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Deep Learning for Distribution Channels' Management

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  • Sabina-Cristiana NECULA

Abstract

This paper presents an experiment of using deep learning models for distribution channel management. We present an approach that combines self-organizing maps with artificial neural network with multiple hidden layers in order to identify the potential sales that might be addressed for channel distribution change/ management. Our study aims to highlight the evolution of techniques from simple features/learners to more complex learners and feature engineering or sampling techniques. This paper will allow researchers to choose best suited techniques and features to prepare their churn prediction models.

Suggested Citation

  • Sabina-Cristiana NECULA, 2017. "Deep Learning for Distribution Channels' Management," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 21(4), pages 73-84.
    • Handle: RePEc:aes:infoec:v:21:y:2017:i:4:p:73-84
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    Cited by:

    1. Shahriar Akter & Katina Michael & Muhammad Rajib Uddin & Grace McCarthy & Mahfuzur Rahman, 2022. "Transforming business using digital innovations: the application of AI, blockchain, cloud and data analytics," Annals of Operations Research, Springer, vol. 308(1), pages 7-39, January.

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