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Financial Technology and Innovation
Volume 3 , Issue 2, PP: 23-29 , 2023 | Cite this article as | XML | Html |PDF

Title

Leveraging Big Data Processing in Computer Networks for Effective Digital Marketing Strategies

  Kuzikulova Dilfuza 1 * ,   Rozikov Ravshan 2

1  Corporate Governance faculty, Tashkent State University of Economics, Uzbekistan
    (k.dilfuza@tsue.uz)

2  Corporate Governance faculty, Tashkent State University of Economics, Uzbekistan
    (r.rozikov@tsue.uz)


Doi   :   https://doi.org/10.54216/FinTech-I.030203

Received: June 17, 2023 Accepted: December 22, 2023

Abstract :

In the rapidly evolving digital landscape, businesses are increasingly turning to digital marketing strategies to engage their target audiences effectively. The abundance of data generated through online interactions presents both opportunities and challenges in leveraging it for marketing purposes. This research investigates the potential of integrating big data processing in computer networks to optimize digital marketing strategies and enhance customer targeting. By exploring the current state of digital marketing practices and the role of computer networks in data processing, this study aims to uncover the benefits and limitations of incorporating big data analytics. Drawing from successful case studies, innovative approaches are proposed to integrate big data into marketing platforms, enabling improved customer segmentation and personalized content delivery. Additionally, the impact of big data utilization on customer experience and brand loyalty is examined. Ethical considerations and privacy concerns are also addressed to ensure responsible data usage. Adopting a mixed-methods approach, qualitative and quantitative data are collected, enabling a comprehensive evaluation of the effectiveness and return on investment of marketing campaigns. This research contributes to the existing knowledge by providing valuable insights for businesses to make informed decisions in enhancing their digital marketing strategies while adhering to ethical data practices, fostering customer trust, and gaining a competitive edge in the digital era.

Keywords :

Digital marketing; Big Data processing; Computer networks; Data-driven marketing; Customer segmentation; Personalization; Ethical data usage

References :

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[10] Mikalef, P., van de Wetering, R., & Krogstie, J. (2021). Building dynamic capabilities by leveraging big data analytics: The role of organizational inertia. Information & Management, 58(1), 103382.

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Cite this Article as :
Style #
MLA Kuzikulova Dilfuza, Rozikov Ravshan. "Leveraging Big Data Processing in Computer Networks for Effective Digital Marketing Strategies." Financial Technology and Innovation, Vol. 3, No. 2, 2023 ,PP. 23-29 (Doi   :  https://doi.org/10.54216/FinTech-I.030203)
APA Kuzikulova Dilfuza, Rozikov Ravshan. (2023). Leveraging Big Data Processing in Computer Networks for Effective Digital Marketing Strategies. Journal of Financial Technology and Innovation, 3 ( 2 ), 23-29 (Doi   :  https://doi.org/10.54216/FinTech-I.030203)
Chicago Kuzikulova Dilfuza, Rozikov Ravshan. "Leveraging Big Data Processing in Computer Networks for Effective Digital Marketing Strategies." Journal of Financial Technology and Innovation, 3 no. 2 (2023): 23-29 (Doi   :  https://doi.org/10.54216/FinTech-I.030203)
Harvard Kuzikulova Dilfuza, Rozikov Ravshan. (2023). Leveraging Big Data Processing in Computer Networks for Effective Digital Marketing Strategies. Journal of Financial Technology and Innovation, 3 ( 2 ), 23-29 (Doi   :  https://doi.org/10.54216/FinTech-I.030203)
Vancouver Kuzikulova Dilfuza, Rozikov Ravshan. Leveraging Big Data Processing in Computer Networks for Effective Digital Marketing Strategies. Journal of Financial Technology and Innovation, (2023); 3 ( 2 ): 23-29 (Doi   :  https://doi.org/10.54216/FinTech-I.030203)
IEEE Kuzikulova Dilfuza, Rozikov Ravshan, Leveraging Big Data Processing in Computer Networks for Effective Digital Marketing Strategies, Journal of Financial Technology and Innovation, Vol. 3 , No. 2 , (2023) : 23-29 (Doi   :  https://doi.org/10.54216/FinTech-I.030203)