Blog Archives

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 16, 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. ...

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Identifying Pathways in the Consumer Decision Journey: Nonnegative Matrix Factorization

June 13, 2014
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Identifying Pathways in the Consumer Decision Journey: Nonnegative Matrix Factorization

The Internet has freed us from the shackles of the yellow page directory, the trip to the nearby store to learn what is available, and the forced choice among a limited set of alternatives. The consumer is in control of their purchase journey and can t...

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The Unavoidable Instability of Brand Image

June 5, 2014
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The Unavoidable Instability of Brand Image

"It may be that most consumers forget the attribute-based reasons why they chose or rejected the many brands they have considered and instead retain just a summary attitude sufficient to guide choice the next time."This is how Dolnicar and Rossiter con...

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Which came first, the preference or the choice?

May 27, 2014
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Which came first, the preference or the choice?

Obviously, preference precedes choice because choices are made to maximize preference. That is certainly the way we conduct our marketing research. We generate factorial designs and write descriptions full of information about products and services. Ou...

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The Purchase Funnel Survives the Consumer Decision Journey

May 19, 2014
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The Purchase Funnel Survives the Consumer Decision Journey

The journey metaphor is almost irresistible. All one needs is a starting point and a finish line, plus some notion of progression. Thus, life is a journey, and so is love. Why not apply the metaphor to your next purchase? McKinsey & Company takes s...

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