Publications by Joel Cadwell
Separating Statistical Models of "What Is Learned" from "How It Is Learned"
Something triggers our interest. Possibly it’s an ad, a review or just word of mouth. We want to know more about the movie, the device, the software, or the service. Because we come with different preferences and needs, our searches vary in intensity. For some it is one and done, but others expend some effort and seek out many sourc...
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Using Biplots to Map Cluster Solutions
FactoMineR is a quick and easy R package for generating biplots, such as the following plot showing the columns as arrows with the rows to be added later as points. As you might recall from a previous post, a biplot maps a data matrix by plotting both the rows and columns in the same figure. Here the columns (variables) are arrows and the rows (i...
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Are Consumer Preferences Deep or Shallow?
John Hauser, because no one questions his expertise, is an excellent spokesperson for the viewpoint that consumer preferences are real, as presented in his article “Self-Reflection and Articulated Consumer Preferences.” Simply stated, preferences are enduring when formed over time and after careful consideration of actual products...
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How Much Can We Learn from Top Rankings using Nonnegative Matrix Factorization?
Purchases are choices from available alternatives. Post-purchase, we know what is the most preferred, but all the other options score the same. Regardless of differences in appeal, all the remaining items received the same score of not chosen. A second choice tells us more, as would the alternative selected as third most preferred. A...
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Taking Inventory: Analyzing Data When Most Answer No, Never, or None
Consumer inventories, as the name implies, are tallies of things that consumers buy, use or do. Product inventories, for example, present consumers with rather long lists of all the offerings in a category and ask which or how many or how often they buy each one. Inventories, of course, are not limited to product listings. A tourist s...
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Uncovering the Preferences Shaping Consumer Data: Matrix Factorization
How do you limit your search when looking for a hotel? Those trying to save money begin with price. Members of hotel reward programs focus on their brand. At other times, location is first to narrow our consideration set. What does hotel search reveal about hotel preference?What do consumer really want in a hotel? I could simply provide a list o...
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Variable Selection in Market Segmentation: Clustering or Biclustering?
Will you have that segmentation with one or two modes?The data matrix for market segmentation comes to us with two modes, the rows are consumers and the columns are variables. Clustering uses all the columns to transform the two-mode data matrix (row and columns are different) into a one-mode distance matrix (rows and columns are the same) either...
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Customer Segmentation Using Purchase History: Another Example of Matrix Factorization
As promised in my last post, I am following up with another example of how to perform market segmentations with nonnegative matrix factorization. Included with the R package bayesm is a dataset called Scotch containing the purchase history for 21 brands of whiskey over a one year time period from 2218 respondents. The brands along wit...
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Exploiting Heterogeneity to Reveal Consumer Preference: Data Matrix Factorization
We begin with a data matrix, a set of numbers arrayed so that each row contains information from a different consumer. Marketing research focuses on the consumer, but the columns are permitted more freedom, although they ought to tell us something about consumer perception, preference or consumption. Whatever data have been collected, we organize...
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Extracting Latent Variables from Rating Scales: Factor Analysis vs. Nonnegative Matrix Factorization
For many of us, factor analysis provides a gateway to learning how to run and interpret nonnegative matrix factorization (NMF). This post will analyze a set of ratings on a 218 item adjective checklist using both principal axis factor analysis and NMF. The entire analysis will be performed in R using less than two dozen lines of code ...
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