1. A computer implemented method for generation of demand groups comprising: receiving a product listing via a processor, wherein the product listing includes products comprising existing products of a product line and at least one new product with insufficient information for demand modeling that is being considered for inclusion in the product line; receiving product data for each of the products in the product listing and demand coefficients for existing products in the product listing via the processor; scanning the existing products via scanners at a network node to generate point of sales data and receiving the point of sales data for the existing products in the product listing over a network via the processor; assigning attributes to each of the products of the product listing based upon the product data via the processor, wherein the product data includes product descriptors, and assigning the attributes to the products includes natural language processing of the product descriptors via the processor, wherein the product descriptors are provided in a natural language and the natural language processing enables the processor to generate machine readable attribute data and indicate meanings by annotating terms of the product descriptors with information indicating corresponding characteristics to assign the attributes; analyzing, via the processor, the point of sales data and generating a transition matrix including one or more pairs of existing products and, for each pair of products, a sum of a quantity of occurrences of transitions within a series of purchases to identify substitutable products, wherein a transition is represented by a switch from a first product of that pair in a first purchase to a second product of that pair in a second immediately succeeding purchase; clustering data of the existing products and at the least one new product of the product listing via the processor by: clustering data of the existing products based on the received point of sales data and demand coefficients, the transition matrix, and the assigned attributes including the machine readable attribute data and meanings via the processor; and clustering data of the at least one new product by: determining a distance between the clustered data of the existing products and the assigned attributes of the at least one new product including the machine readable attribute data and meanings from the natural language processing of the product descriptors; and clustering the data of the at least one new product with the clustered data of the existing products based on the determined distance; generating a decision tree modeling consumer decisions for existing products of the product listing utilizing the point of sales data via the processor; receiving, via the processor, at least one demand rule specifying one or more criteria for the demand groups; generating demand groups of the substitutable products, via the processor, by applying the received at least one demand rule to at least one of the decision tree and the clusters of data of the products of the product listing; and setting prices based on the generated demand groups.