# Workflow Overview

The first two ‘Building Blocks’ workflows (BB-101 Simple Monopoly and BB-102 Monopoly Distributions) focused on Monopoly Markets where there was only one Competitor and one Product.

This workflow introduces three other simple types of Markets, and compares:

- Monopoly Markets,
- Commodity Markets,
- Orthogonal Markets, and
- Semi-Orthogonal Markets.

A Commodity Market is where several Competitors all sell identical Products.

An Orthogonal Market is where the Products are in the same Category, and are competing for the same Customers, but the Products are otherwise unrelated. Orthogonal means “statistically independent“.

The Semi-Orthogonal Market contains Products that share some similar characteristics, but also have some unique differentiation. These types of Markets are the most common.

*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

# Monopoly Market

#### Generate WTP

#### Configuration

The WTP Matrix is a Normal Distribution with a Mean = 100 and Standard Deviation (SD) = 50.

#### WTP Statistics

#### Statistics Table

Distribution Statistics include Min, Max, Mean, SD, Variance, Skewness, Kurtosis, Overall Sum, # Missing, Median, Count, and an automatically generated Histogram.

#### Constant Value

#### Monopoly Market

The Output Product Array has been filtered to just show the ‘No Sale’ Quantity (the number of Customers who do not buy the Monopoly Product). There are 8,398 ‘No Sale’ Customers in this Monopoly Market.

# Commodity Market

More precisely, the Market Simulation defines *two* Products with each Product made up of exactly the same *Feature*.

#### Competitors

#### Products vs Features

There are actually two ‘Products’ in this Market, but each Product is made up of the identical ‘Feature’ called “Sprockets”. Price and Cost is the same.

#### Features

#### Definition

Only the Feature Name is required to be defined. But the Customer Distribution Type, and Input Parameters could also have been defined here.

#### Feature WTP

#### Product Generator

#### Configuration

The ‘Product Generator’ node does not need to be configured. It knows what to do based upon the input tables. Note that the middle table (with the white triangle) is optional. Black triangles indicate required input tables.

#### Product Array

The two Competitive Products (Spacely Sprockets and Cogswell Cogs) are now completely defined.

#### Profit Engine

#### Results

The ‘Profit Engine’ node predicts that 814 Customers buy Spacely Sprockets, while 788 Customers buy Cogswell Cogs. As these two Products are undifferentiated, we would expect a 50:50 split. But some randomization is used to settle tie-breaks.

#### Demand Curve

If Spacely Sprockets were to lower Price, then they would win all Customers. But if they were to raise Price, then Cogswell Cogs would sell the cheaper Commodity Product, and Spacely Sprockets’ Market Share would fall to 0%.

#### Commodity Market

#### Results

The Quantity of Customers who do not purchase one of the two Commodity Products is 8,398. This is exactly the same as the number of ‘No Sale’ Customers from the Monopoly Market. This is the expected result as the two Products offer no differentiation and Prices remain the same.

# Orthogonal Market

The value Customers place on Products is called their Willingness To Pay (WTP). When their WTP for one Product is uncorrelated with their WTP for another Product, the two Products are said to be “orthogonal”.

#### Products

#### WTP Matrix

The Willingness To Pay (WTP) Customers have for Spacely Sprockets is uncorrelated with the WTP they have for Cogswell Cogs.

#### Correlation

#### Configuration

The Linear Correlation node is configured to calculate the Correlation between the two Customer Distributions which define the Products.

#### Results

As expected, there is virtually no correlation (0.005) between the WTP Customers have for Spacely Sprockets and the WTP Customers have for Cogswell Cogs.

#### Pie Chart

#### Configuration

Configure the Pie Chart to build Pie slices for each Product by aggregating the Quantity sold.

# Semi-Orthogonal Market

*commodity*Features (Features that many other Products also have) and

*differentiating*Features (Features that make the Products unique and special).

This Semi-Orthogonal Market again comprises of two Competitors. The two Products in the Market share a “Commodity Feature” but are each differentiated by their own “Orthogonal Feature”.

#### Competitors

#### Features

The “Commodity Feature” and the two “Orthogonal Features” can have their own Feature-Level Cost and Price. This Cost and Price will contribute to the overall Cost and Price of the Products.

#### Products

#### Feature WTP

The ‘Product Generator’ node aggregates the WTP Customers have for each of the Features …

#### Simulation

*less differentiated*than the orthogonal Products. This means less variety, and fewer buying Customers.

#### Market Share

More Customers buy a Product than in the Monopoly Market and the Commodity Market. But fewer Customers buy a Product than in the pure Orthogonal Market.

#### Demand Curve

The Demand Curve is no longer a “winner take all” as it was with the Commodity Market. Competitors can now Price above Marginal Cost and be profitable.

# Final Results

#### Concatenate

#### No Sale

The fewest number of Customers bought a Product in the Monopoly and Commodity Markets. The most number of Customers bought a Product in the Orthogonal Market.

#### Bar Chart

The number of Customers who purchased a Product in the Semi-Orthogonal Market (yellow) is between the Orthogonal (green) and Commodity Markets (red / blue).