Market Simulation nodes by Scientific Strategy for KNIME - Community Edition version 4.0.0.v202007040653 by Decision Ready, LLC
The Tune Market node is designed to take raw Customer Distributions for Products found in the Market and quickly tune them to create a Willingness To Pay (WTP) Matrix. A WTP Matrix quantifies the maximum Price that each Customer would pay for each Product in the Market.
Tuning a Market involves adjusting the Differentiation offered by each Product being sold. Stores, Products, and Features all offer their own Differentiating qualities. Differentiation describes the shape of a Customer Distribution and is quantified by the 'Mean', 'SD' (Standard Deviation), and 'Correlation' of the distribution. The 'Mean' quantifies Vertical Differentiation, the 'SD' quantifies Strange Differentiation, and the 'Correlation' quantifies Horizontal Differentiation.
The tuning algorithm systematically alters the 'Mean' and 'SD' (Standard Deviation) tuning parameters of each Customer Distribution until the simulated Market matches actual Market conditions. The initial 'Mean' and 'SD' tuning parameters are set in the Input Product Array or the Input Customer Distribution Matrix. The incoming Product-level Customer Distributions in the 'Input Customer Distribution Matrix' can be Unit Distributions or partially-tuned Willingness To Pay (WTP) Distributions.
The Willingness To Pay (WTP) Matrix output of the Tune Market node can be fed directly into a Profit Engine node in order to optimize the Price of a target Product or generate a Product Demand Curve.
Additional information that can be used to improve the accuracy of tuning is Price Elasticity and Cross Elasticity measurements. Analyzing historical sales data can yield information about the Price Elasticity of individual Products, and about the Cross Elasticity between Products in the Market. Price Elasticity measures the relationship between the changing Price of Product A and the subsequent change in Quantity sold by the same Product A. Cross Elasticity measures the relationship between the changing Price of Product A and the subsequent change in Quantity sold by different Product B. The observed Price Elasticity and Cross Elasticity from the real-world Market can be added to the 'Input Price Elasticity' table to increase the accuracy of the tuned Market Simulation.
Price Elasticity can also include information about the 'Out of Stock' impact a Product can have on other Products. If one Product is Out of Stock then sales of the other Products should increase. The degree by which sales increase reflects the degree of Competitive Rivalry between the Products.
The tuning algorithm operates in two phases. During the first phase, just the Quantity values are taken into account. During the second phase, both the Quantity values and the Price Elasticity measurements are taken into account.
To speed tuning, several Tune Market nodes can be cascaded together with increasing numbers of Virtual Customers. In this way, the calculated tuning parameters become increasingly accurate at each step in the cascade. But to do this, the user should first go into the Market Size Options for all but the last Tune Market node in the cascade and 'Set Output Market Size' to 'Override Product Quantities with Product Array'. This ensures that next node will tune according to actual market conditions.
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