Blog Archives

Extracting Latent Variables from Rating Scales: Factor Analysis vs. Nonnegative Matrix Factorization

August 21, 2014
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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....

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Exploiting Heterogeneity to Reveal Consumer Preference: Data Matrix Factorization

August 11, 2014
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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 abou...

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Customer Segmentation Using Purchase History: Another Example of Matrix Factorization

August 2, 2014
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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 with some features...

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Variable Selection in Market Segmentation: Clustering or Biclustering?

July 29, 2014
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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 a...

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Uncovering the Preferences Shaping Consumer Data: Matrix Factorization

July 23, 2014
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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 re...

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Taking Inventory: Analyzing Data When Most Answer No, Never, or None

July 15, 2014
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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 bu...

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How Much Can We Learn from Top Rankings using Nonnegative Matrix Factorization?

July 10, 2014
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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...

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Are Consumer Preferences Deep or Shallow?

July 8, 2014
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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 e...

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Using Biplots to Map Cluster Solutions

July 2, 2014
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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 ...

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Separating Statistical Models of "What Is Learned" from "How It Is Learned"

June 21, 2014
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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...

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