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  • 2022
  • Working Paper

Market Segmentation Trees

By: Ali Aouad, Adam Elmachtoub, Kris Ferreira and Ryan McNellis
  • Format:Print
  • | Language:English
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Abstract

As more commerce and media consumption are being conducted online, a wealth of new opportunities are emerging for personalized advertising. We propose a general methodology, Model Trees for Personalization (MTP), for tackling a broad class of personalized decision-making problems including personalized advertising. The MTPs learn an interpretable market segmentation driven by differences in user behavior. Using this methodology, we design two new algorithms for fundamental problems in personalized advertising – Choice Model Trees (CMTs) for the user ad-response prediction problem, and Isotonic Regression Model Trees (IRMTs) for the bid landscape forecasting problem. We provide a customizable, computationally-efficient, and open-source code base for training MTPs in Python. We train our IRMT algorithm on historical bidding data from three different ad exchanges and show that the IRMT achieves 5-29% improvement in bid landscape forecasting accuracy over the model that a leading demand-side platform (DSP) provider currently uses in production.

Keywords

Decision Trees; Computational Advertising; Market Segmentation; Digital Marketing; Customization and Personalization; Segmentation; Decision Making

Citation

Aouad, Ali, Adam Elmachtoub, Kris Ferreira, and Ryan McNellis. "Market Segmentation Trees." Working Paper, July 2022.
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About The Author

Kris Johnson Ferreira

Technology and Operations Management
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Related Work

    • 2022
    • Faculty Research

    Market Segmentation Trees

    By: Ali Aouad, Adam Elmachtoub, Kris Ferreira and Ryan McNellis
Related Work
  • Market Segmentation Trees By: Ali Aouad, Adam Elmachtoub, Kris Ferreira and Ryan McNellis
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