What’s New

Scientific Strategy offers 26 Premium Nodes which are not available as part of the free Community Edition. Premium Nodes:

  • clean real-world data,
  • connect real-world data to Market Simulations, and
  • provide advanced Market Science analysis.

The available Premium Nodes include:

Ungroup Words Node: The Ungroup Words node is designed to take a user selected column and Ungroup the Words found in each input String into separate rows. The results can be used to identify a Product Name, SKU Number, or Brand from a general Description of the Product. Both Chinese and English is currently supported.

String Repair Node: The String Repair node is typically designed to look through an Input Product Array for the String columns selected by the user for clean up and repair. Dirty string problems can arise when crawling data from competitor websites.
Brand Discover Node: The Brand Discover node is designed to take a long list of Brand names and intelligently group them into a Brand Dictionary. The Brand Dictionary can then be used by a downstream Brand Repair node to clean up and repair the Brand names found in an Input Product Array. Both Chinese and English is currently supported.
Brand Repair Node: The Brand Repair node is designed to look through an Input Product Array for raw Brand names and match them against a cleaned Brand Dictionary. If a match is found then the raw Brand name will be replaced by the clean Brand name found in the Brand Dictionary. Both Chinese and English is currently supported.
Correlation Repair Node: The Correlation Repair node replaces missing Product Attributes with the Attributes found in highly correlated Products. For example, if the user wishes to repair the ‘Brand’ column, then all Products with missing Brand values are compared against similar Products having a Brand value.
Tag Importance Node: The Tag Importance node correlates the Features, Benefits, Attributes and Consumer Sentiment that describe a Product with the sales performance of that Product. Each Tag is correlated against: Product Purchased, Product In Consideration Set, Product Ranking, Customer Willingness To Pay, Product Price, etc.
Keyword Resistance Node: The Keyword Resistance node is designed to identify all of the Paths from the Keyword Search Results to the Product Buy Pages. The node then aggregates the total Customer Resistance to moving along any of those Paths to find the Product.
Product Ranking Node: The Product Ranking node is designed to take a set of Products in a Market and determine how each Virtual Customer ranks each of those Products. Rankings are then used to build Cumulative Rank Histograms and Venn Diagrams, and are used to calculate Expected Value.
Competitive Radar Node: The Competitive Radar node determines the degree of Competitive Rivalry between Products in a Market. The competitive landscape can then be plotted in a scatter plot or bubble chart with the Focus Product located at the (0, 0) origin and each of the Competitive Rival Products located within a concentric circle up to a distance of 1.0 from that origin.
Similarity Family Node: The Similarity Family node takes a super-set of Products and allocates those Products into a smaller set of Product Families. The Products are allocated in accordance to their mutual correlation as well as whether the Products have the same Brand, Store, Location, Category, and Platform.
Correlation Segmentation Node: The Correlation Segmentation node allocates all Products into Product Families based upon their mutual Correlation. Very similar Products, as perceived by Customers, should end up in the same Product Family, whereas different Products should end up in different Product Families.
Store Match Node: The Store Match node finds the best match across each Family of Products sold by the Brand’s Master Distributor. The Matching Algorithm relies heavily upon text-matching within the Description, but the Correlation can also be used if the Input Similarity Rankings are provided.
Similarity Matrix Node: The Similarity Matrix node is designed to take a super-set of Products and determine which of those Products should be included in the Market based upon their Similarity Rankings and Quantity sold. Only those Competitive Products most relevant to the ‘Focus Product’ are included in the Market.
Similarity Collapse Node: The Similarity Collapse node takes a super-set of Products and collapses them into a smaller set of aggregated Products. At the same time, the Similarity Collapse node creates a corresponding Output Correlation Matrix based upon the smaller set of aggregated Products. In this way, a large Market can be simulated through a smaller set of representative Products.
Elasticity Similarity Node: The Elasticity Similarity node generates a ‘Product Similarity Rankings’ table by calculating the degree of Cross-Elasticity between Products using historic sales data from one or more Markets. The Output Product Similarity Rankings table can then be used to generate a WTP Matrix containing the Willingness To Pay (WTP) of each Customer for each Product in a Market.
Clickstream Similarity Node: The Clickstream Similarity node converts a Clickstream Log File into a Product Similarity Rankings table that can be used by downstream nodes to generate a Product Correlation Matrix. The Output Product Similarity Rankings table can then be used to generate a WTP Matrix containing the Willingness To Pay (WTP) of each Customer for each Product in a Market.
Clickstream Conjoint Node: The Clickstream Conjoint node calculates the Importance of Product Attributes to Customers by passing an eCommerce Clickstream into a Conjoint Analysis. By replacing a traditional survey-based study, this node vastly increases survey size, eliminates survey costs and complexity, and studies what Customers actually do (versus what they say in a survey).
Simulate Conjoint Node: The Simulate Conjoint node runs a Conjoint Analysis using data from a Willingness To Pay Matrix. The Input WTP Matrix can be generated upstream with ‘Differentation Horizontal’ and ‘Differentiation Vertical’ nodes. The Simulate Conjoint node calculates the Customer Importance of each Attribute.
Tune Asymmetric Node: The Tune Asymmetric node takes the WTP Matrix for Products from the Focus Store to generate a larger WTP Matrix for a wider Market. The node can be used when a lot is known about the Focus Store but much less is known about the sales of Competitive Stores other than what identical Products they stock, their Prices, and their relative magnitude.
Extend Market Node: The Extend Market node is designed to take what is known about a few Products, a few Stores, a few Brands, etc., and project outwards to create a wider Market which includes other vendors selling similar Products. The user can manually change the characteristics of similar but un-tuned Competitive Products using ‘Competitive Factors’ generated through their own expertise.
Profit At Risk Node: The Profit At Risk node is designed to take a set of Products in a Market and determine how easy or difficult it would be for Competitive Rivals to win those Customers away. A Competitor may design a coupon that precisely identifies those Customers who would have bought the Competitive Product offering the exact value that would induce the Customer to switch.
Market Mix Node: The Market Mix node is designed to take a set of Products and determine which mix, or subset, minimizes inventory carrying costs while generating nearly the same level of profitability. This capability is also known by the terms ‘Assortment Optimization’, ‘Shelf Optimization’, and ‘Merchandise Planning’.
Goal Seek Node: The Goal Seek node is designed to manipulate the Prices of a selection of Products in order to reach a set of user-defined Goals. Goals can be set for ‘Quantity’, ‘Share’, ‘Revenue’, ‘Margin’, or ‘Profit’ levels. For example, the user may wish to increase Revenue and Market Share for the Products sold by a given Store while retaining the Store’s existing level of Profitability.
What If Product Node: The What If Product node makes a user-defined change to a sub-set of Products and predicts how that change will impact the entire Market. For example, the user can predict the ‘Worst Case Scenario’ should the Top Competitive Rivals react after the Focus Store discounts their Product Prices.
What If Positioning Node: The What If Positioning node systematically alters the Focus Product’s Willingness To Pay (WTP) to make the Focus Product appear to Customers as either more alike or less alike each of the Competitive Rivals. The node then predicts the impact on the Market Share, Revenue, and Profitability of all the Products in the Market.
What If Tween Node: The What If Tween node takes a sub-set of Products and predicts how a gradually changing Scenario, across a user-defined range of values, will impact all Products in the Market. For example, the user may sweep a Price change for the Focus Store Products from -2% to +2% over 10 steps to predict the impact on Profitability at each step.