Node Description

Product Ranking Node

The Product Ranking node is designed to take a set of Products in a Market and determine how each Virtual Available Customer ranks each of those Products. The top ranked Products receive a Customer ranking of #1, with each subsequent Product receiving a lower ranking.

The Product Ranking node then groups these Customer rankings and answers the following questions:

  1. “How often does each Product fall within the #1 ranking bin, the #2 ranking bin, the #3 ranking bin, etc?”. This analysis shows the attractiveness of each Product in the mind of the consumer and the size of the potential market from the perspective of the Product.
  2. “Which other Product is in the #1 top ranking bin with me?”. This analysis describes the competitive rivalry faced by each Product when trying to extend the immediate market and capture more nearby Customers.
  3. “For those Customers who ranked my Product as #2 (or #3, #4, or lower) which Competitor’s Product did they rank as #1?”. This analysis helps the #2 Product understand which Competitive Rival they need to surpass in order to win more Customers. This same analysis also helps the low ranking #50 Product visualize their most unattainable Customers – those Customers who rank a Competitive Rival’s Product as #1.
  4. “What other Products should I recommend to a Customer when they consider my Focus Product?”. A ‘Recommendation Engine’ can be built from the ranking results to help drive profitability. Starting with the knowledge that a Customer is considering the Focus Product, it is possible to calculate the ‘Expected Value’ of each of the other Products in the Market. The Expected Value is the marginal Revenue and Profit that can be generated from those other Products when sold, multiplied by the probability that the Customer will buy those Products. The Focus Store’s other Products with the highest Expected Value should be recommended to the Customer.

This Premium Node is not available as part of the free Community Edition. Premium Nodes help clean and connect real-world data to Market Simulations, and provide advanced Market Science analysis. Note that these descriptions are often deliberately vague.


Product Ranking

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.


Product Array

The set of Products that define the Market. Each row corresponds to a Product that competes for customers in the Market.

WTP Matrix

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.



Users can configure the size of the ‘Top-N’ Bin. This ‘Top-N’ bin is used within the Output Venn Diagram and the Output Expected Value to aggregate all Competitive Products that are in the, say, Top-10 along with each Target Product. This ‘Top-N’ bin is also used within the Output Competitor Ranking Summary as the ‘Then Ranked Bin’ when counting how the Focus Product is ranked whenever a Product is ranked within the ‘Top-N’ by a Customer.


Rank Histogram

Counts each time each of the Products falls within each of the Histogram Ranking Bins. If the user selects a Cumulative Histogram then the ranking count will increase for each Ranking Bin until all Customers in the WTP Matrix is counted in the last bin.

Venn Diagram

Counts the number of Competitive Rivals in the ‘Top-N’ Products along with each Target Product. The ‘Top-N’ is set by the ‘In Top Bin Size’ in the Venn Diagram configuration options. For example, if Product P1 is ranked by Customers within in the Top-10 choices then count up the number of times Product P2, P3, and P4 are also ranked in the Top-10 along with Product P1.

Competitor Ranking Summary

Counts the number of #1 ranked Competitive Rivals for each Product at each Customer Ranking.

Expected Value

Calculates the Expected Value of each Product given the Focus Product is ranked within the Top Bin. The Expected Value is the Product’s Revenue and Profit multiplied by the Probability that the Customer will buy the Product.

Ranked Products Matrix

The list of Products and their ranking by each Virtual Customer.