List Price Optimization Using Customized Decision Trees

Abstract

There are many data mining solutions in the market which cater to solving pricing problems to various sectors in the business industry. The goal of such solutions is not only to give an optimum pricing but also maximize earnings of the customer. This paper illustrates the application of custom data mining algorithms to the problem of list price optimization in B2B (Business to Business). Decision trees used are typically binary and pick the right order based on impurity measures like Gini/entropy and mean squared error (for example in CART). In our study we take a novel approach of non-binary decision trees with order of splits being the choice of business and stopping criteria being the classical. We exploit proxies for list price changes as discount %age and Special Pricing Form (SPF) discounting. We calculate transaction thresholds, anchor discounts and elasticity determinants for each Stock Keeping Unit (SKU) segment to arrive at recommended list price which gets used by pricing unit.