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The Wall Street Journal recently ran an article entitled “Are the Yankees Truly the Most-Despised Ballclub?” It states that “Contrary to popular belief, the Yankees are only the fifth-most despised team in the majors, according to an Internet algorithm built by Nielsen Co. that analyzes how people feel about certain things.” Here’s the article: http://online.wsj.com/article/SB10001424052748704471204575210384180269378.html

Interesting topic, but I had this reaction:

  • How transparent is that method?
  • Can you run the analysis yourself using Nielsen’s algorithm?
  • How willing am I to trust results when I can’t see the underlying data myself, can’t evaluate the algorithm, and can’t do the analysis?

With our PreferenceSphere tool, you can drill down from the high-level 2×2 map showing Net Preference and Influence to the underlying Directional Graph, and drill down from there to see every one of the actual sound bites from consumers that generated the graphs. It’s an affordable, self-service approach that’s transparent and lets you examine source data and understand how it’s presented in graphical form. We believe such an approach gives you a great deal of confidence in the tool’s findings.

To illustrate the difference, here’s The Hatred Index from the WSJ article, created by Nielsen. It does make me wonder about how the algorithm generated these numbers and what the underlying source data had to say.

And here are the 2×2 graph and Directional Graph that shows visually how the numbers were calculated.  When we put this graphing capability into our product it will also show the underlying data, or “sound bites” as we call them.  For more on how to interpret these graphs see part 1 and part 2 of my postings on visually representing brand preference.

Whether you’re from Missouri or not, we think you’ll want to run these analyses yourself and have complete access to the method and data for reaching conclusions about brand preference. Let us know if that’s true. We welcome any other feedback on this evolving tool.

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