Artificial Intelligence and Digital Marketing Performance: A Systematic Review

Authors

  • Septiandi Putra Universitas Lancang Kuning

DOI:

https://doi.org/10.59261/bustechno.v7i4.822

Keywords:

Artificial Intelligence, Digital Marketing, Online Marketplace, Marketing Performance, Systematic Review

Abstract

Background: The evolution of digital technology has transformed how companies market products and interact with consumers, particularly through digital platforms. Intensifying competition has encouraged businesses to use technologies that support more targeted and measurable marketing activities. Artificial intelligence (AI) is one of the technologies increasingly applied in this area.

Objective: This study synthesizes how AI is utilized in digital marketing, identifies the performance indicators reported in the literature, and examines the factors that enable or constrain its effectiveness, with particular attention to its relevance to online marketplace contexts.

Method: A systematic review was conducted using 17 peer-reviewed articles retrieved from the Scopus database.

Results: The reviewed literature identifies four main forms of AI utilization: prediction and targeting, personalization and user experience, customer relationship management and data integration, and segmentation and strategic capability enhancement. Digital marketing performance is assessed through behavioral and conversion indicators, experience and relational indicators, and market and financial indicators. The literature also identifies data quality, organizational capabilities, and strategic clarity as enabling factors, while algorithmic bias, privacy concerns, limited transparency, and inadequate governance constitute important constraints.

Conclusion: AI has the potential to support improvements in digital marketing performance, but the reported outcomes depend on the implementation context, organizational capabilities, data quality, and governance practices. This review contributes an integrated conceptual mapping of AI utilization, performance indicators, and enabling and constraining factors relevant to digital marketing and online marketplace research.

References

Akter, S., Dwivedi, Y. K., Sajib, S., Biswas, K., Bandara, R. J., & Michael, K. (2022). Algorithmic bias in machine learning-based marketing models. Journal of Business Research, 144, 201–216. https://doi.org/10.1016/j.jbusres.2022.01.083

Chinakidzwa, M., & Phiri, M. (2020). Impact of digital marketing capabilities on market performance of small to medium enterprise agro-processors in Harare, Zimbabwe. Business: Theory and Practice, 21(2), 746–757. https://doi.org/10.3846/btp.2020.12149

Choi, Y., & Choi, J. W. (2023). Assessing the predictive performance of machine learning in direct marketing response. International Journal of E-Business Research, 19(1), 1–12. https://doi.org/10.4018/IJEBR.321458

Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0

Gallego, V., Lingan, J., Freixes, A., Juan, A. A., & Osorio, C. (2024). Applying machine learning in marketing: An analysis using the NMF and k-means algorithms. Information, 15(7), 368. https://doi.org/10.3390/info15070368

Hsu, F. M., Lu, L. P., & Lin, C. M. (2022). Segmenting online donors through data mining techniques. International Journal of Electronic Commerce Studies, 13(2), 33–54.

Huang, D. (2022). Innovative application of big data combined with machine learning in education and training product marketing. Mobile Information Systems, 2022, 9169871. https://doi.org/10.1155/2022/9169871

Kaponis, A., Maragoudakis, M., & Sofianos, K. C. (2025). Enhancing user experiences in digital marketing through machine learning: Cases, trends, and challenges. Computers, 14(6), 211. https://doi.org/10.3390/computers14060211

Krisnanto, A. B., Surachman, S., Rofiaty, R., & Sunaryo, S. (2023). The Role of Marketing and Digital Marketing Capabilities: Entrepreneurial Orientation on the Marketing Performance of Public Enterprises. Revista de Cercetare Si Interventie Sociala, 82. https://doi.org/10.33788/rcis.82.7

Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2019). Understanding the role of artificial intelligence in personalized engagement marketing. California Management Review, 61(4), 135–155. https://doi.org/10.1177/0008125619859317

Labib, E. (2024). Artificial intelligence in marketing: exploring current and future trends. In Cogent Business and Management (Vol. 11, Number 1). https://doi.org/10.1080/23311975.2024.2348728

Miklosik, A., & Evans, N. (2020). Impact of big data and machine learning on digital transformation in marketing: A literature review. IEEE Access, 8, 101284–101292. https://doi.org/10.1109/ACCESS.2020.2998754

Mrad, A. Ben, & Hnich, B. (2024). Intelligent attribution modeling for enhanced digital marketing performance. Intelligent Systems with Applications, 21, 200337. https://doi.org/10.1016/j.iswa.2024.200337

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2022). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Revista Panamericana de Salud Publica/Pan American Journal of Public Health, 46. https://doi.org/10.26633/RPSP.2022.112

Pașcalău, S.-V., Popescu, F.-A., Bîrlădeanu, G.-L., & Gigauri, I. (2024). The effects of a digital marketing orientation on business performance. Sustainability, 16(15), 6685. https://doi.org/10.3390/su16156685

Peng, Z. (2022). New media marketing strategy optimization in the catering industry based on deep machine learning algorithms. Journal of Mathematics, 2022, 5780549. https://doi.org/10.1155/2022/5780549

Saura, J. R. (2021). Using data sciences in digital marketing: Framework, methods, and performance metrics. Journal of Innovation & Knowledge, 6(2), 92–102. https://doi.org/10.1016/j.jik.2020.08.001

Ullal, M. S., Hawaldar, I. T., Soni, R., & Nadeem, M. (2021). The role of machine learning in digital marketing. SAGE Open, 11(4), 21582440211050390. https://doi.org/10.1177/21582440211050394

Volkmar, G., Fischer, P. M., & Reinecke, S. (2022). Artificial intelligence and machine learning: Exploring drivers, barriers, and future developments in marketing management. Journal of Business Research, 149, 599–614. https://doi.org/10.1016/j.jbusres.2022.04.007

Yaiprasert, C., & Hidayanto, A. N. (2023). AI-driven ensemble three machine learning to enhance digital marketing strategies in the food delivery business. Intelligent Systems with Applications, 18, 200235. https://doi.org/10.1016/j.iswa.2023.200235

Yasa, N. N. K., Ekawati, N. W., Rahmayanti, P. L. D., & Tirtayani, I. G. A. (2024). The role of “Tri Hita Karana” business strategy mediates government support and environmental orientation on sustainable business performance. Proceedings of the 8th International Conference on Accounting, Management, and Economics (ICAME 2023), 239–256. https://doi.org/10.2991/978-94-6463-400-6_18

Zaki, K., Alhomaid, A., & Shared, H. (2025). Leveraging machine learning to analyze influencer credibility’s impact on brand admiration and consumer purchase intent in social media marketing. Human Behavior and Emerging Technologies, 2025(1), 9959697. https://doi.org/10.1155/hbe2/9959697

Downloads

Published

2026-10-06