The Differentiation Horizontal node is designed to take a list of Features, along with an optional list of Variations, and quantify the Horizontal Differentiation between each. The quantified Horizontal Differentiation between all of the Feature Variations is expressed as a Correlation Matrix.
When Features (or Products) cannot be rank ordered in an objective way then they are said to exhibit Horizontal Differentiation. This means that while Customers may, on average, agree that the value of one Feature Variation is the same as the value of another Feature Variation, those Customers may disagree as to which of the two is better. There is Horizontal Differentiation because sentiment about the first Feature Variation is uncorrelated with sentiment about the second Feature Variation. In other words, Horizontal Differentiation is high when Correlation is low.
For example, the Correlation between 'Coca Cola' branded beverages versus 'Pepsi Cola' branded beverages may be 0.0 or even negative (suggesting that Pepsi-drinkers actually hate Coke, and visa-versa). These Products, distinguished primarily by their strong and independent Brands, both enjoy high levels of profitability because of their Horizontal Differentiation.
On the other hand, when Features can be objectively ranked then they are said to exhibit Vertical Differentiation. Horizontal Differentiation is low when Correlation is high.
For example, the Correlation between a '1-year warranty' and a '2-year warranty' will be very close to 1.0 as all Customers universally agree that 2-years is better than 1-year. Hence the success of these Products will not depend upon their negligible Horizontal Differentiation but upon their Vertical Differentiation.
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