Node Description
Differentiation Horizontal
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.
This Community Node documentation assumes you have already downloaded the open-source KNIME analytics platform and installed the free Market Simulation (Community Edition) plugin. If not, start by returning to Getting Started.
Downloads
#1 Ordinal Features
Inputs
Related Features
The collection of related Feature names. These may be ordinal Features related by the fact that they can be ranked. For example, the Feature List may be ‘5-star’, ‘4-star’, ‘3-star’, and ‘2-star’. Or these may be categorical Features that are not ranked but are nevertheless related. For example, the Features ‘Japanese’, ‘Korean’, and ‘German’ will be correlated (Customers generally perceive the two Asian Products as being more similar to each other than to the European Products).
Node
Configuration
What is the relationship between all of the Features in the Input Feature List? Are there very clear and objective differences that all Customers would agree on? Or are the differences between the Features subjective such that Customers would disagree as to the relative value of those Features? For example, ‘storage size’, ‘engine capacity’, and ‘number of megapixels’ are all highly objective Features.
Outputs
Correlation Matrix
The output set of correlations that define the relationship between Features and downstream Customer Distributions.
#2 Categorical Features
Inputs
Unrelated Features
Unrelated Features, having no Correlation, should be generated using several of these Differentiation Horizontal nodes.
Node
Configuration
Quite Subjective Features have a Correlation = 0.40.
Outputs
Correlation Matrix
If the Features are Not Ranked (Categorical) then the Correlation relationship between all Features will be set the same. For example, the way Customers think about the Feature ‘style’ will be no more-or-less Correlated with ‘color’ than with ‘ambience’.
#3 Feature Variations
Inputs
Features
The list of Input Related Features.
Variations
A Variation of a Feature may be associated with a Brand, Product, Channel, Demographic, or Technology. If the Variation is the name of a Brand, then all Products having the same Brand will exhibit the same Variation on the Feature.
Node
Configuration
When there are Feature Variations added to the second input port, the output format of the Features can be set.
Outputs
Feature Variations
The degree of Conformity the Variation has from a Feature norm (range limited to between +1.0 and -1.0). Conformity = 1.0 (default) means that the Variation precisely offers what is expected from the normal Feature.
Repaired Matrix
Repairing is required when the correlations are unrealistic. For example, if A is highly correlated to B (for example, A:B = +0.99) and if A is highly correlated with C (for example, A:C = +0.99) then B must be highly correlated with C (that is, B:C >> 0.0).
Error Matrix
The difference between the Output Correlation Matrix and the Output Correlation Repaired Matrix.
