Rise of AI: Transforming Data Analytics in Marketing Strategies
Abstract
This study investigates the transformative impact of AI technology and data analytics on marketing strategies within PT. Lautan Luas Tbk, a leading chemical distribution and manufacturing company. Through a quantitative research design, employing random sampling of 100 consumers and utilizing Smart PLS for analysis, the study examines the relationships between AI Technology Implementation, Data Quality and Availability, Analytical Capabilities, and Marketing Performance. The findings reveal significant direct effects, indicating that both AI Technology Implementation and Data Quality and Availability positively influence Analytical Capabilities and Marketing Performance. Moreover, significant indirect effects highlight the mediating role of Analytical Capabilities in the relationship between AI Technology Implementation / Data Quality and Availability and Marketing Performance. These results underscore the critical importance of adopting advanced data analytics and AI technologies, as well as ensuring data quality and availability, in enhancing marketing performance. The implications suggest that investments in AI technology integration and data management practices are essential for driving effective marketing strategies and achieving sustainable business growth in the competitive landscape of the chemical industry.
References
[2] T. Arumugam, R. Arun, S. Natarajan, K. K. Thoti, P. Shanthi, and U. K. Kommuri, “Unlocking the power of artificial intelligence and machine learning in transforming marketing as we know it,” Data-Driven Intell. Bus. Sustain., pp. 60–74, 2023, doi: 10.4018/979-8-3693-0049-7.ch005.
[3] S. Kaggwa, T. F. Eleogu, F. Okonkwo, O. A. Farayola, P. U. Uwaoma, and A. Akinoso, “AI in Decision Making: Transforming Business Strategies,” Int. J. Res. Sci. Innov., vol. X, no. XII, pp. 423–444, 2024, doi: 10.51244/ijrsi.2023.1012032.
[4] H. Noori Hussain, T. T. Yousif Alabdullah, E. R. Ahmed, and K. A. M. Jamal, “Implementing Technology for Competitive Advantage in Digital Marketing,” Int. J. Sci. Manag. Res., vol. 06, no. 06, pp. 95–114, 2023, doi: 10.37502/ijsmr.2023.6607.
[5] M. Petrescu and A. S. Krishen, “Hybrid intelligence: human–AI collaboration in marketing analytics,” J. Mark. Anal., vol. 11, no. 3, pp. 263–274, 2023, doi: 10.1057/s41270-023-00245-3.
[6] A. Purwanto, A. Sulaiman, and K. Fahmi, “The Role of Buzz and Viral Marketing on SMEs Online Shop Marketing Performance: CB-SEM AMOS Analysis,” Int. J. Soc. Manag. Stud., vol. 4, no. 3, pp. 1–7, 2023.
[7] M. M. Dhiaf, N. Khakan, O. F. Atayah, H. Marashdeh, and R. El Khoury, “The role of FinTech for manufacturing efficiency and financial performance: in the era of industry 4.0,” J. Decis. Syst., vol. 33, no. 2, pp. 220–241, 2024, doi: 10.1080/12460125.2022.2094527.
[8] D. L. T. Anh and C. Gan, “The impact of the COVID-19 lockdown on stock market performance: evidence from Vietnam,” J. Econ. Stud., vol. 48, no. 4, pp. 836–851, 2020, doi: 10.1108/JES-06-2020-0312.
[9] A. R. Munir, J. Maming, N. Kadir, and M. Sobarsyah, “Brand Resonancing Capability: The Mediating Role between Social Media Marketing and Smes Marketing Performance,” Acad. Entrep. J., vol. 27, no. 1, pp. 1–12, 2021.
[10] et al., “The role of ecological innovation and ecological marketing towards green marketing performance improvement,” Manag. Entrep. trends Dev., vol. 1, no. 11, pp. 98–112, 2020, doi: 10.26661/2522-1566/2020-1/11-07.
[11] W. Yang, “Artificial Intelligence education for young children: Why, what, and how in curriculum design and implementation,” Comput. Educ. Artif. Intell., vol. 3, no. January, p. 100061, 2022, doi: 10.1016/j.caeai.2022.100061.
[12] R. Kaplan-Rakowski, K. Grotewold, P. Hartwick, and K. Papin, “Generative AI and Teachers’ Perspectives on Its Implementation in Education,” J. Interact. Learn. Res., vol. 34, no. 2, pp. 313–338, 2023.
[13] B. M. Raparthi, “Real-Time AI Decision Making in IoT with Quantum Computing : Investigating & Exploring the Development and Implementation of Quantum-Supported AI Inference Systems for IoT Applications,” vol. 1, no. 1, pp. 18–27.
[14] T. Talaviya, D. Shah, N. Patel, H. Yagnik, and M. Shah, “Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides,” Artif. Intell. Agric., vol. 4, pp. 58–73, 2020, doi: 10.1016/j.aiia.2020.04.002.
[15] R. R. Nadikattu and U. States, “Implementation of New Ways of,” vol. 14, no. 1001, pp. 5983–5997, 2020, [Online]. Available: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3620017
[16] M. Manni, M. R. Berkeley, M. Seppey, and E. M. Zdobnov, “BUSCO: Assessing Genomic Data Quality and Beyond,” Curr. Protoc., vol. 1, no. 12, pp. 1–41, 2021, doi: 10.1002/cpz1.323.
[17] E. Peer, D. Rothschild, A. Gordon, and E. Damer, “Erratum to Peer et al. (2021) Data quality of platforms and panels for online behavioral research,” Behav. Res. Methods, vol. 54, no. 5, pp. 2618–2620, 2022, doi: 10.3758/s13428-022-01909-1.
[18] R. Abbott et al., “Open data from the first and second observing runs of Advanced LIGO and Advanced Virgo,” SoftwareX, vol. 13, 2021, doi: 10.1016/j.softx.2021.100658.
[19] S. Duggineni, “An Evolutionary Strategy for Leveraging Data Risk-Based Software Development for Data Integrity,” Isaca J., vol. 4, pp. 34–38, 2023, [Online]. Available: https://psyarxiv.com/sutm3/%0Ahttps://psyarxiv.com/sutm3/download?format=pdf
[20] A. S. Albahri et al., “A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion,” Inf. Fusion, vol. 96, pp. 156–191, 2023, doi: 10.1016/j.inffus.2023.03.008.
[21] M. Yasmin, E. Tatoglu, H. S. Kilic, S. Zaim, and D. Delen, “Big data analytics capabilities and firm performance: An integrated MCDM approach,” J. Bus. Res., vol. 114, no. March, pp. 1–15, 2020, doi: 10.1016/j.jbusres.2020.03.028.
[22] S. Akter et al., “Building dynamic service analytics capabilities for the digital marketplace,” J. Bus. Res., vol. 118, pp. 177–188, 2020, doi: 10.1016/j.jbusres.2020.06.016.
[23] A. K. Jha, M. A. N. Agi, and E. W. T. Ngai, “A note on big data analytics capability development in supply chain,” Decis. Support Syst., vol. 138, no. March, p. 113382, 2020, doi: 10.1016/j.dss.2020.113382.
[24] E. Kristoffersen, P. Mikalef, F. Blomsma, and J. Li, “The effects of business analytics capability on circular economy implementation, resource orchestration capability, and firm performance,” Int. J. Prod. Econ., vol. 239, no. April, p. 108205, 2021, doi: 10.1016/j.ijpe.2021.108205.
[25] F. Ciampi, S. Demi, A. Magrini, G. Marzi, and A. Papa, Exploring the impact of big data analytics capabilities on business model innovation: The mediating role of entrepreneurial orientation, vol. 123. 2021. doi: 10.1016/j.jbusres.2020.09.023.
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