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Global Journal of Flexible Systems Management

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07-02-2024 | ORIGINAL RESEARCH

Predicting Consumer Behavior Based on Big Data of User-Generated Online Content in Retail Marketing

Author: Gleb Karpushkin

Published in: Global Journal of Flexible Systems Management | Issue 1/2024

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Abstract

The purpose of this study is to create, using big data from user content in retail marketing, a prediction approach to predicting consumer behavior. Based on an approach with two key components, prediction is achievable. The first step is accurate big data analytics of user-generated material, highlighting the essential information and monitoring changes in user behavior (posting and purchasing) as a result of shifting value proposition variables. Second, it involves the inclusion of specialists and seasoned marketers in sociological surveys that use large samples of respondents and the probabilistic method. The value proposition structure was broken down into ten components that influence the rhetoric of user content using the stratification approach. The competitive advantages or business objectives of stores, in turn, made clear the essential categories of user content. The study focuses on Russia’s most widely used digital trading platforms. The study developed an approach to the expert prediction of consumer behavior following changes in content quality and highlighted efficient digital tools for doing so using the sociological technique. The methodology for expert forecasting of consumer behavior amid changes in the quality of user content was developed using empirical, probabilistic, and sociological methods. The competitive advantage or goal of an online store was shown to be the most important element in altering consumer behavior. The proposed expert prediction methodology based on the likelihood matrix of a decline in customer conversion rates due to user content degradation is the study’s scientific contribution.

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Aliahmadi, A., Nozari, H., & Ghahremani-Nahr, J. (2022). A framework for IoT and blockchain based on marketing systems with an emphasis on Big Data analysis. International Journal of Innovation in Marketing Elements, 2(1), 25–34. https://doi.org/10.52547/ijime.2.1.25CrossRef

Ariannezhad, M., Jullien, S., Nauts, P., Fang, M., Schelter, S., & de Rijke M. (2021). Understanding multi-channel customer behavior in retail. In Proceedings of the 30th ACM international conference on information & knowledge management (pp. 2867–2871). Association for Computing Machinery. https://doi.org/10.1145/3459637.3482208

Arkhipova, N., & Karminsky, A. (2023). Demographic characteristics as determinants of retail customers’ credit behavior. Evidence from Russian regions. Procedia Computer Science, 221, 1091–1098. https://doi.org/10.1016/j.procs.2023.08.092CrossRef

Bappy, M. M., Key, J., Hossain, N. U. I., & Jaradat, R. (2022). Assessing the social impacts of additive manufacturing using hierarchical evidential reasoning approach. Global Journal of Flexible Systems Management, 23, 201–220. https://doi.org/10.1007/s40171-021-00295-5CrossRef

Bowden, J., & Mirzaei, A. (2021). Consumer engagement within retail communication channels: An examination of online brand communities and digital content marketing initiatives. European Journal of Marketing, 5(5), 1411–1439. https://doi.org/10.1108/EJM-01-2018-0007CrossRef

Chaudhary, K., Alam, M., Al-Rakhami, M. S., & Gumaei, A. (2021). Machine learning-based mathematical modelling for prediction of social media consumer behavior using big data analytics. Journal of Big Data, 8, 73. https://doi.org/10.1186/s40537-021-00466-2CrossRef

Chen, S. C., & Shang, S. S. (2021). Sustaining user experience in a smart system in the retail industry. Sustainability, 13(9), 5090. https://doi.org/10.3390/su13095090CrossRef

Dement’eva, I. N., & Shakleina, M. V. (2019). Primeneniye indeksnogo metoda v issledovaniyakh potrebitel'skikh nastroyeniy naseleniya [Application of the index method in studies of consumer sentiment of the population]. Economic and Social Changes: Facts, Trends, Forecast, 12(1), 153–173.

Engel, J. F., Blackwell, R. D., & Winiard, P. W. (1994). Perilaku Konsumen. Binarupa Aksara.

Febrian, A., Bangsawah, S., MS, M., & Ahadiat, A. (2021). Digital content marketing strategy in increasing customer engagement in Covid-19 situation. International Journal of Pharmaceutical Research, 13(1), 4797–4805. https://doi.org/10.31838/ijpr/2021.13.01.684CrossRef

Gonzalez, C. (2010). Social media best practices for communication professionals through the lens of the fashion industry [Published doctoral dissertation]. University of Southern California.

Gorelova, A. A. (2017). Bol'shiye dannyye i napravleniya ispol'zovaniya v marketing [Big data and directions of their use in marketing]. Actual Problems of the Humanities and Natural Sciences, 4–2, 11–16.

Gorobchenko, S. L., & Artamonov, O. N. (2019). Novyye vozmozhnosti marketinga bol’shikh baz dannykh. dnevnik marketologa [New opportunities for marketing large databases. marketer’s diary]. Pipeline Fittings and Equipment, 2(101), 52–54.

Grupac, M., & Lăzăroiu, G. (2022). Image processing computational algorithms, sensory data mining techniques, and predictive customer analytics in the metaverse economy. Review of Contemporary Philosophy, 21, 205–222. https://doi.org/10.22381/RCP21202213CrossRef

Kumar, S., Ashoka Rajan, R., Swaminathan, A., & Johnson, E. (2022a). Hyper-personalization and its impact on customer buying behaviour. In Data Intelligence and Cognitive Informatics: Proceedings of ICDICI 2022 (pp. 649–664). Springer Nature.

Kumar, V., Vrat, P., & Shankar, R. (2022b). Factors influencing the implementation of industry 4.0 for sustainability in manufacturing. Global Journal of Flexible Systems Management, 23, 453–478. https://doi.org/10.1007/s40171-022-00312-1CrossRef

Kushwaha, A. K., Kar, A. K., & Dwivedi, Y. K. (2021). Applications of big data in emerging management disciplines: A literature review using text mining. International Journal of Information Management Data Insights, 1(2), 100017. https://doi.org/10.1016/j.jjimei.2021.100017CrossRef

Lahunou, I. (2022). Your complete guide to predicting customer behavior. Verfacto. https://www.verfacto.com/blog/behavioral-data/predicting-customer-behavior/

Liu, H. (2021). Big data precision marketing and consumer behavior analysis based on fuzzy clustering and PCA model. Journal of Intelligent & Fuzzy Systems, 40(4), 6529–6539. https://doi.org/10.3233/JIFS-189491CrossRef

Malaviya, P., & Wadhwa, S. (2005). Innovation management in organizational context: An empirical study. Global Journal of Flexible Systems Management, 6(2), 1–14.

Martín, G. A., Fernández-Isabel, A., Martín de Diego, I., & Beltrán, M. (2021). A survey for user behavior analysis based on machine learning techniques: Current models and applications. Applied Intelligence, 51(8), 6029–6055. https://doi.org/10.1007/s10489-020-02160-xCrossRef

Mehta, R., Singh, H., Banerjee, A., Bozhuk, S., & Kozlova, N. (2020). Comparative analysis of the consequences of purchasing models transformation within the global digitalization of the economy. In IOP Conference series: Materials science and engineering (Vol. 940, p. 012071). https://doi.org/10.1088/1757-899X/940/1/012071

Nazarov, A. D. (2020). BIG DATA v marketinge: Trendy i problem [BIG DATA in marketing: Trends and problems]. Economics: Yesterday, Today, Tomorrow, 10(6–1), 169–176.

Prasad, B., & Ghosal, I. (2022). Forecasting buying intention through artificial neural network: An algorithmic solution on direct-to-consumer brands. FIIB Business Review, 11(4), 405–421. https://doi.org/10.1177/23197145211046CrossRef

Ramazanov, I. A., Panasenko, S. V., Cheglov, V. P., Krasil’nikova, E. A., & Nikishin, A. F. (2021). Retail transformation under the influence of digitalisation and technology development in the context of globalisation. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 49. https://doi.org/10.3390/joitmc7010049CrossRef

Shilov, N. (2021). An approach to behavior modeling based on elements of theories of planned and organizational behavior. In 2021 30th conference of open innovations association FRUCT (pp. 146–152) IEEE. https://doi.org/10.23919/FRUCT53335.2021.9599960

Shkileva, A. V. (2021). Perspektivy ispol'zovaniya instrumenta big-data v marketingovykh issledovaniyakh mezhdunarodnykh kompaniy [Prospects for using the big-data tool in marketing research of international companies]. Theory of Law and Interstate Relations, 4(16), 309–315.

Shkor, O. N., & Pogoretskaya, A. D. (2021). BIG DATE v marketinge: Vozmozhnosti, problemy, issledovaniya i tendentsii [BIG DATE in marketing: Opportunities, problems, research and trends]. Big Data and Advanced Analytics, 7–2, 156–159.

Srivastava, S. K., & Bag, S. (2023). Recent developments on flexible manufacturing in the digital era: A review and future research directions. Global Journal of Flexible Systems Management, 24, 483–516. https://doi.org/10.1007/s40171-023-00351-2CrossRef

Timoumi, A., Gangwar, M., & Mantrala, M. K. (2022). Cross-channel effects of omnichannel retail marketing strategies: A review of extant data-driven research. Journal of Retailing, 98(1), 133–151. https://doi.org/10.1016/j.jretai.2022.02.008CrossRef

Van Osselaer, S. M. J. (2008). Associative learning and consumer decisions. In C. P. Haugtvedt, P. M. Herr, & F. R. Kardes (Eds.), Handbook of consumer psychology (pp. 699–729). Taylor & Francis Group/Lawrence Erlbaum Associates.

Vivek, V., Mahesh, T. R., Saravanan, C., & Kumar, K. V. (2022). A novel technique for user decision prediction and assistance using machine learning and NLP: A model to transform the E-commerce system. In Big data management in sensing (pp. 61–76). River Publishers.

Vohra, V., Singh, S., & Dutta, T. (2023). Embracing flexibility post-COVID-19: A systematic review of flexible working arrangements using the SCM-TBFO Framework. Global Journal of Flexible Systems Management. https://doi.org/10.1007/s40171-023-00366-9CrossRef

Wang, J., Xu, C., Zhang, J., & Zhong, R. (2022). Big data analytics for intelligent manufacturing systems: A review. Journal of Manufacturing Systems, 62, 738–752. https://doi.org/10.1016/j.jmsy.2021.03.005CrossRef

Yadav, J., Misra, M., Rana, N. P., & Singh, K. (2022a). Exploring the synergy between nano-influencers and sports community: Behavior mapping through machine learning. Information Technology & People, 35(7), 1829–1854. https://doi.org/10.1108/ITP-03-2021-0219CrossRef

Yadav, J., Misra, M., & Singh, V. (2022b). Sensitizing Netizen’s behavior through influencer intervention enabled by crowdsourcing–a case of reddit. Behaviour & Information Technology, 41(6), 1286–1297. https://doi.org/10.1080/0144929X.2021.1872705CrossRef

Yadav, J., Yadav, A., Misra, M., Rana, N. P., & Zhou, J. (2023). Role of social media in technology adoption for sustainable agriculture practices: Evidence from Twitter analytics. Communications of the Association for Information Systems, 52, 833–851. https://doi.org/10.17705/1CAIS.05240CrossRef

Zaoui, S., Foguem, C., Tchuente, D., Fosso-Wamba, S., & Kamsu-Foguem, B. (2023). The viability of supply chains with interpretable learning systems: The case of COVID-19 vaccine deliveries. Global Journal of Flexible Systems Management, 24, 633–657. https://doi.org/10.1007/s40171-023-00357-wCrossRef

Zhang, C., & Tan, T. (2020). The impact of big data analysis on consumer behavior. Journal of Physics: Conference Series, 1544, 012165. https://doi.org/10.1088/1742-6596/1544/1/012165CrossRef

Zhang, Y., Wang, A., & Hu, W. (2022). Deep learning-based consumer behavior analysis and application research. Wireless Communications and Mobile Computing, 2022, 4268982. https://doi.org/10.1155/2022/4268982CrossRef

Title: Predicting Consumer Behavior Based on Big Data of User-Generated Online Content in Retail Marketing
Author: Gleb Karpushkin
Publication date: 07-02-2024
Publisher: Springer India
Published in: Global Journal of Flexible Systems Management / Issue 1/2024
Print ISSN: 0972-2696
Electronic ISSN: 0974-0198
DOI: https://doi.org/10.1007/s40171-024-00372-5

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