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Leveraging e-Commerce Performance through Machine Learning Algorithms

Author

Listed:
  • Adrian MICU

    (Dunarea de Jos University of Galati, Romania)

  • Marius GERU

    (Transilvania University of Brasov, Romania)

  • Alexandru CAPATINA

    (Dunarea de Jos University of Galati, Romania)

  • Constantin AVRAM

    (Dunarea de Jos University of Galati, Romania)

  • Robert RUSU

    (Dunarea de Jos University of Galati, Romania)

  • Andrei Alexandru PANAIT

    (Transilvania University of Brasov, Romania)

Abstract
Machine learning (ML) is quickly emerging as a new discipline and resembles to be an attractive alternative to statistical methods in various industries. An appreciation of the possible applications of ML in digital marketing and eCommerce will be proposed in this article. The authors will examine qualitative determinant factors on brand logos and correlations on companies income, with profit, the number of employees, images and number of product images on eCommerce homepage. 1420 Romanian companies were analyzed in this research in order to identify specific factors that determines the success of an eCommerce business.

Suggested Citation

  • Adrian MICU & Marius GERU & Alexandru CAPATINA & Constantin AVRAM & Robert RUSU & Andrei Alexandru PANAIT, 2019. "Leveraging e-Commerce Performance through Machine Learning Algorithms," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 2, pages 162-171.
    • Handle: RePEc:ddj:fseeai:y:2019:i:2:p:162-171
    DOI: https://doi.org/10.35219/eai1584040947
    as

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    References listed on IDEAS

    as
    1. ,, 1998. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 14(1), pages 151-159, February.
    2. Bettels, Jannick & Wiedmann, Klaus-Peter, 2019. "Brand logo symmetry and product design: The spillover effects on consumer inferences," Journal of Business Research, Elsevier, vol. 97(C), pages 1-9.
    3. ,, 1998. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 14(5), pages 687-698, October.
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    7. Park, C. Whan & Eisingerich, Andreas B. & Pol, Gratiana & Park, Jason Whan, 2013. "The role of brand logos in firm performance," Journal of Business Research, Elsevier, vol. 66(2), pages 180-187.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Anton Zhuk & Oleh Yatskyi, 2024. "The use of artificial intelligence and machine learning in e-commerce marketing," Technology audit and production reserves, PC TECHNOLOGY CENTER, vol. 3(4(77)), pages 33-38, June.

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