Publications by Joel Cadwell
The Brand as Affordance: Item Response Modeling of Brand Perceptions
It is just too easy to think of a brand as a web of associations. What comes to mind when I say “Subway Sandwich”? Did you remember a commercial or the “eat fresh” tagline? Without much effort, one can generate a long list of associations with the Subway brand, and why not map all those associations with a network struct...
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Using Heatmaps to Uncover the Individual-Level Structure of Brand Perceptions
Heatmaps, when the rows and columns are appropriately ordered, provide insight into the data structure at the individual level. In an earlier post I showed a cluster heatmap with dendrograms for both the rows and the columns. In addition, I provided an example of what a heatmap might look like if the underlying structure were a sc...
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Latent Variable Mixture Modeling: When Heterogeneity Requires Both Categories and Dimensions
Dichotomies come easily to us, especially when they are caricatures as shown in this cartoon. These personality types do seem real, and without much difficulty, we can anticipate how they might react in different situations. For example, if we were to give our Type A and Type B vacationers a checklist to indicate what activities they would li...
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Remembering the Gist, But Not the Details: One-Dimensional Representation of Consumer Ratings
In survey research, it makes a difference how the question is asked. “How would you rate the service you received at that restaurant?” is not the same as “Did you have to wait to be seated, to order your meal, to be served your food, or to pay your bill?” Questions about specific occurrences can be answered only by recolle...
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Key Driver vs. Network Analysis in R
When marketing researchers speak of driver analysis, they are referring to an input-output model with overall satisfaction as the output and performance ratings of specific product and service components as the inputs. The causal model is straightforward: brands provide the stuff customers want, customers see this and they are happy. What custo...
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Maximizing Return from Every Item in the Marketing Research Questionnaire
Consumers will not complete long questionnaires, so marketing research must get the most it can from every item. In this post, we look into the toolbox of R packages and search for statistical models that enable us to learn a great deal about each individual without demanding much information from any one respondent.Bradley Efron an...
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Feature Prioritization: Multiple Correspondence Analysis Reveals Underlying Structure
Measuring the Power of Product Features to Generate Increased DemandProduct management requires more from feature prioritization than a rank ordering. It is simply not enough to know the “best” feature if that best does not generate increased demand. We are not searching for the optimal features in order to design a product that no one will b...
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The Complexities of Customer Segmentation: Removing Response Intensity to Reveal Response Pattern
At the end of the last post, the reader was left assuming respondent homogeneity without any means for discovering if all of our customers adopted the same feature prioritization. To review, nine features were presented one at a time, and each time respondents reported the likely impact of adding the feature to the current product. Re...
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Latent Variable Mixture Models (LVMM): Decomposing Heterogeneity into Type and Intensity
Adding features to a product can be costly, so brands have an incentive to include only those features most likely to increase demand. In the last two posts (first link and second link), I have recommended what could be called a “features stress test” that included both a data collection procedure and some suggestions for how to ...
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An Introduction to Statistical Learning with Applications in R
Statistical learning theory offers an opportunity to those of us trained as social science methodologists to look at everything we have learned from a different perspective. For example, missing value imputation can be seen as matrix completion and recommender systems used to fill-in empty questionnaire items that were never shown to more than a ...
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