The Differentiation Vertical node is designed to take a list of Features, along with an optional list of Variations, and quantify the Vertical Differentiation between each. The quantified Vertical Differentiation between all of the Feature Variations is expressed as a Mean, Standard Deviation (SD), and optional supplier Cost. The Mean and Standard Deviation (SD) are then combined with the Horizontal Differentiation Correlation Matrix in a downstream node to generate a set of part-worth Customer Distributions for each of the Feature Variations.

When Features (or Products) can be rank ordered in an objective way then they are said to exhibit Vertical Differentiation. Features often simultaneously exhibit both Vertical Differentiation and Horizontal Differentiation. But when all Customers universally agree that one Feature is better than another then Vertical Differentiation dominates. In that case, Customers can still disagree as to how much better the one Feature is from the other. This disagreement is reflected in a Customer Distribution comprising a range of part-worth values. When the Customer Distribution is also a Normal Distribution, then the range can be precisely described using a Mean and Standard Deviation (SD).

For example, there is only Vertical Differentiation between a ‘1-year warranty’ and a ‘2-year warranty’ because all Customers universally agree that 2-years is better than 1-year. But that is not to say that all Customers will make the same purchase decision when deciding between the two. Nor is it to say that Price is the only decision factor: Mean and Standard Deviation (SD) of the Customer Distribution, along with Price are all important.

Consider a Market in which Customers generally believe that if a Product fails then it will fail quickly. The Mean of the ‘2-year warranty’ will still be greater than the Mean of the ‘1-year warranty’, but the Standard Deviation (SD) for the ‘2-year warranty’ will shrink. Hence there is a bias to select the ‘1-year warranty’ even among Customers who place a high value on warranties.

Strictly speaking, supply Cost is not part of Vertical Differentiation. Nor, in fact, is Product Price. But the calculation of Feature supply Cost has been included here as a convenience as there is often a relationship between the value of a Feature and the Cost to supply that Feature (the difference between Value and Cost is called ‘Value Created’). But there are many ways to calculate supply Cost, and calculating Feature Cost is but one way. Costs can be calculated at a Store-level, a Product-level, a Feature-level, or a Customer-level. Costs are only important to the Market Simulation when maximizing Profitability – hence Costs need not be calculated at all.

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.

Feature List

The Output Feature List from the node is the output set of objective metrics for each Feature Variation. These Vertical Differentiation objective metrics are combined with the Horizontal Differentiation Correlation Matrix in downstream nodes to generate a set of part-worth Customer Distributions and, ultimately, a Willingness To Pay (WTP) Matrix.

Feature List

The Output Feature List uses the Mean and SD from the Input Feature List.

Feature List

Note that the ‘Output Related Features’ list is not restricted to Features. These output values can also be used as a Customer Distribution with thousands of rows of data.

Feature Variations

Quality changes the Mean. Niche changes the Standard Deviation (SD). And Expense changes the Cost. A value of 0.0 indicates the Variation does not change the Feature.

Output Distribution

The ‘Differentiation Vertical’ node has directly generated a Customer Distribution that spikes at 0.0 and 100.0.