Bibliographic Details
| Title: |
INTEGRATION OF DIGITAL TOOLS IN OPTIMIZING AGRIBUSINESS MARKETING POTENTIAL - CASE OF SLOVAKIA. |
| Authors: |
PARKHOMENKO, Nataliia1,2 nataliia.parkhomenko@fm.uniba.sk, VILCEKOVA, Lucia1 lucia.vilcekova@fm.uniba.sk, STARCHON, Peter1 peter.starchon@fm.uniba.sk, DEMENTIEV, Оleksandr2 dlada12@meta.ua |
| Source: |
Scientific Papers Series Management, Economic Engineering in Agriculture & Rural Development. 2025, Vol. 25 Issue 4, p673-686. 14p. |
| Subjects: |
Web analytics, Social media, Digital technology, Cognitive structures, Agricultural industries, Customer relations, Webometrics |
| Geographic Terms: |
Slovakia |
| Abstract: |
This study investigates the impact of digital tools, including web analytics, social media, and cognitive models, on the marketing potential of agricultural businesses. Using fuzzy cognitive maps and various web analytics methods, we analyzed the websites of Slovak companies. The findings show that social and search traffic significantly influences metrics such as time on site, bounce rates, and page views per visit. Social media traffic increases user engagement, while search traffic reduces bounce rates, enhancing the overall user experience. The analysis also highlights the importance of reducing bounce rates to improve conversions. The study concludes that integrating digital tools can optimize marketing strategies, leading to increased engagement and competitiveness in the global market. However, challenges such as infrastructure limitations and high initial costs must be considered when implementing these strategies. Future research should focus on expanding the model to include external factors and competitive analysis. [ABSTRACT FROM AUTHOR] |
|
Copyright of Scientific Papers Series Management, Economic Engineering in Agriculture & Rural Development is the property of University of Agronomical Medical Sciences & Veterinary Medicine Bucharest and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Engineering Source |