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List Price Optimization Using Customized Decision Trees

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9729))

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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.

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Authors and Affiliations

  1. VMware Inc., Palo Alto, USA

    Kiran Rama, Shashank Shekhar, John Kiran, Raghava Rau, Sam Pritchett, Anit Bhandari & Parag Chitalia

Authors
  1. Kiran Rama
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  2. Shashank Shekhar
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  3. John Kiran
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  4. Raghava Rau
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  5. Sam Pritchett
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  6. Anit Bhandari
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  7. Parag Chitalia
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Corresponding author

Correspondence to Kiran Rama .

Editor information

Editors and Affiliations

  1. IBaI, Inst of Comp Vision and applied Comp Sci, Leipzig, Sachsen, Germany

    Petra Perner

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© 2016 Springer International Publishing Switzerland

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Rama, K. et al. (2016). List Price Optimization Using Customized Decision Trees. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2016. Lecture Notes in Computer Science(), vol 9729. Springer, Cham. https://doi.org/10.1007/978-3-319-41920-6_7

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  • DOI: https://doi.org/10.1007/978-3-319-41920-6_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41919-0

  • Online ISBN: 978-3-319-41920-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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