IDEAS home Printed from https://ideas.repec.org/a/ers/journl/vxxivy2021ispecial1-part2p3-16.html
   My bibliography  Save this article

Data Science and Marketing in E-Commerce Amid COVID-19 Pandemic

Author

Listed:
  • Olha Fedirko
  • Tetiana Zatonatska
  • Tomasz Wolowiec
  • Stanislaw Skowron

Abstract

Purpose: The objective of this study involves the determination of data-driven solutions needed to increase the usability of e-commerce systems and its profitability. Design/Methodology/Approach: In the research implementation process, logic generalization and induction to identify and analyze the most beneficial data science tools in e-commerce. deign of the study is to generalize existing approaches of data science usage in e-commerce, to develop practical recommendations to ensure the competitive advantages of e-commerce market participants and to estimate the cost of technical tools needed to launch the data science project in e-commerce. Findings: The results clearly demonstrate that in 2020 businesses that have e-commerce system were financially successful and in next 3 years online sales will increase rapidly. The simple analytics will not cover the demand of online business and it is needed to implement advanced data-driven decisions now. Practical Implications: The present research provides generalized knowledge on how to launch a data science project in e-commerce and how to choose the best programming and visualization app to ensure the profitability of a project. The scientific paper gives an instruction on the marketing contribution analysis, which is the tool of key importance for online marketplaces. Originality/Value: The main research value drawn from the study is to launch the data-driven models in e-commerce company it is needed to observe the real business need and available data, find the best programming and visualization tools. It was defined that the most beneficial data science solutions are demand forecasting, estimation of the marketing contribution, customers clustering, recommendation system and customers’ attitude analysis. The main business need for each e-commerce company is to estimate the contribution of all marketing channels and advertisement formats separately. This issue may be easily handled with a regression modelling, which helps to understand a set of factors influencing sales.

Suggested Citation

  • Olha Fedirko & Tetiana Zatonatska & Tomasz Wolowiec & Stanislaw Skowron, 2021. "Data Science and Marketing in E-Commerce Amid COVID-19 Pandemic," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 1), pages 3-16.
    • Handle: RePEc:ers:journl:v:xxiv:y:2021:i:special1-part2:p:3-16
    as

    Download full text from publisher

    File URL: https://ersj.eu/journal/2187/download
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Data science; marketing; e-commerce; online shopping.;
    All these keywords.

    JEL classification:

    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

    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:ers:journl:v:xxiv:y:2021:i:special1-part2:p:3-16. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Marios Agiomavritis (email available below). General contact details of provider: https://ersj.eu/ .

    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.