List Price Optimization Using Customized Decision Trees
| Autor: | K. Raghava Rau, Kiran Rama, John Kiran, Shashank Shekhar, Sam Pritchett, Anit Bhandari, Parag Chitalia |
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| Rok vydání: | 2016 |
| Předmět: |
0209 industrial biotechnology
Discounting Mean squared error Earnings Operations research Computer science business.industry Decision tree 02 engineering and technology Business-to-business computer.software_genre 020901 industrial engineering & automation Stock keeping unit 0202 electrical engineering electronic engineering information engineering Entropy (information theory) 020201 artificial intelligence & image processing Data mining business Database transaction computer |
| Zdroj: | Machine Learning and Data Mining in Pattern Recognition ISBN: 9783319419190 ICDM Advances in Data Mining. Applications and Theoretical Aspects ISBN: 9783319415604 |
| DOI: | 10.1007/978-3-319-41920-6_7 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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