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Price War

Market Simulation nodes by Scientific Strategy for KNIME - Community Edition version 4.3.1.v202102181051 by Decision Ready, LLC

The Price War node is designed to simulate how Competitors might dynamically respond to a changing competitive environment over multiple Rounds of Battle.

At the beginning of each Round of Battle, each Competitor will conduct Private Experiments by continuously changing the Price of a small set of Focus Products until a Maximization Goal is reached. Other Competitors cannot see these Private Experiments. The Maximization Goal can be to maximize Profit, Revenue, or Quantity Sold. While only the Competitor's selected 'Focus Products' will change Price, the Maximization Goal will be evaluated across all of the Competitor's Products. The Private Experiments always assume that the other Competitors in the Market maintain their existing Prices.

For example, a Retailer may wish to maximize Profitability across their entire Store by changing the Price of one or two Focus Products. The Retailer will first secretly conduct a series of Private Experiments by raising and lowering the Price of those selected Focus Products until the total Store Profit is maximized. The Retailer will then push these Maximizing Prices out to the rest of the Market with an Expected Result of increased Profits.

Unfortunately, each of the other Retailers in the Market are all following exactly the same methodology. Each Competitor secretly conducts their own Private Experiments, then all Competitors push out their Maximizing Prices to the rest of the Market at the same time. As a result, there will be a difference between the Expected Results of the Competitors and the Actual Results from the Market.

The Price War continues in this fashion for the number of 'Rounds of Battle' specified by the user. Competitors always following the same methodology and never anticipating that other Competitors might also change Prices.

A dynamic Price Equilibrium is often the outcome of the Price War node. For example, Competitors might drive Price down when the Expected Result is increased Profit, but will then increase Price after discovering the other Competitors have also set lower Prices. This orbiting chaotic decrease - increase - decrease - increase dynamic Price Equilibrium can carry on indefinitely.

But the Price War node can also be used to find a static Price Equilibrium across all Competitors. A static 'Price Equilibrium' is found after many Rounds of Battle when the allowed Price 'Adjustment Percentage' gets increasingly small. At this static equilibrium point, Competitors are generally as happy as can be expected given ever tightening Market dynamics - they neither wish to raise nor lower their Price.

To find a static 'Price Equilibrium' follow these steps:
(a) Set a high 'Starting Adjustment Percentage' of around 16%,
(b) Set a low 'Ending Adjustment Percentage' of around 0.25%,
(c) Set the 'Maximum Number of Tuning Adjustments' to '-1' (this is very important otherwise this static 'Price Equilibrium' methodology won't work),
(d) Set a high number of 'Rounds of Battle' of around 100 to give Competitors a chance to find their best Price points. As each Competitor makes just a single Private Experiment each Round, the algorithm should run reasonably quickly.

The Price War node can find the Price Equilibrium point even when Product Costs are changing dynamically. Dynamic Costs depend both upon how many Customers purchase the Product and which are the Customers who purchase. The average Cost To Make (CTM) a Product might decrease as the Quantity sold increases. Or Customers with a higher Cost To Serve (CTS) might start buying the Product if it starts getting very cheap. The Price Equilibrium point which maximizes Profitability takes into account these dynamically changing Costs.

More Help: Examples and sample workflows can be found at the Scientific Strategy website: www.scientificstrategy.com.

Options

Standard Options

Focus Products
The user-selected set of Products that will change Price (multiple Products can be selected). Additional Products are 'In Scope' if they are also considered by the Competitor when deciding whether an improvement has been made. For example, the user may wish to maximize Profitability across the Store. While only the Focus Products change Price, the impact on the rest of the same-Store Products will also be considered. The Competitors who actively compete in the Price War depend upon the set of Focus Products selected by the user.
Maximization Goal
The user can define the Maximization Goal to maximize Profit, Revenue, or Quantity Sold. If the goal is to maximize total Profit then the Cost of all 'In Scope' Products must be known.
Competitors are Defined by
Competitors can be defined by their Store, Brand, Location, or other Product Attribute found in the Input Product Array. These Competitor definitions are also used to define the set of additional 'In Scope' Products considered by the Competitor when evaluating whether a maximizing improvement has been made. For example, if the user wishes to change Price to maximize Store Profitability, then same-Store Cannibalization will be taken into account. That is, additional Focus Product sales from Customers who would have bought something else from the same-Store do not maximize total Store Profitability unless the Customer is paying a higher margin.
Starting Adjustment Percentage
The Private Experiments conducted by each Competitor work like this. At the beginning of each Round of Battle, each Competitor will start by altering their Focus Product Prices by plus/minus the 'Starting Adjustment Percentage'. The Price of all Focus Products will be altered by the same 'Adjustment Percentage'. After no more improvements are possible, the Competitor will halve the 'Adjustment Percentage' and continue looking for maximization improvements. These Private Experiments will stop after the 'Maximum Number of Tuning Adjustments', or after the 'Adjustment Percentage' gets too low.
Ending Adjustment Percentage
At the beginning of each Round of Battle, the first 'Adjustment Percentage' used in the Private Experiments of the Competitors will move closer (in a linear fashion) to the 'Ending Adjustment Percentage'. For the final Round of Battle, the first 'Adjustment Percentage' will be set to the 'Ending Adjustment Percentage'. However, as Private Experiments continue until Maximizing Prices are found, the 'Ending Adjustment Percentage' makes very little difference to the outcome. Hence, the 'Ending Adjustment Percentage' should generally be set to the same value as the 'Starting Adjustment Percentage'. Price Equilibrium Exception: When the 'Maximum Number of Tuning Adjustments' is set to '-1' then the Private Experiments will end after just a single round of experiments. In this case, the 'Ending Adjustment Percentage' becomes important to finding a static 'Price Equilibrium'.
Maximum Number of Tuning Adjustments
The maximum number of improvements that can be made to the Maximization Goal before the Competitor's Private Experiments will end. If set to '1' then the Private Experiments will continue until exactly one improvement has been found, even if the 'Adjustment Percentage' needs to be reduced. If set to '-1' then the Private Experiments will end after trying to make just a single adjustment based only upon the best result from three experiments: (a) Increase Price, (b) Decrease Price, and (c) No Change. Set the 'Maximum Number of Tuning Adjustments' to '-1' when looking for a static 'Price Equilibrium'.
Rounds of Battle
In each Round of Battle, each Competitor will set Maximizing Prices for their selected Products expecting that no other Competitor will also change Price. Actual results are then calculated when all Competitors try to set Maximizing Prices at the same time. At the next Round of Battle, each Competitor will again attempt to set Maximizing Prices starting from their Prices pushed in the previous round. The Market Simulation stops after the final Round of Battle. Set a high number of 'Rounds of Battle' when finding a static 'Price Equilibrium' across all Competitors.

Advanced Options

Output Table Results Include
The results sent to the Output Tables can be very detailed or can only contain summary results. Selecting 'Output Competitor Expectations' is the most detailed selection and will cause the Output Tables to include the Expected Results of each Competitor secretly calculated during their Private Experiments. Selecting 'Output Individual Battle Results' will only include simulated Actual Results at the end of each Round of Battle when all Competitors attempt to set Maximizing Prices at the same time. Selecting 'Output Final Results Only' is the least detailed selection and will only include the actual results from the final Round of Battle.

Market Size Options

Set Output Market Size
The output Market Size used to scale the Quantity sold, Revenue, and Profitability for each Product in the Market. The options include:
Set to total number of Customers in WTP Matrix (default) the output Market Size is determined by the number of Customers in the Input WTP Matrix.
Set to total number of Customers in Product Array the output Market Size is determined by the total number of Customers listed in the Input Product Array.
Override Product Quantities with Product Array the simulated output Quantity of each Product is overridden with the Quantity found in the Input Product Array.
Fixed number of Customers (including 'No Sale') the output Market Size is set by the user in the 'Set Fixed Number of Customers' option below. The 'Set Fixed Number of Customers' Market Size includes 'No Sale' customers who didn't purchase any of the Products listed in the Input Product Array.
Fixed number of Customers (excluding 'No Sale') the output Market Size is set by the user in the 'Set Fixed Number of Customers' option below. The 'Set Fixed Number of Customers' Market Size does not include 'No Sale' customers - only customers who made an actual purchase.
Set Fixed Number of Customers
The output Market Size when the user selects the 'Fixed number of Customers' option from the list above.
Then Multiply Market Size by Scaling Factor
After the output Market Size has been determined from the list of options above, and the simulation has been run, the final output Market Size is re-scaled according to this scaling factor. Note that everything in the Market will grow / shrink at the same rate, including 'Capacity' and the number of 'No Sale' Products. If the scaling factor is 1.1 then the final output Market Size is increased by 10%. The final Market Size will be rounded to the nearest integer.

Input Ports

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Input Product Array: The set of Products that define the Market. Each row corresponds to a Product that competes for customers in the Market. The 'Input Product Array' should include the following columns:
  1. Product (string - required): The name of the Product corresponding to a column of the same name in the 'WTP Matrix' input port. There can also be an additional row with a Product named 'No Sale' - this row is used to track those Customers who are in the Market but have not yet purchased a Product. If the Product column is not found then the Row Identification (RowID) column will be used in place of the Product column.
  2. Description (optional): The description of the Product or the full name of the Product if the Product field contains an identification number.
  3. Brand (string): The name of the Product Brand. This is required to calculate the total Profit, Revenue, or Quantity Sold across all same-Brand 'In Scope' Products.
  4. Store (string): The name of the Product Store. This is required to calculate the total Profit, Revenue, or Quantity Sold across all same-Store 'In Scope' Products.
  5. Location (string): The name of the Product Location. The additional set of 'In Scope' Products can also be defined by same-Location.
  6. Family (string): The name of the Product Family. Many Product SKUs can be classified as all belonging to the same Family, and can be grouped as 'In Scope' Products.
  7. Category (string): The name of the Product Category. Several Product Families may be part of the same Category.
  8. Platform (string): The name of the Product Platform. The Platform is a general purpose label that can be used in any way. It can, for example, indicate whether the Product is sold both 'Online' as well as 'Offline'.
  9. Price (double - required): The 'Static Price' of each Product in the Market. This 'Static Price' can be adjusted by the personalized 'Dynamic Price' found in the 'Input WTP Matrix'. Personalized Price = Static Price x PAV + PAF. For example, some Customers may be entitled to the percentage Discount found in the 'Price Adjustment Variable' (PAV) column, or some Customers may need to pay the personalized delivery charges found in the 'Price Adjustment Fixed' (PAF) column. The Static Price of the 'No Sale' Product, if included, should be zero. Products with missing Prices are deemed to be Out-of-Stock.
  10. Cost (double): The Product Cost is required for all 'In Scope' Products. Cost estimates are also useful for other Products as the node will calculate the change in Profitability for Competitive Rivals. This can be useful in evaluating how a Competitor might react to a Price change.
  11. Volume (double - optional): The 'Static Volume' of the Product relative to the Volume of other Products in the Market. For example, if a Product were a twin-pack then its Volume would be '2' while the Volume of the original Product would be '1' (default). Other scales could also be used, so that one Product might be 250 (ml) while another 500 (ml). But care with the scale should be taken as the Units are disregarded and the default of '1' will always be used if a Volume is missing. This 'Static Volume' field in the 'Input Product Array' is only important if an accompanying 'Dynamic Volume' field (or _VOL field) is found in the 'Input WTP Matrix'. Otherwise this field is ignored as Virtual Customers do not distinguish Products by the different Volume they require.
  12. Units (string - ignored): The Units of Measurement (UoM) associated with the Product's Volume. For example, the Units could be set to 'ml' when the Products are liquids or beverages. But note that this Units field is ignored and only provided as a convenience. No conversion is provided between different Units. The model will not understand, for instance, that 1 'Litre' contains twice as much Product as 500 'mL'.
  13. Capacity (integer): The Capacity Constraint for the Product. A Product's Capacity may be limited by manufacturing constraints or by inventory levels. If the Capacity level is provided then the Quantity sold for the Product cannot exceed the Capacity limitation. If Capacity is not provided, or Capacity is negative, then the Quantity sold for the Product is not limited. Capacity will be limited relative to the total number of Customers in the 'Market Size' Configuration Dialog.
  14. Quantity (integer): The Quantity sold for each Product in the actual Market. But note that this Quantity value will largely be ignored by the node. Instead, Quantity sold is determined by the Price and Willingness To Pay (WTP) of Products. The Quantity value provided in this field can only be used to set the total output Market Size.
  15. Transactions (integer): A reference number of Transactions for each Product in the actual (real-world) Market. Transactions are only relevant if Customers purchase by Volume. That is, the 'Input WTP Matrix' must contain either a 'Volume' field or at least one '_VOL' field. Otherwise each Customer will purchase only a single Product, and the number of Transactions will equal the Quantity sold.
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Input Willingness To Pay Matrix (double): The Willingness To Pay (WTP) Customer Distribution matrix for each Product column in the Market by each Virtual Customer row. The total number of Virtual Available Customers is equal to the number of rows in the WTP Matrix. In addition to each of the Product's WTP Customer Distributions, this 'Input WTP Matrix' can also contain two types of 'Dynamic Price' and two types of 'Dynamic Cost' Distributions that depend upon the Customers who Purchase the Product. These personalized 'Dynamic Prices' and 'Dynamic Costs' adjust the 'Static Price' and 'Static Cost' found in the 'Input Product Array' to calculate the Product's Margin.
  1. Product01, Product02, etc (double): Each of the Products listed in the 'Input Product Array' should have a corresponding column in this 'Input WTP Matrix'. Each row represents a different Virtual Customer, and each value represents the Customer's Willingness To Pay (WTP) for each Product.
  2. Volume (double - optional): The personalized 'Dynamic Volume' of Product demanded by each Virtual Customer. For example, if the 'Input Product Array' contains a list of beverages of Volume 250ml, 330ml, 500ml, 750ml, and 1000ml then a Virtual Customer with a demanded Volume of '1000' could purchase a Quantity of either 4, 3, 2, 1, or 1 of the Products (respectively). If Customers are buying by Volume then they must purchase in whole number integers. For instance, if a Product had a Volume of 250ml but a Customer demanded 300ml then the Customer would only be able to buy 1 of that Product. Note that the excess Volume of 50ml received by the Customer is deemed to be of no value! The Price, WTP, and Consumer Surplus are all re-scaled by the relative Quantity demanded. When Customers buy in Volume then the Output Transactions field will differ from the Output Quantity field - otherwise these two values ought to be the same. Note that the 'Input Product Array' need not also contain a 'Volume' field. If the Product Volume is missing then the Product is presumed to be sold in Volumes of 1 Unit.
  3. _VOL (double - optional): The per-Product 'Dynamic Volume' (VOL) demanded by each Virtual Customer. This per-Product 'VOL' value will override the general 'Volume'. For example, a Virtual Customer buying laundry detergent might generally demand a 'Volume' of 2 (Litres) but might only demand a '_VOL' of 1 (Litre) for the concentrated detergent Product. If both of the 'Dynamic Volume' values ('Volume' and '_VOL') are missing then a default of '1' will be used.
  4. _PAV (double - optional): The Price Adjustment Variable (PAV) is the percentage adjustment to the Price (typically a Discount) a particular Customer would receive when they Purchase the Product. For example, if the Customer is entitled to a 10% Discount then the 'PAV' would be set to 0.90. The 'Price Adjustment Variable' column is identified by the Product's Name followed by a trailing 'PAV'. The 'PAV' designator can be upper-case or lower-case and may-or-may-not be separated by a space, underscore, or other single character. For example, 'Product_01_PAV' or 'Product 02 PAV' or 'Product03pav'.
  5. _PAF (double - optional): The Price Adjustment Fixed (PAF) is the fixed adjustment to the Product's Price. For example, if Customers pay different amounts for Shipping the Product then this could be modeled using the 'PAF' column. If the WTP Matrix contains both 'PAV' and 'PAF' columns, then the Price is first multiplied by the variable 'PAV' before adding the fixed 'PAF'. The 'Price Adjustment Fixed' column is also identified by the Product's Name followed by a trailing 'PAF' in a manner similar to the 'PAV' designator.
  6. _CTS (double - optional): The Cost To Serve (CTS) is the additional Cost that must be incurred when a Product is sold to a particular Customer. This is a Dynamic Cost as some Customers are cheaper to serve than others, and is only incurred if the Customer actually Purchases the Product. The 'Cost To Serve' column is identified by the Product's Name followed by a trailing 'CTS'. The 'CTS' designator can be upper-case or lower-case and may-or-may-not be separated by a space, underscore, or other single character. For example, 'Product_01_CTS' or 'Product 02 CTS' or 'Product03cts'.
  7. _CTM (double - optional): The Cost To Make (CTM) depends not upon the individual Customer but upon the number of Customers who Purchase the Product. This 'Cost To Make' can be used to simulate the Law of Diminishing Returns. Starting from the first row in the column, each 'Cost To Make' row represents the incremental Cost of manufacturing each additional Product. If the Product is sold ten-times, then the total Dynamic Cost is the sum of the first 10 CTM rows. The 'Cost To Make' column is also identified by the Product's Name followed by a trailing 'CTM' in a manner similar to the 'CTS' designator.

Output Ports

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Output Product Array: The output Product Array corresponds to the Input Product Array but updated to reflect the impact of the 'Change Scenario' outcome. The Product Array will contain these columns:
  1. Battle: An incrementing value for each Round of Battle.
  2. Competitor: The name of the Competitor for the Expected Results from the Competitor's Private Experiments. Or 'Market' for the simulated Actual Results obtained when all Competitors try to set Maximizing Prices at the same time.
  3. Product: The name of the Product corresponding to the Input Product Array.
  4. Brand: The name of the Product Brand or a missing value if the Brand has not been defined.
  5. Store: The name of the Product Store or a missing value if the Store has not been defined.
  6. Location: The name of the Product Location or a missing value if the Location has not been defined.
  7. Family: The name of the Product Family or a missing value if the Product Family has not been defined.
  8. Category: The name of the Product Category or a missing value if the Category has not been defined.
  9. Platform: The name of the Product Platform or a missing value if the Platform has not been defined.
  10. Price: The 'Static Price' of each Product from the 'Input Product Array'. If the Product is the 'Focus Product' then the Price will be updated to reflect the 'Change Scenario'. The 'Dynamic Price' from the 'Input WTP Matrix' is NOT included in this output 'Price' field so as to allow multiple Market Simulation nodes to be chained together. Instead the 'Dynamic Price' is accounted for in the output 'Margin' field.
  11. Cost: The 'Static Cost' of each Product from the 'Input Product Array' or a missing value if the Cost has not been defined. The 'Dynamic Cost' from the 'Input WTP Matrix' is NOT included in this output 'Cost' field so as to allow multiple Market Simulation nodes to be chained together. Instead the 'Dynamic Cost' is accounted for in the output 'Margin' field.
  12. Margin: The average Profit Margin of each Product, where: Profit Margin = Personalized Price - Static Cost - Average Dynamic Cost. The Personalized Price is the 'Static Price' adjusted by the two types of 'Dynamic Price'. The 'Dynamic Cost' depends upon the Customers who Purchase the Product and is averaged across the total Quantity Sold.
  13. Capacity: The Capacity Constraint of each Product or a missing value if the Capacity has not been defined.
  14. Quantity: The simulated number of Customers who selected each Product, including those who selected the 'No Sale' option.
  15. Transactions: The simulated number of Transactions for each Product. By default, each Customer will purchase only a single Product, and the number of Transactions will equal the Quantity sold. Transactions and Quantity will only vary if Customers are purchasing by Volume. That is, the 'Input WTP Matrix' must contain either a 'Volume' field or at least one '_VOL' field so that Virtual Customers demand different Product Quantity.
  16. Share: The simulated Market Share as a percentage (%) of each Product based upon the 'Quantity' column but not including those customers who selected the 'No Sale' option.
  17. Revenue: The Product's Revenue is calculated as the Product's 'Price' and final 'Quantity'.
  18. Profit: The Product's Profitability is only calculated if the Product's 'Cost' was provided in the 'Input Product Array'. Both Static and Dynamic Prices and Costs are included in the Profit.
  19. Include: Whether the Product was included in the 'In Scope' set and was evaluated for maximization in accordance with the user-specified 'Change Scenario'.
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Output Change Results: Lists the degree by which each Product in the Market was changed (if at all) and the impact of the change on each of the other Products in the Market. The 'Change Results' contains these columns:
  1. Battle: An incrementing value for each Round of Battle.
  2. Competitor: The name of the Competitor for the Expected Results from the Competitor's Private Experiments. Or 'Market' for the simulated Actual Results obtained when all Competitors try to set Maximizing Prices at the same time.
  3. Product: The name of the Product that was (or was not) changed as part of the 'Change Scenario'.
  4. Brand: The name of the Product Brand or a missing value if the Brand has not been defined.
  5. Store: The name of the Product Store or a missing value if the Store has not been defined.
  6. Location: The name of the Product Location or a missing value if the Location has not been defined.
  7. Family: The name of the Product Family or a missing value if the Family has not been defined.
  8. Category: The name of the Product Category or a missing value if the Category has not been defined.
  9. Platform: The name of the Product Platform or a missing value if the Platform has not been defined.
  10. Change Scenario: The method by which this Product was changed. For example, the Change Scenario may be 'Maximize Profit for Store' or 'Maximize Revenue for Brand'.
  11. Change Factor: The numeric degree by which the Price of this Product was changed.
  12. Price Change From: The original Price of this Product.
  13. Price Change To: The new Price of this Product after the 'Change Scenario' caused a change.
  14. Price Change: The difference between the original Price and the new Price of the Product.
  15. Price Change Percentage: The percentage difference between the original Price and the new Price of the Product.
  16. Margin Change From: The original average Profit Margin of the Product, where: Profit Margin = Personalized Price - Static Cost - Average Dynamic Cost. The Personalized Price is the 'Static Price' adjusted by the two types of 'Dynamic Price'. The 'Dynamic Cost' depends upon the Customers who Purchase the Product and is averaged across the total Quantity Sold.
  17. Margin Change To: The new average Profit Margin of the Product after its positioning was altered.
  18. Quantity Change From: The original Quantity of the Focus Product.
  19. Quantity Change To: The new Quantity of the Focus Product after its positioning was altered.
  20. Quantity Change: The difference between the original Quantity and the new Quantity of the Focus Product.
  21. Quantity Change Percentage: The percentage difference between the original Quantity and the new Quantity of the Focus Product.
  22. Share Change From: The original Market Share of the Focus Product.
  23. Share Change To: The new Market Share of the Focus Product after its positioning was altered.
  24. Share Change: The difference between the original Market Share and the new Market Share of the Focus Product.
  25. Share Change Percentage: The percentage difference between the original Market Share and the new Market Share of the Focus Product.
  26. Revenue Change From: The original Revenue of the Focus Product.
  27. Revenue Change To: The new Revenue of the Focus Product after its positioning was altered.
  28. Revenue Change: The difference between the original Revenue and the new Revenue of the Focus Product.
  29. Revenue Change Percentage: The percentage difference between the original Revenue and the new Revenue of the Focus Product.
  30. Profit Change From: The original Profit of the Focus Product.
  31. Profit Change To: The new Profit of the Focus Product after its positioning was altered.
  32. Profit Change: The difference between the original Profit and the new Profit of the Focus Product.
  33. Profit Change Percentage: The percentage difference between the original Profit and the new Profit of the Focus Product.
  34. Include: Whether the Product was included in the user-selected set of 'Focus Products'.
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Output Purchased Products List: The list of Products purchased by each Virtual Customer. The user can use the views to hilite the Customers of interest who switched their purchase decision. These Customers who switched as a result of the 'Change Scenario' change can then be further analyzed. The 'Purchased Products List' contains these columns:
  1. Before Purchases: The name of the Product selected by each Virtual Customer row before the Price War.
  2. Before Price: The Price of the purchased Product scaled by the Demand Quantity before.
  3. Before Cost: The Cost to supply the purchased Product scaled by the Demand Quantity before.
  4. Before WTP: The WTP of the purchased Product scaled by the Demand Quantity before.
  5. Before Surplus: The Consumer Surplus of the purchased Product scaled by the Demand Quantity before.
  6. Before Quantity: The Quantity of the Product purchased by each Virtual Customer row before the Price War. This Quantity will be set to '1' unless Customers are purchasing by Volume.
  7. After Purchases: The name of the Product selected by each Virtual Customer row after the Price War.
  8. After Price: The Price of the purchased Product scaled by the Demand Quantity before.
  9. After Cost: The Cost to supply the purchased Product scaled by the Demand Quantity before.
  10. After WTP: The WTP of the purchased Product scaled by the Demand Quantity before.
  11. After Surplus: The Consumer Surplus of the purchased Product scaled by the Demand Quantity before.
  12. After Quantity: The Quantity of the Product purchased by each Virtual Customer row after the Price War. This Quantity will be set to '1' unless Customers are purchasing by Volume.
  13. Switched: Whether the Customer switched the Product they purchased as a result of the Price War.
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Output KPI Indicators: The Output KPI Indicators contain select information about the tuning process and the quality of the final results. The Output KPI Indicators will contain these columns:
  1. Competitor: The Competitor associated with the Key Performance Indicator (KPI). If the KPI refers to the overall results and not to a specific Product then this field will be set to 'Market'.
  2. Indicator: The name of each Key Performance Indicator (KPI), including:
    Iteration Count: the number of Rounds of Battle the Competitor participated in.
    Total Price Experiment Count: the total number of Private Experiments conducted by the Competitor.
    Improving Price Experiment Count: the number of successful Private Experiments that resulted in an expected improvement.
    Average Price Change Percentage: the average Price change of the 'In Scope' Products expressed as a percentage.
    Average Weighted Price Change Percentage: the weighted average Price change of the 'In Scope' Products where Prices are weighed by Revenue contribution expressed as a percentage.
    Quantity Change Percentage: the total percentage change in Quantity of the 'In Scope' Products.
    Revenue Change Percentage: the total percentage change in Revenue of the 'In Scope' Products.
    Profit Change Percentage: the total percentage change in Profit of the 'In Scope' Products.
    Goal Growth Percentage: the growth of the user-defined Pricing Goal (Quantity/Revenue/Profit) as a percentage.
  3. Value: The final value of each Key Performance Indicator (KPI).

Best Friends (Incoming)

Best Friends (Outgoing)

Workflows

Installation

To use this node in KNIME, install Market Simulation nodes by Scientific Strategy for KNIME - Community Edition from the following update site:

KNIME 4.3

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