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Tune Scenarios

Market Simulation nodes by Scientific Strategy for KNIME - Community Edition version 4.0.0.v202007040653 by Decision Ready, LLC

The 'Tune Scenarios' node generates a single tuned 'Output WTP Matrix' based upon observations from a number of Market Conditions.

The 'Input Market Array' contains the Competitive Scenarios of different Market Conditions, along with the Products, Prices, and Quantities sold within each Scenario. The different Market Conditions might include historic observations from different sales seasons. Or the different Market Conditions might include Product Sensitivity data observed when Prices of the different Products are altered. Market Conditions vary when there are different competitive Products in the Market selling at Prices that change over time. As a result, the Quantity sold of each Product will typically be different under each Competitive Scenario.

The number of Products in each Competitive Scenario can vary. Products that are not included in one or more Market Conditions: (i) may not yet have entered, (ii) may have exited, or (iii) may be Out-of-Stock.

This node assumes the level of Customer Demand is constant, with only the nature of the Competition in the Market changing. When Competitors change their Prices or Product Assortment, Customers will make different Purchase decisions even when their underlying preferences remain constant.

As a result, this 'Tune Scenarios' node is not designed to interpret seasonality. Only one Output Willingness To Pay (WTP) Matrix is generated, and this must represent the unchanging Willingness to Pay of Customers across all Market Conditions. To simulate seasonality, start with the Output WTP Matrix from this node, then scale the Mean (and SD) of the WTP Matrix with the 'Scale Distributions' node.

This node is designed to find three values:

  • Market Mean: The reference Mean of the WTP Customer Distributions across all Products in the Market.
  • Market SD: The reference Standard Deviation (SD) of the WTP Customer Distributions across all Products in the Market.
  • Market Correlation: The reference Correlation between all Customer Distributions within the Output WTP Matrix.

But individual Product Mean, SD, and Correlation values can be varied by the user according to the 'Input Product Attributes' table and the 'Conformity Boosters' in the Configuration Dialog. See below for more details.

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

Options

Standard Options

Number of Virtual Customers
The number of Virtual Customers to be generated by the 'Tune Scenarios' node. Each Virtual Customer will be represented by a separate row in the 'Output WTP Matrix'.
Starting Market Mean
The Mean of the WTP Customer Distributions of all Products is first set to this Market Mean, and then modified on a Product-by-Product basis according to the Product 'Quality' variation in the 'Input Product Attributes' table. If this 'Starting Market Mean' is set to 0.0 zero then it cannot be adjusted by the 'Adjustment Percentage'. In this case, the 'Quality' variation in the 'Input Product Attributes' table will set the Product Mean to the fixed 'Quality' value.
Starting Market SD
The Standard Deviation (SD) of the WTP Customer Distributions of all Products is first set to this Market SD, and then modified on a Product-by-Product basis according to the Product 'Niche' variation in the 'Input Product Attributes' table. If the 'Starting Market SD' is set to 0.0 then all 'Product Niche' variations must be set to a positive value.
Starting Market Correlation
The Correlation between each of the WTP Customer Distributions for each of the Products is first set to this Market Correlation, and then modified on a Product-by-Product basis according to the Product 'Conformity' variation in the 'Input Product Attributes' table, and the 'Conformity Booster' values found in the Configuration Dialog.
Starting Adjustment Percentage
This 'Tune Scenarios' node will start by altering the Market Mean/SD/Correlation by plus/minus the 'Starting Adjustment Percentage'. After no more tuning improvements are possible, the 'Tune Scenarios' node will halve the 'Adjustment Percentage' and continue looking for tuning improvements. The algorithm will stop after the 'Maximum Number of Tuning Adjustments', or after the 'Adjustment Percentage' gets too low.
Maximum Number of Tuning Adjustments
The maximum number of improvements that can be made to the tuned Market before the Tuning Algorithm in the 'Tune Scenarios' node will end. Only actual improvements are counted towards the total.
Output Table Results Include
The results sent to the 'Output Market Array' can be very detailed or can only contain summary results. Selecting the 'Input Market Array' option will generate the more detailed results by causing the 'Output Market Array' to extend the original 'Input Market Array' table by appending the Quantity Error, Mean, and SD results to each Market Scenario. Selecting the 'Input Product Attributes' will generate a less detailed summary 'Output Product Array' which includes only simulation results from the Prices and Products found in the 'Input Product Attributes' table. This 'Output Product Array' can be directly used with the 'Output WTP Matrix' by downstream Market Simulation nodes.

Advanced Options

Brand Conformity Booster
Boosts the Correlation between Products having the same Brand. Boosting is often required as same-Brand Products are perceived by Consumers as being similar, so the Correlation between same-Brand Products needs to be increased. Conformity Boost = 0.0 means that the Product Correlation will be unchanged and equal the tuned Market Correlation. Conformity Boost = 1.0 means that the tuned Market Correlation will be ignored and same-Brand Products will all be 100% correlated with one-another. Conformity Boost = 0.20 is typical, and will boost the Correlation between same-Brand Products by 20%. Hence if the final tuned Market Correlation = 0.5 then same-Brand Products will have a mutual Correlation of 0.6.
Store Conformity Booster
Boosts the Correlation between Products sold by the same Store. Conformity Boost = 0.20 will boost the Correlation between same-Store Products by 20%.
Location Conformity Booster
Boosts the Correlation between Products sold at the same Location. Conformity Boost = 0.20 will boost the Correlation between same-Location Products by 20%.
Family Conformity Booster
Boosts the Correlation between Products from the same Family. Conformity Boost = 0.20 will boost the Correlation between same-Family Products by 20%.
Category Conformity Booster
Boosts the Correlation between Products in the same Category. Conformity Boost = 0.20 will boost the Correlation between same-Category Products by 20%.
Platform Conformity Booster
Boosts the Correlation between Products on the same Platform. Conformity Boost = 0.20 will boost the Correlation between same-Platform Products by 20%.
Save Randomizing Seed
A Randomizing Seed can be saved to ensure that the random WTP Customer Distributions generated by this node are always generated in the same way. If a 'Tune Scenarios' node is copied within a workflow then the user should ensure the Saved Randomizing Seed is changed or not saved - otherwise Customer Distributions that ought to be uncorrelated may be incorrectly generated. The 'New' button will generate a new Randomized Seed. Disable the CheckBox to generate a new Randomizing Seed each time the node is run.

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 Virtual Customers in WTP Matrix (default) the output Market Size is determined by the number of Virtual 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 the final output Market Size is scaled according to this scaling factor. 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 Market Array: A Scenario of Market Conditions representing historic data from different seasons, or representing different degrees of Product Sensitivity at different Prices. Each Market Scenario contains a set of Products competing in the Market under the different conditions. The 'Input Market Array' should include the following columns (other input columns will be ignored):
  1. Market (string): The name of each Market Scenario found in the Input Market Array. A Market Scenario might contain details of the Price and Quantity of all Products sold during a certain time period. Or a Market Scenario might contain Price Sensitivity results, showing the degree of Product substitutability given different Prices of each.
  2. Product (string): The name of each Product found within the given Market Scenario. Not all Products need to be in each Market Scenario. If a Product from the 'Input Product Attributes' is not found within a Market Scenario then it is assumed that the Product: (i) has not yet entered the Market, (ii) has exited the Market, or (iii) is Out of Stock. The Product 'No Sale' indicates the relative number of Products that were not sold within the Market under the Competitive Scenario.
  3. Price (double): The Price of the Product within the Market during the Competitive Scenario. If the Price is missing from this Input Market Array then the default Price from the 'Input Product Attributes' table will be used for the Product instead.
  4. Capacity (double): (optional) The total Capacity (or maximum Quantity) of the Product that can be manufactured and sold within the Market or during the Competitive Scenario. The Product's Capacity level can be changed from time-to-time and Market-to-Market as the manufacturing Capacity of the seller increases or decreases. If Capacity is not set within this 'Input Market Array' then the default Capacity from the 'Input Product Attributes' table is used. If no default value is set then Product sales are not limited by Capacity for the given Market Scenario.
  5. Quantity (double): The Quantity sold for the Product during the given Market Scenario time period. The Total Available Market (TAM) for each Market Scenario is calculated according to the relative ratio value of each Product Quantity, as well as the 'No Sale' value for the Market, and the 'Market' Product name which indicates the overall size of the TAM when individual Product Quantity values are not known. The Tuning Algorithm attempts to minimize the cumulative Quantity Error for each Product within each Scenario's Market Conditions. If the individual Quantity for a Product is missing and unknown, then the Quantity Error for the Product does not contribute to the Tuning Algorithm. Input Quantity values are treated as ratios making up the total 'Number of Virtual Customers' set by the user in the Configuration Dialog. The 'No Sale' values (if added) should not reflect the seasonality, but the proportion of Customers from the (perhaps smaller) Total Available Market (TAM) who would have purchased if Prices were more reasonable.
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Input Product Attributes: Additional detail concerning each Product found within the 'Input Market Array'. In most cases, this additional detail remains constant throughout all Market Scenarios, but in some cases this additional detail represents the default value used whenever the 'Input Market Array' contains missing values. The 'Input Product Attributes' can include the following columns:
  1. Product (string): The unique name of each Product found in the Input Market Array.
  2. Description (string): (optional) A long identifier or brief Description of the Product.
  3. Brand (string): (optional) The Brand of the Product. Products having the same Brand can be configured to have greater mutual correlation within the Output WTP Matrix by using the 'Brand Conformity Booster' setting in the Configuration Dialog.
  4. Store (string): (optional) The Store seller of the Product. Products sold by the same Store seller can be configured to have greater mutual correlation within the Output WTP Matrix by using the 'Store Conformity Booster' setting in the Configuration Dialog.
  5. Location (string): (optional) The Location of the Product. Products sold at the same Location can be configured to have greater mutual correlation within the Output WTP Matrix by using the 'Location Conformity Booster' setting in the Configuration Dialog.
  6. Family (string): (optional) The Sub-Category or Family name of the Product.
  7. Category (string): (optional) The name of the Category that the Product falls within.
  8. Platform (string): (optional) A general purpose Platform attribute that can be assigned to each Product.
  9. Price (double): (optional) The default Price of the Product. If the Product Price is missing from the 'Input Market Array' then this default Price will be used for the Product. Each Product in each Market Scenario must have a Price.
  10. Cost (double): (optional) The Cost of the Product used to calculate Profitability and Cost of Goods Sold (COGS). It is assumed that the Product Cost doesn't change, so any Costs found within the 'Input Market Array' will be ignored.
  11. Capacity (double): (optional) The default maximum Quantity of the Product that can be manufactured and sold. A changing Capacity level can be set within the 'Input Market Array'. If Capacity is not set within the 'Input Market Array' then this default will be used. If no default value is set then Product sales are not limited by Capacity.
  12. Quantity (double): (optional) A reference Quantity sold for the Product. This Quantity is only used to set the size of the Total Available Market (TAM) when the user wishes to output the 'Input Product Attributes' table as an 'Output Product Array'. This Quantity is NOT used within the Tuning Algorithm and usually does not need to be provided.
  13. Quality (double): (optional) The relative Quality the Product (between -2.0 and +2.0) has from the Market Mean. The Quality variation modifies the 'Mean' of the Product. Quality = 0.0 (default) means that the Product offers what is expected from the tuned Market (no change to the reference Market Mean). Quality = +1.0 means that the Product is a vast improvement from the norm (200% x Market Mean). Quality = -1.00 means that the Product is vastly inferior to the norm (50% x Market Mean). Quality = +/- 0.05 is typical, and would be used to generate Products that all offer small variations around what is accepted as a Product in the Market. If the 'Starting Market Mean' is set to 0.0 then the 'Product Mean' will be set to this fixed Quality value. Note that objectively inferior Products having poor relative Vertical Differentiation (that is, having a Quality variation < 0.0) may still attract Customers either because they offer Horizontal Differentiation (that is, their Customer Distribution is uncorrelated with other Products), or because they reach a Customer Niche (that is, they have a wider Standard Deviation (SD) with a Niche variation > 0.0).
  14. Niche (double): (optional) Whether the Product is relatively more appealing to a Customer Niche or to the mass market. The Niche variation (between -2.0 and +2.0) modifies the Standard Deviation (SD) of the Product. Niche = 0.0 (default) means that the Product offers what is expected from the tuned Market (no change to the reference Market SD). Niche = +1.0 means that the Product's Customer Distribution has a wider variance than the Market norm (200% x SD). Niche = -1.00 means that the Product has a tighter variance than the Market norm (50% x SD). Niche = +/- 0.10 is typical. If the 'Starting Market SD' is set to 0.0 then the 'Product SD' will be set to this fixed Niche value (Niche must be greater than 0.0). Niche variations can be used to simulate objectively inferior Products that nevertheless have high Prices and significant Market Share. For example, the second-generation Apple Mac computers were technically inferior to Intel-Windows computers, and yet 10% of Customers paid 50% more for an Apple Mac. To simulate this kind of phenomenon, set Quality Variation = -0.05 (95% x Mean) and Niche Variation > +0.10 (110% x SD). If the 'Starting Market SD' is set to 0.0 then all 'Product Niche' variations must be set to a positive value.
  15. Conformity (double): (optional) The degree of Conformity (between +1.0 and 0.0) the Product has from the Market norm, and the relative difference in Correlation the Product has with respect other Products. Conformity = 1.0 means that the Product precisely offers what is expected from the tuned Market (no change to the reference Market Correlation). Conformity = 0.0 means that the Product is vastly different from the Market norm. Conformity = 0.95 is typical, and would be used for Products that all offer small variations around what is accepted from the Market norm. For example, 'Sony', 'Samsung', and 'Canon' may all have a Conformity = 0.95 (they all offer typical Products with only a little distinction), whereas 'Apple' may have a Conformity = 0.20 because Apple's Variation is highly distinctive. Note that these values do not describe whether 'Sony' is better or worse than 'Apple'. Horizontal Differentiation describes only whether Customers view the Product as similar or different.

Output Ports

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Output Market Array: The 'Output Market Array' corresponds to either the 'Input Market Array' or the 'Input Product Attributes' but is updated to reflect the results from the Market Simulation. A simplified 'Output Product Array' can be generated by selecting 'Output Table Results Include: Input Product Attributes' in the Configuration Dialog. The 'Output Market Array' can contain these columns:
  1. Market: The name of each Market Scenario from the Input Market Array.
  2. Product: The name of the Product corresponding to the Input Market Array.
  3. Brand: The name of the Product Brand or a missing value if the Brand has not been defined.
  4. Store: The name of the Product Store or a missing value if the Store has not been defined.
  5. Location: The name of the Product Location or a missing value if the Location has not been defined.
  6. Family: The name of the Product Family or a missing value if the Product Family has not been defined.
  7. Category: The name of the Product Category or a missing value if the Category has not been defined.
  8. Platform: The name of the Product Platform or a missing value if the Platform has not been defined.
  9. Price: The Price of each Product.
  10. Cost: The Cost of each Product or a missing value if the Cost has not been defined.
  11. Capacity: The Capacity Constraint of each Product or a missing value if the Capacity has not been defined.
  12. Quantity: The simulated number of Customers who selected each Product, including those who selected the 'No Sale' option.
  13. Error: The absolute value of the difference between the actual input 'Quantity' and the simulated output 'Quantity'. The Error can be weighed and summed by downstream nodes to determine the 'Total Error' has part of the Tuning Loop to tune the Market Model. Note that the Quantity Error for the 'No Sale' Product is not calculated unless the user explicitly includes the 'No Sale' Product in the Input Product Array and sets a target Quantity to be greater than zero.
  14. 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.
  15. COGS: The Cost of Goods Sold (COGS) for the Product is calculated as the Product's 'Cost' and final 'Quantity'. COGS is only calculated if the Product's 'Cost' was provided in the 'Input Product Array'. Both the Static Costs and Dynamic Costs are included in the COGS.
  16. Revenue: The Product's Revenue is calculated as the Product's 'Price' and final 'Quantity'.
  17. Profit: The Product's Profitability is only calculated if the Product's 'Cost' was provided in the 'Input Product Attributes'.
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Output Willingness To Pay Matrix: The 'Output WTP Matrix' made up of the Customer Distributions for each Product column in the Market by each Virtual Customer row. The number of rows in the WTP Matrix is equal to the total 'Number of Virtual Available Customers' set by the user in the Configuration Dialog. The Mean of each column will be set to the tuned 'Market Mean' and adjusted according to the Product 'Quality' variation in the 'Input Product Attributes' table. The Standard Deviation (SD) of each column will be set to the tuned 'Market SD' and adjusted according to the Product 'Niche' variation in the 'Input Product Attributes' table. The mutual Correlation of each column will be set to the tuned 'Market Correlation' and adjusted according to both the Product 'Conformity' variation in the 'Input Product Attributes' table, as well as the 'Conformity Booster' settings for same-Brand, same-Store, etc. Products in the Configuration Dialog.
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Output Correlation Matrix: The output set of correlations that define the relationship between each Product's Customer Distribution column within the 'Output WTP Matrix'. The 'Output Correlation Matrix' will be symmetrical such that the number of data rows match the number of columns. The Product correlation values depends upon: (a) the tuned 'Market Correlation', (b) the Product 'Conformity' variations in the 'Input Product Attributes' table, and (c) the 'Conformity Booster' settings for same-Brand, same-Store, etc. Products in the Configuration Dialog. The 'Output Correlation Matrix' will contain these columns:
  1. Distribution: Each unique Product found in the 'Input Market Array' with a corresponding Customer Distribution in the 'Output WTP Matrix'.
  2. Correlated Distributions: Each WTP Customer Distribution name for each Product, along with the degree of correlation to the other Products. Output correlations will be symmetrical and range-limited to -1.0 and +1.0.
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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. Market: The name of each Market Scenario from the 'Input Market Array'. If the KPI refers to the overall results and not to a specific Market then this field will be left blank.
  2. Product: The Product 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 left blank.
  3. Indicator: The name of each Key Performance Indicator (KPI), including:
    Iteration Count: the number of iterations used during the tuning process.
    Final Adjustment: the final percentage (%) by which the Mean / SD / Correlation of the Market was adjusted.
    Sum of Total Error: the minimized Total Quantity Error calculated across all Market Scenarios.
    Market Size: the number of Virtual Customer rows in the Output WTP Matrix defined by the user in the Configuration Dialog.
    Market Mean: the final Mean of all Product Distributions in the Market WTP Matrix (before the Product Means are individually modified by their 'Quality' variations).
    Market SD: the final Standard Deviation (SD) of all Product Distributions in the Market WTP Matrix (before the Product SD are individually modified by their 'Niche' variations).
    Market Correlation: the final Correlation between all Product Distributions in the Market WTP Matrix (before the mutual Product Correlations are individually modified by their 'Boosters' and 'Conformity' variations).
    Scenario Quantity Error: the aggregated Quantity Error from all Products calculated for each Market Scenario.
    Product Quantity Error: the aggregated Quantity Error for each Product aggregated across all Market Scenarios.
  4. Value: The final value of each Key Performance Indicator (KPI).

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