Big Data Technology to Improve Marketing Strategy
DOI:
https://doi.org/10.59247/bim.v3i1.331Keywords:
Big Data, Marketing Strategy, SmartPLS, Decision Making , Consumer Analytics, Digital TechnologyAbstract
This study aims to analyze the effect of Big Data technology implementation on the effectiveness of a company's marketing strategy, by considering intermediary variables such as consumer understanding and data-based decision making. A quantitative approach was used in this study by collecting data through a survey of 110 respondents who are marketing managers in various industrial sectors in Indonesia. The data were analyzed using the Structural Equation Modeling (SEM) method based on Partial Least Squares (PLS) through SmartPLS 4.0 software. The results of the study indicate that the implementation of Big Data has a significant positive effect on marketing strategies, both directly and through increasing understanding of consumer behavior. This finding indicates that the integration of Big Data in the marketing process can improve the accuracy of market segmentation, campaign effectiveness, and service personalization. The practical implications of this study encourage companies to adopt data analytics as the main foundation in strategic marketing decision making in the digital era.
References
E. W. Solikhah, I. Fatmawati, R. Widowati, and M. Suyanto, “Factors Influencing Purchase Decisions on Online Sales in Indonesia,” Stud. Syst. Decis. Control, vol. 487, pp. 329–339, 2024, https://doi.org/10.1007/978-3-031-35828-9_28.
M. A. Amanullah et al., “Deep learning and big data technologies for IoT security,” Comput. Commun., vol. 151, pp. 495–517, 2020, https://doi.org/10.1016/j.comcom.2020.01.016.
Y. Duan, J. S. Edwards, and Y. K. Dwivedi, “Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda,” Int. J. Inf. Manage., vol. 48, pp. 63–71, 2019, https://doi.org/10.1016/j.ijinfomgt.2019.01.021.
H. Zhang, S. Lee, Y. Lu, X. Yu, and H. Lu, “A Survey on Big Data Technologies and Their Applications to the Metaverse: Past, Current and Future,” Mathematics, vol. 11, no. 1, 2023, https://doi.org/10.3390/math11010096.
A. Mohamed, M. K. Najafabadi, Y. B. Wah, E. A. K. Zaman, and R. Maskat, “The state of the art and taxonomy of big data analytics: view from new big data framework,” Artif. Intell. Rev., vol. 53, no. 2, pp. 989–1037, 2020, https://doi.org/10.1007/s10462-019-09685-9.
L. I. U. Xiao-Yuan, “Agricultural products intelligent marketing technology innovation in big data era,” Procedia Comput. Sci., vol. 183, pp. 648–654, 2021, https://doi.org/10.1016/j.procs.2021.02.110.
A. Sestino, M. I. Prete, L. Piper, and G. Guido, “Internet of Things and Big Data as enablers for business digitalization strategies,” Technovation, vol. 98, 2020, https://doi.org/10.1016/j.technovation.2020.102173.
C. chong Qi, “Big data management in the mining industry,” Int. J. Miner. Metall. Mater., vol. 27, no. 2, pp. 131–139, 2020, https://doi.org/10.1007/s12613-019-1937-z.
D. Samara, I. Magnisalis, and V. Peristeras, “Artificial intelligence and big data in tourism: a systematic literature review,” J. Hosp. Tour. Technol., vol. 11, no. 2, pp. 343–367, 2020, https://doi.org/10.1108/JHTT-12-2018-0118.
A. K. Sandhu, “Big Data with Cloud Computing: Discussions and Challenges,” Big Data Min. Anal., vol. 5, no. 1, 2022, https://doi.org/10.26599/BDMA.2021.9020016.
M. N. I. Sarker, Y. Peng, C. Yiran, and R. C. Shouse, “Disaster resilience through big data: Way to environmental sustainability,” Int. J. Disaster Risk Reduct., vol. 51, 2020, https://doi.org/10.1016/j.ijdrr.2020.101769.
Z. Lv and L. Qiao, “Analysis of healthcare big data,” Futur. Gener. Comput. Syst., vol. 109, pp. 103–110, 2020, https://doi.org/10.1016/j.future.2020.03.039.
A. Hamdan, “Artificial intelligence and transforming digital marketing,” p. 1188, 2024, https://doi.org/10.1007/978-3-031-35828-9.
S. Teja and B. Sr, “Big Data Meets Machine Learning: Strategies for Efficient Data Processing and Analysis in Large Datasets,” Int. J. Creat. Res. Comput. Technol. Des., vol. 2, no. 2, 2020, [Online]. Available: https://jrctd.in/index.php/IJRCTD/article/view/68.
M. M. Hasan, J. Popp, and J. Oláh, “Current landscape and influence of big data on finance,” J. Big Data, vol. 7, no. 1, 2020, https://doi.org/10.1186/s40537-020-00291-z.
S. J. Miah, M. Miah, and J. Shen, “Editorial note: Learning management systems and big data technologies for higher education,” Educ. Inf. Technol., vol. 25, no. 2, pp. 725–730, 2020, https://doi.org/10.1007/s10639-020-10129-z.
C. Odedina, “Impact of Big Data on Marketing Strategy and Consumer Behavior Analysis in the Us,” SSRN Electron. J., 2023, https://doi.org/10.2139/ssrn.4520361.
L. T. Khrais, “Role of artificial intelligence in shaping consumer demand in e-commerce,” Futur. Internet, vol. 12, no. 12, pp. 1–14, 2020, https://doi.org/10.3390/fi12120226.
PP Kuantitatif, “Metode penelitian kunatitatif kualitatif dan R&D,” Alfabeta, 2016, [Online]. Available: https://www.researchgate.net/publication/377469385_METODE_PENELITIAN_KUANTITATIF_KUALITATIF_DAN_RD.
A. Muri Yusuf, Metode Penelitian Kuantitatif, Kualitatif & Penelitian Gabungan. Prenada Media, 2016. [Online]. Available: https://books.google.co.id/books?id=RnA-DwAAQBAJ&dq=Metodologi+Penelitian+Kualitatif,+Kuantitatif+dan+Penelitian+Gabungan.&lr=&hl=id&source=gbs_navlinks_s.
Siswoyo Haryono, “Metode SEM Untuk Penelitian Manajemen dengan AMOS LISREL Smart PLS,” J. Phys. A Math. Theor., p. 450, 2016, [Online]. Available: https://text-id.123dok.com/document/ozl4x92y-metode-sem-untuk-penelitian-manajemen-dengan-amos-lisrel-pls.html.
I. Ghozali, Model persamaan struktural: Konsep dan aplikasi dengan program AMOS 16.0. Badan Penerbit Universitas Diponegoro, 2008.
I. Ghozali, Structural Equation Modeling Metode Alternatif Dengan Partial Least Squares (PLS). Universitas Diponegoro Semarang. 2014. [Online]. Available: https://repository.telkomuniversity.ac.id/pustaka/107442/structural-equation-modeling-metode-alternatif-dengan-partial-least-squares-pls-.html.
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