IDEAS home Printed from https://ideas.repec.org/a/eco/journ3/2024-04-7.html
   My bibliography  Save this article

Using Social Media Analysis to Improve E-commerce Marketing Strategies

Author

Listed:
  • Olha Semenda

    (Department of Marketing, Faculty of Economics and Entrepreneurship, Uman National University of Horticulture, Uman, Ukraine)

  • Yuliia Sokolova

    (Department of Marketing and Logistics, Faculty of Economy and Management, National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine)

  • Olena Korovina

    (Department of Marketing, Faculty of Trade and Marketing, State University of Trade and Economics, Kyiv, Ukraine)

  • Oleksandra Bratko

    (Department of International Economic Relations, B. Havrylyshyn Education and Research Institute of International Relations, West Ukrainian National University, Ternopil, Ukraine)

  • Iryna Polishchuk

    (Department of Information Systems in Management and Accounting, Zhytomyr Polytechnic State University, Zhytomyr, Ukraine)

Abstract

This study investigates the application of game theory and matrix-based analysis in enhancing social media marketing strategies for e-commerce businesses. By integrating these mathematical models with social media analytics, aim to provide a comprehensive framework that can predict consumer behavior, optimize competitive strategies, and improve engagement on digital platforms. This study's application of a game theory matrix model on social media marketing strategies showcased clear benefits for e-commerce entities, with aggressive marketing tactics boosting market share by 30% against passive competitors and achieving a 20% increase even when competitors also adopted aggressive approaches. The Nash Equilibrium emphasize the balanced market share gains when both firms engaged in aggressive strategies. Statistical analysis reinforced the efficacy of these strategies, with a chi-square test yielding a significant value of 13.4, suggesting a strong link between aggressive marketing and enhanced engagement metrics. Regression analysis further validated the impact of engagement on sales, indicating that a 1% increase in likes, comments, and shares corresponded to a 0.75% uplift in sales, evidenced by significant predictors with ß values of 0.25, 0.35, and 0.40 for likes, comments, and shares respectively. Content analysis and consumer surveys highlighted a preference for authentic, value-aligned content, with aggressive strategies leading to a 50% higher engagement rate and a 60% consumer preference for such content, emphasizing the critical role of strategic alignment with consumer expectations. Incorporating game theory and matrix-based analysis into e-commerce social media strategies offers a novel approach to understanding and leveraging the complex interplay of consumer interactions and competitive dynamics. This methodology enables marketers to devise more targeted, adaptive, and effective marketing campaigns, driving growth and enhancing consumer satisfaction in the competitive digital marketplace.

Suggested Citation

  • Olha Semenda & Yuliia Sokolova & Olena Korovina & Oleksandra Bratko & Iryna Polishchuk, 2024. "Using Social Media Analysis to Improve E-commerce Marketing Strategies," International Review of Management and Marketing, Econjournals, vol. 14(4), pages 61-71, July.
  • Handle: RePEc:eco:journ3:2024-04-7
    as

    Download full text from publisher

    File URL: https://www.econjournals.com/index.php/irmm/article/download/16196/8017
    Download Restriction: no

    File URL: https://www.econjournals.com/index.php/irmm/article/view/16196
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Game Theory; Social Media Analytics; E-commerce Strategies; Consumer Engagement; Market Share Optimization; Digital Marketing Innovation;
    All these keywords.

    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce

    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:eco:journ3:2024-04-7. 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: Ilhan Ozturk (email available below). General contact details of provider: http://www.econjournals.com .

    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.