Blog Archives

Archetypal Analysis: Similarity Defined by Distances from Contrasting Ideals

December 5, 2014
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Archetypal Analysis: Similarity Defined by Distances from Contrasting Ideals

Carl Jung was at least partially correct. We do tend to think in terms of the extremes as shown in this archetypal wheel with rulers versus outlaws and heroes versus caregivers at different ends of bipolar dimensions. Happily, we are not required to ac...

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In Praise of Substantive Expertise in Data Science

November 14, 2014
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In Praise of Substantive Expertise in Data Science

Substantive expertise makes it into the Data Science Venn Diagram from DataCamp's infographic on how to become a data scientist. It's one of the three circles of equal size along with programming and statistics. Regrettably, substantive expertise ...

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Building Blocks: A Compelling Image for Clustering with Nonnegative Matrix Factorization (NMF)

November 12, 2014
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Building Blocks: A Compelling Image for Clustering with Nonnegative Matrix Factorization (NMF)

Would hierarchical clustering be as popular without the dendrogram? Cannot the same be said of finite mixture modeling with its multidimensional spaces populated by normal distributions? I invite you to move your mouse over the figure on the introducto...

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Net Promoter Mixture Modeling: Can a Single Likelihood Rating Reveal Customer Segments?

November 5, 2014
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Net Promoter Mixture Modeling: Can a Single Likelihood Rating Reveal Customer Segments?

Net Promoter believes that customers come in one of three forms: promoters (happy yellows), passives (neutral grays), or detractors (angry reds). Cluster identification is relatively easy for all you need to do is ask the "ultimate question" concerning...

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Let’s Do Some MORE Hierarchical Bayes Choice Modeling in R!

November 3, 2014
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Let’s Do Some MORE Hierarchical Bayes Choice Modeling in R!

R enables us to "test drive" statistical models by running simulations and analyzing actual data. We can look at the output and get some sense of how the whole thing works. The goal is to provide a realistic application that is not too difficult to fol...

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Combating Multicollinearity by Asking the Right Questions and Uncovering Latent Features

October 26, 2014
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Combating Multicollinearity  by Asking the Right Questions and Uncovering Latent Features

Overview. When responding to questions about brand perceptions or product feature satisfaction, consumers construct a rating  by relying on their overall satisfaction with the brand or product plus some general category knowledge of how diffi...

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Modeling Plenitude and Speciation by Jointly Segmenting Consumers and their Preferences

October 21, 2014
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Modeling Plenitude and Speciation by Jointly Segmenting Consumers and their Preferences

In 1993, when music was sold in retail stores, it may have been informative to ask about preference across a handful of music genre. Today, now that the consumer has seized control and the music industry has responded, the market has exploded into more...

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Beware Graphical Networks from Rating Scales without Concrete Referents

October 15, 2014
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Beware Graphical Networks from Rating Scales without Concrete Referents

We think of latent variables as hidden causes for the correlations among observed measures and rely on factor analysis to reveal the underlying structure. In a previous post, I borrowed an alternative metaphor from the R package qgraph and produce...

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Consumer Preference Driven by Benefits and Affordances, Yet Management Sees Only Products and Features

October 3, 2014
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Consumer Preference Driven by Benefits and Affordances, Yet Management Sees Only Products and Features

Return on Investment (ROI) is management's bottom line. Consequently, everything must be separated and assigned a row with associated costs and profits. Will we make more by adding another product to our line? Will we lose sales by limiting the feature...

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TURF Analysis: A Bad Answer to the Wrong Question

September 29, 2014
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TURF Analysis: A Bad Answer to the Wrong Question

Now that R has a package performing Total Unduplicated Reach and Frequency (TURF) Analysis, it might be a good time to issue a warning to all R users. DON'T DO IT!The technique itself is straight out of media buying from the 1950s. Given some number of...

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