Node Description
Product Generator
The Product Generator node iterates through the Input Product Features table and aggregates together all of the matching Customer Distributions found in the Input Customer Distributions table.
For example, the Input Product Features table may associate Product01 with Feature01, Feature02, and Feature03. The Customer Distributions for these three Features would all be found in the Input Customer Distributions table. The Output Willingness To Pay Matrix (Output WTP Matrix) would then create a new Customer Distribution called “Product01” which would be the sum of Feature01 + Feature02 + Feature03.
Each Customer Distribution comprises a set of part-worth values for a set of Customers. The Feature part-worth values for each Customer can be added together to calculate the Customer’s overall Willingness To Pay (WTP) for the Product. Note that the part-worth values for all of the Features that make up a Product must be included, and the Features must describe orthogonal aspects of the Product (meaning that the Feature part-worth values cannot be double-counted).
In many Markets, the same Product might be sold in several Locations and by several competitive Stores. For a Market Simulation to distinguish between these different Product-Location and Product-Store combinations, the Product name given to each combination must be unique. For example, the same Product sold in Location01 and Location02 might be designated “Product.Location01” and “Product.Location02”. Similarly, the same Product sold by competitors at Store01 and Store02 might be designated “[Store01].[Product]” and [Store02].[Product]”.
But these “same” Products are not really the same at all. Each offers additional differentiation that make the Product Variations more or less appealing to a Customer. For example, Customers usually prefer Products sold from Locations that are convenient to them. In this case, a ‘Geographic Feature’ node can be used to calculate the ‘Lost Value’ Customers would suffer for travel, shipping costs, and waiting time. Similarly, competitors can also offer their own differentiation to otherwise identical Products. For example, Customers may perceive a beverage sold by a 5-star hotel to be differentiated from an otherwise identical beverage sold by a convenience store. The 5-star hotel version of the Product may have an additional Feature called “Premium Outlet”.
Note that the Product Generator node adds tiny random value to each of the Customer Willingness To Pay (WTP) values in the Output WTP Matrix. These tiny random values are imperceptible, but serve as a tie-breaker in the event that a downstream Market Simulation node is deciding how a Customer would make a Purchase decision when evaluating identical Products. Without this tiny random tie-breaker, the downstream Market Simulation node would always select the first of the identical Products.
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 Product Distributions
Inputs
Product Features
The Input Product Features table contains unique Product names and a list of the Features associated with each. Additional columns, including Price, Cost, and Quantity, contain additional (optional) information about the assembled Products.
Customer Distributions
The Input Customer Distributions contain the set of part-worth values for each Feature and each Virtual Customer in the Market. These Feature-Distributions are added together to generate Product-Distributions.
Node
Configuration
No configuration is required.
Outputs
Product Array
The Output Product Array contains all of the unique Products found in the Input Product Features table, along with the additional optional columns: Price, Cost, and Quantity.
WTP Matrix
The Output Willingness To Pay (WTP) Matrix is made up of the aggregated Feature Customer Distributions found within each Product.
#2 Feature Detail
Inputs
Product Details
Additional details about each Feature and each Product can be found in both the Input Product Features table (top-input) as well as the Input Feature List (middle-input). In this example, the optional ‘Quantity’ column provides details about the amount of Quantity sold of each Product.
Feature Details
The optional Input Feature List (middle input) contains additional details about each of the Features that make up the Products. Feature details can include Price, Cost, and Quantity. These Feature details are aggregated together to describe the final Products. For example, including the ‘Sprocket’ Feature will add $900 to the Cost of providing that Product.
The Mean and Standard Deviation (SD) values in this table are ignored by the ‘Product Generator’ node. These values are only used by the ‘Matrix Distributions’ node.
Customer Distributions
The Input Customer Distributions contain the set of part-worth values for each Feature and each Virtual Customer in the Market.
Node
Configuration
No configuration is required.
Note that the middle-input port is white to indicate that the input data is optional. The top-input and bottom-input are both black to indicate that the input data is required.
Outputs
Product Array
The Output Product Array contains the aggregated Product details for Price, Cost, and Quantity from the top-input and middle-input.
WTP Matrix
The Output Willingness To Pay (WTP) Matrix is made up of the aggregated Feature Customer Distributions found within each Product.
