Workflow Overview

In the last workflow, BB-141 Three Competitors, three Products were put into a Market to compete. Each Product was configured so that its Willingness To Pay (WTP) Customer Distribution was more or less correlated with the other two Products. A Product is said to have Horizontal Differentiation with another Product if the WTP Distributions for the two Products have a low degree of correlation.

But in most cases, it would be difficult to determine the correlation between Products without conducting some sort of Conjoint Analysis study.

In many cases, and with some experience, it is easier to estimate the correlation between Features.

For example, objective Features, like megapixels and gigabytes, are highly correlated as most everybody would agree the more the better.

On the other hand, subjective Features, like brands and styling, are usually uncorrelated. Some Customers are fiercely loyal to a certain Brand, while other Customers are passionate about a competitive Brand.

In a Market Simulation designed to model a real-world Market, the user would start with best-guess Feature correlations and then set up the Market Simulation in a tuning loop.

This Building Blocks workflow examines Feature-level correlation. The workflow also introduces a new node, called the ‘Correlation Concatenation’ node, which allows groups of Features to be concatenated together.

This Building Blocks example 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

KNIME Workflow
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Competitive Story

In the last workflow, BB-141 Three Competitors, George Jetson failed to successfully launch his ‘Jetson Gears’ as the Product was not sufficiently differentiated from the incumbent Products.

But George still wants to borrow from his extensive industry experience to develop the best Product he can for the Market.

So instead, George breaks his Product down into a set of Features. He borrows from his Competitors to make his Product better, but he also works to ensure his Product is distinct.

Product Features

Each Product in the Market comprises of three Features.

Feature Detail

The three Features that each Product has are:

  1. Functionality
  2. Performance
  3. Style

Feature Detail

Not that for the first time in the Building Block workflows, the Vertical Differentiation or importance of each Feature to Customers (the Mean and Standard Deviation) is specified directly in the Product Features table instead of in the ‘Matrix Distributions’ node. Functionality and Performance is more important (has a higher Mean) but provides less distinction than Style.

Functionality

Spacely Sprockets, Cogswell Cogs, and Jetson Gears are built on objective technologies that make them more or less similar.

Correlation

The Functionality of Jetson Gears is somewhat similar to Spacely Sprockets but not to Cogswell Cogs.

Performance

The same is true for the Performance Feature of Jetson Gears.

Style

And the same is true again for the Style Feature of Jetson Gears.

Matrix

As before, the ‘Correlation Pairs to Matrix’ node converts the list into a Correlation Matrix.

Correlation

The Correlation Matrix for Functionality corresponds to the input list of correlations.

Performance

Style

Concatenation

The ‘Correlation Concatenation’ node is needed to join together the Functionality, Performance, and Style Features.

Correlation

Functionality (input Matrix A) and Performance (input Matrix B) are correlated. But neither are correlated with Style (input Matrix C).

Matrix

The ‘Correlation Concatenation’ node collects together the correlations of all Product-Features with all other Product-Features.

Matrix

The ‘Matrix Distributions’ node generates Willingness To Pay (WTP) Customer Distributions for the Features in the Market.

Configuration

In this case, the Default Mean and Default SD (Standard Deviation) are ignored because these values have already been specified in the Input Attribute List.

Feature Value

The ‘Matrix Distributions’ node generates the Customer perceived values of each Feature.

Products

The ‘Product Generator’ node aggregates together all of the Feature Customer Distributions based upon which Products have which Features.

Product Array

The final list of Products.

WTP Matrix

The final set of WTP Customer Distributions for the Products.

Statistics

Statistics can be generated to help evaluate the accuracy of the Market Simulation.

Mean & SD

The Mean and Standard Deviation (SD) of the Product Customer Distributions.

Correlation Matrix

The Product Correlation Matrix looks similar to the correlations from the previous workflow: BB-141 Three Competitors.

Profit Engine

The ‘Profit Engine’ node predicts which Customers will buy which Product from which Competitor.

Configuration

The Profit Engine is set up to calculate the Demand Curve for Jetson Gears.

Demand Curve

This Demand Curve for Jetson Gears is fatter, flatter, and more forgiving than the last Demand Curve.

Results

Market Share

The Market Share of all three Competitors can be shown using a Pie Chart.

Color

The upstream ‘Color Manager’ is used to set the Color of each Product.

Quantity

The size of each pie-slice is determined by the Quantity of each Product sold.

Improvement

The Market Share for the Jetson Gears Product in this workflow is an improvement, with the Product picking up 33% of Customers.

Jetson Gears steals more Market Share from Spacely Sprockets than Cogswell Cogs, and pushes Spacely Sprockets into last place. This is understandable as Jetson Gears are more closely correlated with Spacely Sprockets and hence more appealing to the same set of Customers.