a real data set.
Marketing researchers are regularly faced with a variety of challenges, including tight deadlines, demands for results that are concise and easy to understand, and an abundance of data. This article discusses multivariate analysis as a body of statistical techniques that helps researchers isolate actionable findings quickly and reduce them to a couple of charts and graphs.
This article describes the use of vulnerability analysis to show the relationship between an individual attribute and a dependent measure when analyzing data from customer satisfaction surveys. The author presents four ways in which vulnerability analysis goes beyond simply using a graph showing each attribute's importance and satisfaction levels in a simple two-dimensional scatterplot.
Findings from the annual Confirmit Market Research Software Survey show that firms feel they aren’t getting everything they need from existing software. As a result, they are developing their own programs and actively open to switching to those of other providers.
This article responds to an article by Doug Grisaffe's, published in this month’s Quirk’s Marketing Research Review, in which Grisaffe critiqued the author’s views about the strengths of using self-stated importance and the weaknesses of using multiple regression analysis to measure customer satisfaction.
The quality of a market analysis is judged by its validity. Unfortunately, data from non-probability, informal sample surveys lack measurable confidence. This article demonstrates an easy method of calculating the sample size needed for a specific market survey or experiment.
Michael Feehan, Cristina Ilangakoon and Penny Mesure
Using examples of Net Promoter Score data from two studies - one of patients assessing their primary care physicians and the other from the consumer electronics industry - the authors explore strategies for extracting insights from large qualitative data sets.
This first of two articles about analysis methods examines key driver analysis, including single dependent variable, multiple dependent variables, non-linearity, artificial intelligence, recent advances and tools.
Most researchers are already familiar with universal statistical methods. This article discusses multivariate statistical methods, including key characteristics of multivariate procedures and examples.
There are two fundamental questions in marketing research that appear at some point in most questionnaires: What did they buy? What’ll they buy next? This article discusses probability analysis technique, a procedure used to maximize the output from these two basic questions.
Assessing the prospects of a new product concept takes more than a cookie-cutter approach. Each concept requires its own set of strategies for how it will be presented to consumers in testing and a careful analysis of how the new idea fits corporate and brand objectives.
This article examines factors contributing to researchers’ increased interest in address-based sampling (ABS) and looks at the pros and cons of ABS. Against a backdrop of declining response rates, ABS appears to offer a convenient framework for effective design and implementation of surveys that employ multimode alternatives for data collection.
Few marketing managers account for business cycle variation in buyer attitude data. This article discusses how to remove business cycle impact from buyer attitude data to show actual effectiveness of brand management actions.
Customer satisfaction researchers often use statistical methods to infer how “important” various drivers are to overall satisfaction scores or customer loyalty indices. This article discusses latent class analysis, a technique that addresses respondent heterogeneity, which is one of the most significant pitfalls of traditional methods for computing derived importances. These methods provide a deeper understanding of individual differences between customers.
While customer satisfaction evaluations are widely used, score reporting isn't consistent. This article discusses various methods of reporting customer satisfaction scores, including an alternative the authors have found useful.