IDEAS home Printed from https://ideas.repec.org/a/ibn/ijmsjn/v12y2020i1p39.html
   My bibliography  Save this article

The European Union’s GDPR and Its Effect on Data-Driven Marketing Strategies

Author

Listed:
  • Fadye Saud Al-Fayad

Abstract

This research paper analyzes the developing effect that the European Union’s (EU) recently developed General Data Protection Regulation (GDPR) will have on the marketing strategies of firms that rely on big data. Big data is identified as consisting of data and data analytics involving a huge volume of data, a diverse variety of data, and a high velocity of data capture and collection. This analysis begins with some discussion of the concept of big data and follows this up with overviews of both the GDPR and big data use in the marketplace. The EU replaced its older Data Protection Directive or DPD with the GDPR. The GDPR consists of a series of chapters and articles that require, among other things, consent to collect and store data, the anonymization of data, announcement in 72 hours of a data breach, provision of encryption and the identification of a Data Protection Officer. Marketing and the marketing function can implement emergent technologies that augment big data and its analysis while simultaneously achieving compliance with regulatory frameworks like the GDPR. These marketing related solutions are those such as blockchain marketing applications like Brave Browser and Blockstack among others. The report also examines the way in which enterprises use big data in their marketing strategies and how they are affected by it now that it has come into effect. Some of the more marketing-oriented uses and applications of big data are found in sophisticated loyalty programs, demand forecasting and customization either of experience or product/service. This study also offers some final recommendations related to GDPR compliant marketing strategies. These include the development of a comprehensive program to purchase consumer data directly from consumers and the introduction of blockchain as a means to facilitate a smoother transition to GDPR compliance.

Suggested Citation

  • Fadye Saud Al-Fayad, 2020. "The European Union’s GDPR and Its Effect on Data-Driven Marketing Strategies," International Journal of Marketing Studies, Canadian Center of Science and Education, vol. 12(1), pages 1-39, March.
  • Handle: RePEc:ibn:ijmsjn:v:12:y:2020:i:1:p:39
    as

    Download full text from publisher

    File URL: http://www.ccsenet.org/journal/index.php/ijms/article/download/0/0/42111/43814
    Download Restriction: no

    File URL: http://www.ccsenet.org/journal/index.php/ijms/article/view/0/42111
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shahriar Akter & Samuel Fosso Wamba, 2016. "Big data analytics in E-commerce: a systematic review and agenda for future research," Electronic Markets, Springer;IIM University of St. Gallen, vol. 26(2), pages 173-194, May.
    2. Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
    3. Bradlow, Eric T. & Gangwar, Manish & Kopalle, Praveen & Voleti, Sudhir, 2017. "The Role of Big Data and Predictive Analytics in Retailing," Journal of Retailing, Elsevier, vol. 93(1), pages 79-95.
    4. Yeh, Chih-Liang, 2018. "Pursuing consumer empowerment in the age of big data: A comprehensive regulatory framework for data brokers," Telecommunications Policy, Elsevier, vol. 42(4), pages 282-292.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Leogrande, Angelo, 2021. "The Destruction of Price-Representativeness," MPRA Paper 111239, University Library of Munich, Germany.
    2. Shivaji Alaparthi & Manit Mishra, 2021. "BERT: a sentiment analysis odyssey," Journal of Marketing Analytics, Palgrave Macmillan, vol. 9(2), pages 118-126, June.
    3. S. Vijayakumar Bharathi, 2017. "Prioritizing and Ranking the Big Data Information Security Risk Spectrum," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(3), pages 183-201, September.
    4. Aljumah, Ahmad Ibrahim & Nuseir, Mohammed T. & Alam, Md. Mahmudul, 2021. "Traditional Marketing Analytics, Big Data Analytics, Big Data System Quality and the Success of New Product Development," OSF Preprints 9auec, Center for Open Science.
    5. Zhiting Song & Yanming Sun & Jiafu Wan & Lingli Huang & Jianhua Zhu, 2019. "Smart e-commerce systems: current status and research challenges," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(2), pages 221-238, June.
    6. Ghasemaghaei, Maryam & Calic, Goran, 2019. "Does big data enhance firm innovation competency? The mediating role of data-driven insights," Journal of Business Research, Elsevier, vol. 104(C), pages 69-84.
    7. Fatih Pinarbasi & Zehra Nur Canbolat, 2019. "Big data in marketing literature: A bibliometric analysis," International Journal of Business Ecosystem & Strategy (2687-2293), Bussecon International Academy, vol. 1(2), pages 15-24, April.
    8. Raguseo, Elisabetta & Vitari, Claudio & Pigni, Federico, 2020. "Profiting from big data analytics: The moderating roles of industry concentration and firm size," International Journal of Production Economics, Elsevier, vol. 229(C).
    9. Wamba, Samuel Fosso & Dubey, Rameshwar & Gunasekaran, Angappa & Akter, Shahriar, 2020. "The performance effects of big data analytics and supply chain ambidexterity: The moderating effect of environmental dynamism," International Journal of Production Economics, Elsevier, vol. 222(C).
    10. Tabesh, Pooya & Mousavidin, Elham & Hasani, Sona, 2019. "Implementing big data strategies: A managerial perspective," Business Horizons, Elsevier, vol. 62(3), pages 347-358.
    11. Ahmad Ibrahim Aljumah & Mohammed T. Nuseir & Md. Mahmudul Alam, 2021. "Traditional marketing analytics, big data analytics and big data system quality and the success of new product development," Post-Print hal-03538161, HAL.
    12. de Camargo Fiorini, Paula & Roman Pais Seles, Bruno Michel & Chiappetta Jabbour, Charbel Jose & Barberio Mariano, Enzo & de Sousa Jabbour, Ana Beatriz Lopes, 2018. "Management theory and big data literature: From a review to a research agenda," International Journal of Information Management, Elsevier, vol. 43(C), pages 112-129.
    13. Aaltonen, Aleksi Ville & Alaimo, Cristina & Kallinikos, Jannis, 2021. "The making of data commodities: data analytics as an embedded process," LSE Research Online Documents on Economics 110296, London School of Economics and Political Science, LSE Library.
    14. Anke Joubert & Matthias Murawski & Markus Bick, 2023. "Measuring the Big Data Readiness of Developing Countries – Index Development and its Application to Africa," Information Systems Frontiers, Springer, vol. 25(1), pages 327-350, February.
    15. Namin, Aidin & Soysal, Gonca P. & Ratchford, Brian T., 2022. "Alleviating demand uncertainty for seasonal goods: An analysis of attribute-based markdown policy for fashion retailers," Journal of Business Research, Elsevier, vol. 145(C), pages 671-681.
    16. Rampersad, Giselle, 2020. "Robot will take your job: Innovation for an era of artificial intelligence," Journal of Business Research, Elsevier, vol. 116(C), pages 68-74.
    17. Sidney Anderson, 2024. "Expanding data literacy to include data preparation: building a sound marketing analytics foundation," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(2), pages 227-234, June.
    18. Chiara Mio & Silvia Panfilo & Benedetta Blundo, 2020. "Sustainable development goals and the strategic role of business: A systematic literature review," Business Strategy and the Environment, Wiley Blackwell, vol. 29(8), pages 3220-3245, December.
    19. Magdalena Rusch & Josef‐Peter Schöggl & Rupert J. Baumgartner, 2023. "Application of digital technologies for sustainable product management in a circular economy: A review," Business Strategy and the Environment, Wiley Blackwell, vol. 32(3), pages 1159-1174, March.
    20. Fatao Wang & Lihui Ding & Hongxin Yu & Yuanjun Zhao, 0. "Big data analytics on enterprise credit risk evaluation of e-Business platform," Information Systems and e-Business Management, Springer, vol. 0, pages 1-40.

    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ibn:ijmsjn:v:12:y:2020:i:1:p:39. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.