Differentiation Vertical

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The Differentiation Vertical node is designed to take a list of Features, along with an optional list of Variations, and quantify the Vertical Differentiation between each. The quantified Vertical Differentiation between all of the Feature Variations is expressed as a Mean, Standard Deviation (SD), and optional supplier Cost. The Mean and Standard Deviation (SD) are then combined with the Horizontal Differentiation Correlation Matrix in a downstream node to generate a set of part-worth Customer Distributions for each of the Feature Variations.

When Features (or Products) can be rank ordered in an objective way then they are said to exhibit Vertical Differentiation. Features often simultaneously exhibit both Vertical Differentiation and Horizontal Differentiation. But when all Customers universally agree that one Feature is better than another then Vertical Differentiation dominates. In that case, Customers can still disagree as to how much better the one Feature is from the other. This disagreement is reflected in a Customer Distribution comprising a range of part-worth values. When the Customer Distribution is also a Normal Distribution, then the range can be precisely described using a Mean and Standard Deviation (SD).

For example, there is only Vertical Differentiation between a '1-year warranty' and a '2-year warranty' because all Customers universally agree that 2-years is better than 1-year. But that is not to say that all Customers will make the same purchase decision when deciding between the two. Nor is it to say that Price is the only decision factor: Mean and Standard Deviation (SD) of the Customer Distribution, along with Price are all important.

Consider a Market in which Customers generally believe that if a Product fails then it will fail quickly. The Mean of the '2-year warranty' will still be greater than the Mean of the '1-year warranty', but the Standard Deviation (SD) for the '2-year warranty' will shrink. Hence there is a bias to select the '1-year warranty' even among Customers who place a high value on warranties.

Strictly speaking, supply Cost is not part of Vertical Differentiation. Nor, in fact, is Product Price. But the calculation of Feature supply Cost has been included here as a convenience as there is often a relationship between the value of a Feature and the Cost to supply that Feature (the difference between Value and Cost is called 'Value Created'). But there are many ways to calculate supply Cost, and calculating Feature Cost is but one way. Costs can be calculated at a Store-level, a Product-level, a Feature-level, or a Customer-level. Costs are only important to the Market Simulation when maximizing Profitability - hence Costs need not be calculated at all.

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

Input Ports

  1. Type: Data <b>Input Related Features</b>: The collection of related Feature names. These may be ordinal Features related by the fact that they can be ranked. For example, the Feature List may be '5-star', '4-star', '3-star', and '2-star'. Or these may be categorical Features: for example, 'Japanese', 'Korean', and 'German'. For ordinal Features, this node can scale a set of Mean and Standard Deviation (SD) output values using the dialog configurations. For categorical Features, the Mean and SD values should be specified in the Input Related Features table itself. The Input Related Features should include the following columns: <ol> <li> <b>Feature</b> (string): The name of all the related Features that will appear within the Output Feature List. The Vertical Differentiation, along with the Horizontal Differentiation, of the Feature needs to be described to generate a Customer Distribution and build a Product Willingness To Pay (WTP) Matrix.</li> <li> <b>Mean</b> (double): (optional) The Mean of the part-worth values that Customers place upon the standard Feature. The Customer Distribution for each Feature comprises of part-worth values and has a Mean and Standard Deviation (SD). A fixed Mean value from the input table will override the user settings in the Configuration Dialog. The Feature Mean can be used as a reference benchmark for calculating Feature Standard Deviation (SD) and Feature Cost. In addition, the 'Quality' field within the Input Feature Variations table will either increase or decrease the Feature Mean.</li> <li> <b>SD</b> (double): (optional) The Standard Deviation (SD) of the part-worth values that Customers place upon the standard Feature. The Customer Distribution for each Feature comprises of part-worth values and has a Mean and SD. A fixed SD value from the input table will override the user settings in the Configuration Dialog. The Feature Mean can be used as a reference benchmark for calculating Feature Standard Deviation (SD). In addition, the 'Niche' field within the Input Feature Variations table will either increase or decrease the Feature Standard Deviation (SD).</li> <li> <b>Cost</b> (double): (optional) The economic Cost to the supplier to provide the standard Feature as part of the overall Product. Product Costs are only important when attempting to maximize Profitability. Furthermore, it is not necessary to calculate Costs on a Feature-by-Feature basis as a single total Product Cost can be specified elsewhere. A fixed Cost value from the input table will override the user settings in the Configuration Dialog. The Feature Mean can be used as a reference benchmark for calculating Feature Cost. In addition, the 'Expense' field within the Input Feature Variations table will either increase or decrease the Feature Cost.</li> </ol>
  2. Type: Data <b>Input Feature Variations</b>: (optional) A Variation of a Feature may be associated with a Brand, Product, Channel, Demographic, or Technology. A Variation may also be an Attribute from Conjoint Analysis, such that: Variation = Attribute, and Feature = Level. If the Variation is the name of a Brand, then all Products having the same Brand will exhibit the same Variation on the Feature. For example, 'Sony', 'Samsung', 'Canon', and 'Apple' may all offer their own Variations of the Features listed. The Brands 'Sony', 'Samsung', and 'Canon' may all have a Quality = 0.00 (they all offer a normal level of Feature quality), whereas 'Apple' may have a Quality = +0.20 (20% greater than the normal level) because Apple's Products have a reputation for higher quality. The Input Feature Variations should include the following columns: <ol> <li> <b>Variation</b> (string): The Variation name to give to each of the related Features. A Variation may be associated with a Brand, Product, Channel, Demographic, or Technology. A Variation may also be an Attribute from Conjoint Analysis, such that: Variation = Attribute, and Feature = Level. If the Variation is the name of a Brand, then all Products having the same Brand will exhibit the same Variation on the Feature. For example, 'Sony', 'Samsung', 'Canon', and 'Apple' may all offer their own Variations of the Features listed.</li> <li> <b>Feature</b> (string): (optional) If a Feature is specified in the Input Feature Variations list, then only the specified Features will have the Variation. If the Feature column is missing, or if the Feature cell is blank, then all Features will have this Variation.</li> <li> <b>Quality</b> (double): (optional) The relative Quality the Variation has from a Feature norm. The Quality Variation modifies the 'Mean' of the Feature. Quality = 0.0 (default) means that the Variation offers what is expected from the normal Feature (no change to the Mean). Quality = +1.0 means that the Variation is a vast improvement from the norm (200% x Mean). Quality = -1.00 means that the Variation is vastly inferior to the norm (50% x Mean). Quality = +/- 0.05 is typical, and would be used to generate a range of Features that all offer small Variations of about 5% around what is accepted as a Feature norm. Note that objectively inferior Products with poor relative Vertical Differentiation (that is, with a Quality Variation &lt; 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 &gt; 0.0).</li> <li> <b>Niche</b> (double): (optional) Whether the Variation is relatively more appealing to a Customer Niche or to a mass market. The Niche Variation modifies the Standard Deviation (SD) of the Feature. Niche = 0.0 (default) means that the Variation offers what is expected from the normal Feature (no change to the SD). Niche = +1.0 means that the Variation's Customer Distribution has a wider variance than the norm (200% x SD). Niche = -1.00 means that the Variation has a tighter variance than the norm (50% x SD). Niche = +/- 0.10 is typical (about 10% variance from the norm). Niche Variation 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 &gt; +0.10 (110% x SD).</li> <li> <b>Expense</b> (double): (optional) The Variation to the economic Cost to the supplier to provide the Feature as part of the overall Product. Expense = 0.0 (default) means that the Cost of the Feature Variation to the supplier is exactly the same as the normal Feature. Expense = +1.0 means that the Variation's Cost is higher than the norm (200% x Cost). Expense = -1.00 means that the Variation's Cost is lower than the norm (50% x Cost). Features offering greater Mean part-worth value tend to Cost more to supply. Personalization may also increase Costs, so Niche Variation &gt; 0.0 and Expense Variation &gt; 0.0. Alternatively, Cost reduction initiatives may remove specialized features and reduce appeal to a more Price-sensitive mass market, so Niche Variation &lt; 0.0 and Expense Variation &lt; 0.0. Note that Product Costs are only important when attempting to maximize Profitability, nor do they need to be calculated here on a Feature-by-Feature basis.</li> </ol>

Output Ports

  1. Type: Data <b>Output Feature List</b>: The output set of objective metrics for each Feature Variation. These Vertical Differentiation objective metrics are combined with the Horizontal Differentiation Correlation Matrix in downstream nodes to generate a set of part-worth Customer Distributions and, ultimately, a Willingness To Pay (WTP) Matrix. The Output Correlation Matrix will contain these columns: <ol> <li> <b>Feature</b>: The unique identifier for each Feature Variation found in the Input Related Features table and optional Input Feature Variations table. The 'Output Name Format' in the Configuration Dialog sets the format for the combined Feature Variation name. These Feature Variation names must match in both the Vertical Differentiation tables and the Horizontal Differentiation tables. The names must also match the Input Product Features table used when aggregating together all of the Features that make up each Product in the Market.</li> <li> <b>Mean</b>: The Mean of the part-worth values in the Customer Distribution for the Feature Variation. The relative difference of the Means between Related Features reflects the primary degree of Vertical Differentiation between each.</li> <li> <b>SD</b>: The Standard Deviation (SD) of the part-worth values in the Customer Distribution for the Feature Variation. A Product lacking Vertical Differentiation (that is, having a low Mean) can still attract Customers if it has a relatively high SD, or if it has Horizontal Differentiation (that is, its Customer Distribution uncorrelated) relative to other Products.</li> <li> <b>Cost</b>: The Cost born by the supplier to provide the Feature to the Customer. Generating each Feature Cost here is but one way of calculating the overall supply Cost of the Product to Customers. A fixed Cost and Profit Margin can be generated elsewhere. Or each Customer may have a different 'Cost to Serve' that can be calculated separately.</li> </ol>

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Scientific Strategy > Market Simulation

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