Social networks as a tool of modern marketing technologies
https://doi.org/10.34020/1993-4386-2024-4-143-152
Abstract
Modern business society is increasingly focused on using social media to promote its brand and attract new customers.
Any social network is a social structure consisting of a multitude of agents (individual and collective) and relationships between them, which in any country are regulated by relevant regulatory legal acts. The article provides a brief description of the legal support for the functioning of social networks in Russia.
The author pays special attention to such popular social networks as VKontakte, Telegram and Odnoklassniki: the factors contributing to their development are analyzed, and the structure of their target audience is assessed.
The issues of possible protection of users of social networks (from malicious and unwanted information) carried out using artificial neural networks (ANN) are considered, the main ways of using ANN in marketing are given, a brief description of possible options for using ANN in product marketing and in assessing the state of social networks is given.
The essence of the strategy for promoting goods or services on social networks is defined and the types of strategies are highlighted, as well as the characteristics of the multi-stage process of creating a content marketing strategy.
About the Author
S. M. Thamokova
V. M. Kokov Kabardino-Balkarian State Agrarian University
Russian Federation
Russian Federation
Svetlana M. Thamokova – Candidate of Economic Sciences, Associate Professor of the Department of Economic
Nalchik, Kabardino-Balkarian Republic
References
1. Akopov G. V., Glushkova S. A. Social networks and their psychological impact, Vestnik nauki, 2022, T. 4, No. 6 (51), рр. 148–156. (In Russ.).
2. Gubanov D. A., Novikov D.A., Chkhartishvili A. G. Sotsial'nye seti: modeli informatsionnogo vliyaniya, upravleniya i protivoborstva [Social networks: models of information influence, management and confrontation], Moscow: Izd-vo fiziko-matematicheskoi literatury, 2010, 228 р. (In Russ.).
3. Selezenev R. S., Skripak E. I. Social networks as a phenomenon of the information society and the specifics of social connections in their environment, Vestnik Kemerovskogo gosudarstvennogo universiteta, 2013, No. 2–3 (54), рр. 125–131.
4. Boyd D. M., Ellison N. B. Social Network Sites: Definition, History, and Scholarship. Journal of Computer-Mediated Communication, 2007, Vol. 13, Issue 1, рр. 210–230. DOI: 10.1111/j.1083-6101.2007.00393.x
5. Belova D. A. Research and analysis of the target audience of SMM promotion services in VKontakte, Issledovaniya molodykh uchenykh [Research by young scientists], Proceedings of the LIII International Scientific Conference (Kazan, January 2023), Kazan: Molodoi uchenyi, 2023, рр. 7–10. (In Russ.) Available et: https://moluch.ru/conf/stud/archive/476/17734/ (date of access: 24.12.2024).
6. Vevyurko V. A., Telepchenkova I. A., Bondaletov V. V. Analysis of the Telegram social network as a mass media and communication, Materialy Afanas'evskikh chtenii, 2020, No. 2 (31), рр. 46–51. (In Russ.).
7. Vertaim K., Fenvik Ya. Tsifrovoi marketing: kak uvelichit' prodazhi s pomoshch'yu sotsial'nykh setei, blogov, viki-resursov, mobil'nykh telefonov i drugikh sovremennykh tekhnologii [Digital marketing: how to increase sales using social networks, blogs, wiki resources, mobile phones and other modern technologies], Moscow: Al'pina Pablisher, 2013, 411 р. (In Russ.)
8. Branitskii A. A., Doinikova E. V., Kotenko I. V. Using neural networks to predict the exposure of social network users to destructive influences, Informatsionno-upravlyayushchie sistemy, 2020, No. 1 (104), рр. 24-33. (In Russ.).
9. Kotenko I., Chechulin A., Komashinsky D. Categorisation of web pages for protection against inappropriate content in the internet. International Journal of Internet Protocol Technology (IJIPT), 2017, Vol. 10, No. 1, рр. 61–71.
10. Kotenko I. V., Chechulin A. A., Komashinskii D. V. Automated website categorization to block web pages with inappropriate content. Information security issues, Komp'yuternye sistemy, 2015, No. 2, рр. 62–68. (In Russ.).
11. Novozhilov D. A., Chechulin A. A., Kotenko I. V. Improving website categorization to block inappropriate content based on HTML tag statistics analysis, Informatsionno-upravlyayushchie sistemy, 2016, No. 6, рр. 65–73. (In Russ.). DOI: 10.15217/issn1684-8853.2016.6.65
12. Borisovskii S. A. Using artificial intelligence tools to promote consumer goods, prospects for the Russian market, Vestnik nauki, 2023, No. 6 (63), рр. 80–89. (In Russ.).
13. Bykova E. N. Optimization of targeted advertising through the use of neural networks, Sotsial'no-gumanitarnye problemy obrazovaniya i professional'noi samorealizatsii (Sotsial'nyi inzhener-2022) [Socio-humanitarian problems of education and professional self-realization (Social Engineer-2022)], Proceedings of the All-Russian Scientific Conference of Young Researchers with International Participation (Moscow, December 12-16, 2022), Moscow: «Rossiiskii gosudarstvennyi universitet imeni A. N. Kosygina (Tekhnologii. Dizain. Iskusstvo)», 2022, р. 25–28. (In Russ.).
14. Tkachev V. V. Using neural networks to adapt content to the preferences of the audience in marketing, Ekonomika i biznes: teoriya i praktika, 2023, No. 5-3 (99), рр. 130–132. (In Russ.).
15. Shikula S. R. Social media content marketing as one of the effective ways to promote a product, Skif, 2021, No. 6 (58), рр. 130–136. (In Russ.) Available et: https://cyberleninka.ru/article/n/kontent-marketing-vsotsialnyh-setyah-kak-odin-iz-effektivnyh-sposobovprodvizheniya-tovara (date of access: 25.12.2024).
Review
For citations:
Thamokova S.M. Social networks as a tool of modern marketing technologies. Siberian Financial School. 2024;(4):143-152. (In Russ.) https://doi.org/10.34020/1993-4386-2024-4-143-152
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