Artificial Intelligence for Big Data in Modern Marketing: A Review about Trends, Applications, and Challenges.
Article Sidebar
Main Article Content
Abstract: The rapid digital transformation has triggered an explosion in data generation, with its core impact on the marketing landscape. Big data, with huge volumes, speed, and variety, is thus a significant field of opportunities and challenges for marketers seeking to unravel actionable insights. Traditional approaches to data processing are only inefficient and unable to manage such scale and complexity of data. However, with the advent of AI, quite a few advanced tools can handle big data with greater efficiency, thus enabling better consumer understanding, personalization of marketing strategies, and quick decision-making. It has revolutionized marketing, where systems can now analyze big datasets, recognize patterns, and predict customer behaviors. From descriptive analytics, the shift toward predictive and prescriptive has empowered businesses to optimize campaigns toward enhanced customer experiences. This integration of AI means it can be done instantly, enabling real-time response and fostering more relevant consumer engagement. This review delivers a critical outlook on the current trends in AI, their application to marketing, and the challenges businesses face in implementing these new technologies. Ethical issues around data privacy, transparency, and bias in AI models are discussed. The paper highlights future research directions, including federated learning, quantum computing, and multimodal AI, which hold great promise for even further transformation of the marketing domain.
Downloads
References
the Impact of Machine Learning on Prescriptive Analytics for Optimized Business Decision-Making, Glob. Mainstream J. 1 (2024) 7–18. https://doi.org/10.62304/ijmisds.v1i1.112. DOI: https://doi.org/10.62304/ijmisds.v1i1.112
A. Hemmati, H.M. Arzanagh, A.M. Rahmani, A taxonomy and survey of big data in social media, Concurr. Comput. Pract. Exp. 36 (2024). https://doi.org/10.1002/cpe.7875. DOI: https://doi.org/10.1002/cpe.7875
M. Bilal, Y. Zhang, S. Cai, U. Akram, A. Halibas, Artificial intelligence is the magic wand making customer-centric a reality! An investigation into the relationship between consumer purchase intention and consumer engagement through affective attachment, J. Retail. Consum. Serv. 77 (2024) 103674. https://doi.org/10.1016/j.jretconser.2023.103674. DOI: https://doi.org/10.1016/j.jretconser.2023.103674
M. Taylor, D. Reilly, C. Wren, Internet of things support for marketing activities, J. Strateg. Mark. 28 (2020) 149–160. https://doi.org/10.1080/0965254X.2018.1493523. DOI: https://doi.org/10.1080/0965254X.2018.1493523
S. Tyagi, Machine Learning for Societal Improvement , Modernization , and Progress, (2023). https://doi.org/10.4018/978-1-6684-4045-2.ch011. DOI: https://doi.org/10.4018/978-1-6684-4045-2.ch011
L. Ma, B. Sun, Machine learning and AI in marketing – Connecting computing power to human insights, Int. J. Res. Mark. 37 (2020) 481–504. https://doi.org/10.1016/j.ijresmar.2020.04.005. DOI: https://doi.org/10.1016/j.ijresmar.2020.04.005
S. Verma, R. Sharma, S. Deb, D. Maitra, Artificial intelligence in marketing : Systematic review and future research direction, Int. J. Inf. Manag. Data Insights. (2021) 100002. https://doi.org/10.1016/j.jjimei.2020.100002. DOI: https://doi.org/10.1016/j.jjimei.2020.100002
A.A.R. R., P. R., Deep Learning for Social Media Data Analytics, Springer International Publishing, Cham, 2022. https://doi.org/10.1007/978-3-031-10869-3. DOI: https://doi.org/10.1007/978-3-031-10869-3
David Iyanuoluwa Ajiga, Ndubuisi Leonard Ndubuisi, Onyeka Franca Asuzu, Oluwaseyi Rita Owolabi, Tula Sunday Tubokirifuruar, Rhoda Adura Adeleye, Ai-Driven Predictive Analytics in Retail: a Review of Emerging Trends and Customer Engagement Strategies, Int. J. Manag. Entrep. Res. 6 (2024) 307–321. https://doi.org/10.51594/ijmer.v6i2.772. DOI: https://doi.org/10.51594/ijmer.v6i2.772
A. Jabbar, P. Akhtar, S. Dani, Real-time big data processing for instantaneous marketing decisions : A problematization approach, Ind. Mark. Manag. 90 (2020) 558–569. https://doi.org/10.1016/j.indmarman.2019.09.001. DOI: https://doi.org/10.1016/j.indmarman.2019.09.001
U. Sivarajah, M.M. Kamal, Z. Irani, V. Weerakkody, Critical analysis of Big Data challenges and analytical methods, J. Bus. Res. 70 (2017) 263–286. https://doi.org/10.1016/j.jbusres.2016.08.001. DOI: https://doi.org/10.1016/j.jbusres.2016.08.001
C. González García, E. Álvarez-Fernández, What Is (Not) Big Data Based on Its 7Vs Challenges: A Survey, Big Data Cogn. Comput. 6 (2022) 1–29. https://doi.org/10.3390/bdcc6040158. DOI: https://doi.org/10.3390/bdcc6040158
T.R. Rao, P. Mitra, R. Bhatt, A. Goswami, The big data system, components, tools, and technologies: a survey, Springer London, 2019. https://doi.org/10.1007/s10115-018-1248-0. DOI: https://doi.org/10.1007/s10115-018-1248-0
A.K. Tyagi, R. G, Machine Learning with Big Data, SSRN Electron. J. (2019) 1011–1020. https://doi.org/10.2139/ssrn.3356269. DOI: https://doi.org/10.2139/ssrn.3356269
G. Volkmar, P.M. Fischer, S. Reinecke, Artificial Intelligence and Machine Learning: Exploring drivers, barriers, and future developments in marketing management, J. Bus. Res. 149 (2022) 599–614. https://doi.org/10.1016/j.jbusres.2022.04.007. DOI: https://doi.org/10.1016/j.jbusres.2022.04.007
N. Anute, M. Paliwal, M. Patel, N. Kandale, Impact of artificial intelligence and machine learning on business operations, J. Manag. Res. Anal. 8 (2021) 69–74. https://doi.org/10.18231/j.jmra.2021.015. DOI: https://doi.org/10.18231/j.jmra.2021.015
A. Haleem, M. Javaid, M. Asim Qadri, R. Pratap Singh, R. Suman, Artificial intelligence (AI) applications for marketing: A literature-based study, Int. J. Intell. Networks. 3 (2022) 119–132. https://doi.org/10.1016/j.ijin.2022.08.005. DOI: https://doi.org/10.1016/j.ijin.2022.08.005
V. Kumar, A.R. Ashraf, W. Nadeem, AI-powered marketing: What, where, and how?, Int. J. Inf. Manage. 77 (2024) 102783. https://doi.org/10.1016/j.ijinfomgt.2024.102783. DOI: https://doi.org/10.1016/j.ijinfomgt.2024.102783
V. Kumar, D. Ramachandran, B. Kumar, Influence of new-age technologies on marketing: A research agenda, J. Bus. Res. 125 (2021) 864–877. https://doi.org/10.1016/j.jbusres.2020.01.007. DOI: https://doi.org/10.1016/j.jbusres.2020.01.007
M. Rajyalaxmi, C. Vijai, K. Srivastava, N.B. Kalyan, B. Pravallika, A. Dutt, Application of Machine Learning Algorithms for Customer Segmentation in E-commerce Management, Proc. 2024 Int. Conf. Sci. Technol. Eng. Manag. ICSTEM 2024. (2024) 1–5. https://doi.org/10.1109/ICSTEM61137.2024.10560944. DOI: https://doi.org/10.1109/ICSTEM61137.2024.10560944
V. Duarte, S. Zuniga-Jara, S. Contreras, Machine Learning and Marketing: A Literature Review., SSRN Electron. J. (2022) 1–19. https://doi.org/10.2139/ssrn.4006436. DOI: https://doi.org/10.2139/ssrn.4006436
A. Ravisankar, M. Ponniah, V. Tripathi, K. Kulshreshtha, A.P. Srivastava, V. Singh, Analysis Role of ML and Big Data Play in Driving Digital Marketing’s Paradigm Shift, Proc. - 2023 IEEE World Conf. Appl. Intell. Comput. AIC 2023. 10 (2023) 388–394. https://doi.org/10.1109/AIC57670.2023.10263813. DOI: https://doi.org/10.1109/AIC57670.2023.10263813
C. Fan, M. Chen, X. Wang, J. Wang, B. Huang, A Review on Data Preprocessing Techniques Toward Efficient and Reliable Knowledge Discovery From Building Operational Data, Front. Energy Res. 9 (2021) 1–17. https://doi.org/10.3389/fenrg.2021.652801. DOI: https://doi.org/10.3389/fenrg.2021.652801
P.J. Lou, M. Johnson, Improving disfluency detection by self-training a self-attentive model, Proc. Annu. Meet. Assoc. Comput. Linguist. (2020) 3754–3763. https://doi.org/10.18653/v1/2020.acl-main.346. DOI: https://doi.org/10.18653/v1/2020.acl-main.346
M. TAKEUCHI, T. IMAFUKU, Y. SAKAI, M. GOTO, A Semi-Supervised Learning Model for Predicting User Attributes Based on Ladder Network, Total Qual. Sci. 9 (2023) 109–120. https://doi.org/10.17929/tqs.9.109. DOI: https://doi.org/10.17929/tqs.9.109
L. Murphy, F. Perales, A. Gopal, Y. Gyurdieva, V. Gueorguiev, P. Shandilya, Time for marketing to embrace reinforcement learning, J. Digit. Soc. Media Mark. 10 (2022) 135. https://doi.org/10.69554/MOYX6262. DOI: https://doi.org/10.69554/MOYX6262
N. Cavlak, The Role of Big Data in Digital Marketing, (2021). https://doi.org/10.4018/978-1-7998-8003-5.ch002. DOI: https://doi.org/10.4018/978-1-7998-8003-5.ch002
V.U. Gongane, M. V. Munot, A.D. Anuse, Detection and moderation of detrimental content on social media platforms: current status and future directions, Springer Vienna, 2022. https://doi.org/10.1007/s13278-022-00951-3. DOI: https://doi.org/10.1007/s13278-022-00951-3
A. Acker, A. Kreisberg, Social media data archives in an API-driven world, Arch. Sci. 20 (2020) 105–123. https://doi.org/10.1007/s10502-019-09325-9. DOI: https://doi.org/10.1007/s10502-019-09325-9
J.L. Hayes, B.C. Britt, W. Evans, S.W. Rush, N.A. Towery, A.C. Adamson, Can Social Media Listening Platforms’ Artificial Intelligence Be Trusted? Examining the Accuracy of Crimson Hexagon’s (Now Brandwatch Consumer Research’s) AI-Driven Analyses, J. Advert. 50 (2021) 81–91. https://doi.org/10.1080/00913367.2020.1809576. DOI: https://doi.org/10.1080/00913367.2020.1809576
B. Baykal, Connected Consumers: Unleashing the Power of IoT for Personalized Marketing, Sos. Bilim. Akad. Araştırma ve Değerlendirmeler - IV. (2023). https://doi.org/10.58830/ozgur.pub261.c1432. DOI: https://doi.org/10.58830/ozgur.pub261.c1432
G.G. de Oliveira, F.L. Lizarelli, J.G. Teixeira, G.H. de S. Mendes, Curb your enthusiasm: Examining the customer experience with Alexa and its marketing outcomes, J. Retail. Consum. Serv. 71 (2023) 103220. https://doi.org/10.1016/j.jretconser.2022.103220. DOI: https://doi.org/10.1016/j.jretconser.2022.103220
J. Bzai, F. Alam, A. Dhafer, M. Bojović, S.M. Altowaijri, I.K. Niazi, R. Mehmood, Machine Learning-Enabled Internet of Things (IoT): Data, Applications, and Industry Perspective, Electron. 11 (2022) 1–33. https://doi.org/10.3390/electronics11172676. DOI: https://doi.org/10.3390/electronics11172676
Y. Sun, Y. Bai, Z. Zhou, Collaboration of AI, big data, and blockchain in Internet of Things (IoT): Emerging trends and perspectives, Internet of Things (Netherlands). (2024) 10–13. https://doi.org/10.1016/j.iot.2024.101234. DOI: https://doi.org/10.1016/j.iot.2024.101234
H. Kumar, T. Chinmaya, K. Dehury, P. Kumar, S. Bharadwaj, Predictive Analytics in Cloud, Fog, and Edge Computing, 2023. https://doi.org/10.1007/978-3-031-18034-7. DOI: https://doi.org/10.1007/978-3-031-18034-7
M.K. Kamila, S.S. Jasrotia, Ethics and marketing responsibility: A bibliometric analysis and literature review, Asia Pacific Manag. Rev. 28 (2023) 567–583. https://doi.org/10.1016/j.apmrv.2023.04.002. DOI: https://doi.org/10.1016/j.apmrv.2023.04.002
B. Vlačić, L. Corbo, S. Costa e Silva, M. Dabić, The evolving role of artificial intelligence in marketing: A review and research agenda, J. Bus. Res. 128 (2021) 187–203. https://doi.org/10.1016/j.jbusres.2021.01.055. DOI: https://doi.org/10.1016/j.jbusres.2021.01.055
A.R.E. Alshawawreh, F. Liébana-Cabanillas, F.J. Blanco-Encomienda, Impact of big data analytics on telecom companies’ competitive advantage, Technol. Soc. 76 (2024). https://doi.org/10.1016/j.techsoc.2024.102459. DOI: https://doi.org/10.1016/j.techsoc.2024.102459
C. Ledro, A. Nosella, A. Vinelli, Artificial intelligence in customer relationship management: literature review and future research directions, J. Bus. Ind. Mark. 37 (2022) 48–63. https://doi.org/10.1108/JBIM-07-2021-0332. DOI: https://doi.org/10.1108/JBIM-07-2021-0332
P. Gaczek, G. Leszczyński, A. Mouakher, Collaboration with machines in B2B marketing: Overcoming managers’ aversion to AI-CRM with explainability, Ind. Mark. Manag. 115 (2023) 127–142. https://doi.org/10.1016/j.indmarman.2023.09.007. DOI: https://doi.org/10.1016/j.indmarman.2023.09.007
M.A. Camilleri, The use of data-driven technologies for customer-centric marketing, Int. J. Big Data Manag. 1 (2020) 50. https://doi.org/10.1504/ijbdm.2020.106876. DOI: https://doi.org/10.1504/IJBDM.2020.106876
D. Zumstein, A. Drobnjak, A. Meier, Data privacy in web analytics: An empirical study and declaration model of data collection on websites, WEBIST 2011 - Proc. 7th Int. Conf. Web Inf. Syst. Technol. (2011) 466–472. https://doi.org/10.5220/0003301404660472. DOI: https://doi.org/10.5220/0003301404660472
N. Rane, Enhancing Customer Loyalty through Artificial Intelligence (AI), Internet of Things (IoT), and Big Data Technologies: Improving Customer Satisfaction, Engagement, Relationship, and Experience, SSRN Electron. J. (2023). https://doi.org/10.2139/ssrn.4616051. DOI: https://doi.org/10.2139/ssrn.4616051
M. Singh, Cross-Channel Marketing Analytics: Integrating Offline and Online Data for Holistic Campaign Analysis, Int. J. Enhanc. Res. …. (2024). https://doi.org/10.55948/IJERMCA.2024.0305.
M.A. Khder, Web scraping or web crawling: State of art, techniques, approaches and application, Int. J. Adv. Soft Comput. Its Appl. 13 (2021) 144–168. https://doi.org/10.15849/ijasca.211128.11. DOI: https://doi.org/10.15849/IJASCA.211128.11
F. Villarroel Ordenes, R. Silipo, Machine learning for marketing on the KNIME Hub: The development of a live repository for marketing applications, J. Bus. Res. 137 (2021) 393–410. https://doi.org/10.1016/j.jbusres.2021.08.036. DOI: https://doi.org/10.1016/j.jbusres.2021.08.036
P. Matta, S. Sharma, N. Uniyal, Comparative Study Of Various Scraping Tools: Pros And Cons, 2022 IEEE Delhi Sect. Conf. DELCON 2022. (2022) 1–5. https://doi.org/10.1109/DELCON54057.2022.9753358. DOI: https://doi.org/10.1109/DELCON54057.2022.9753358
V. Truong, Optimizing mobile in-app advertising effectiveness using app publishers-controlled factors, J. Mark. Anal. (2023). https://doi.org/10.1057/s41270-023-00230-w. DOI: https://doi.org/10.1057/s41270-023-00230-w
I. Hidayati, S. Sudarmiatin, A. Hermawan, Analysis of Point of Sale System Implementation in Coffee Shop, J. Pendidik. Dan Kewirausahaan. 11 (2023) 932–947. https://doi.org/10.47668/pkwu.v11i3.881. DOI: https://doi.org/10.47668/pkwu.v11i3.881
C.P. Marques, A.S. Guedes, R. Bento, Tracking changes in tourism demand with point-of-sale data: The case of Portugal, Tour. Hosp. Res. 23 (2023) 101–107. https://doi.org/10.1177/14673584221075175. DOI: https://doi.org/10.1177/14673584221075175
Gold Nmesoma Okorie, Zainab Efe Egieya, Uneku Ikwue, Chioma Ann Udeh, Ejuma Martha Adaga, Obinna Donald DaraOjimba, Osato Itohan Oriekhoe, Leveraging Big Data for Personalized Marketing Campaigns: a Review, Int. J. Manag. Entrep. Res. 6 (2024) 216–242. https://doi.org/10.51594/ijmer.v6i1.778. DOI: https://doi.org/10.51594/ijmer.v6i1.778
Y. Roh, G. Heo, S.E. Whang, A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective, IEEE Trans. Knowl. Data Eng. 33 (2021) 1328–1347. https://doi.org/10.1109/TKDE.2019.2946162. DOI: https://doi.org/10.1109/TKDE.2019.2946162
R. Wolniak, W. Grebski, The application of business analytics in cost reduction, Sci. Pap. Silesian Univ. Technol. Organ. Manag. Ser. 2023 (2023). https://doi.org/10.29119/1641-3466.2023.184.36. DOI: https://doi.org/10.29119/1641-3466.2023.184.36
R. Chaban, O. Taran, J. Tutt, T. Holotyak, S. Bonev, S. Voloshynovskiy, Machine learning attack on copy detection patterns: Are 1× 1 patterns cloneable?, 2021 IEEE Int. Work. Inf. Forensics Secur. WIFS 2021. (2021) 1–6. https://doi.org/10.1109/WIFS53200.2021.9648387. DOI: https://doi.org/10.1109/WIFS53200.2021.9648387
A. Nambiar, D. Mundra, An Overview of Data Warehouse and Data Lake in Modern Enterprise Data Management, Big Data Cogn. Comput. 6 (2022). https://doi.org/10.3390/bdcc6040132. DOI: https://doi.org/10.3390/bdcc6040132
D. Li, W. Zhang, S. Shen, Y. Zhang, SES-LSH: Shuffle-Efficient Locality Sensitive Hashing for Distributed Similarity Search, Proc. - 2017 IEEE 24th Int. Conf. Web Serv. ICWS 2017. (2017) 822–827. https://doi.org/10.1109/ICWS.2017.99. DOI: https://doi.org/10.1109/ICWS.2017.99
G. Chhabra, V. Vashisht, J. Ranjan, A Comparison of Multiple Imputation Methods for Data with Missing Values, Indian J. Sci. Technol. 10 (2017) 1–7. https://doi.org/10.17485/ijst/2017/v10i19/110646. DOI: https://doi.org/10.17485/ijst/2017/v10i19/110646
A. Mumuni, F. Mumuni, Automated data processing and feature engineering for deep learning and big data applications: A survey, J. Inf. Intell. (2024). https://doi.org/10.1016/j.jiixd.2024.01.002. DOI: https://doi.org/10.1016/j.jiixd.2024.01.002
S. Thudumu, P. Branch, J. Jin, J. (Jack) Singh, A comprehensive survey of anomaly detection techniques for high dimensional big data, J. Big Data. 7 (2020). https://doi.org/10.1186/s40537-020-00320-x. DOI: https://doi.org/10.1186/s40537-020-00320-x
G. KARATAŞ BAYDOĞMUŞ, The Effects of Normalization and Standardization an Internet of Things Attack Detection, Eur. J. Sci. Technol. (2021) 187–192. https://doi.org/10.31590/ejosat.1017427. DOI: https://doi.org/10.31590/ejosat.1017427
J.M. John, O. Shobayo, B. Ogunleye, An Exploration of Clustering Algorithms for Customer Segmentation in the UK Retail Market, Analytics. 2 (2023) 809–823. https://doi.org/10.3390/analytics2040042. DOI: https://doi.org/10.3390/analytics2040042
L.H. Bin Ahmad Sabri, A.H.L. Lim, H.N. Goh, Click Analysis: How E-ommerce Companies Benefit from Exploratory and Association Rule Mining, J. Syst. Manag. Sci. 12 (2022) 511–531. https://doi.org/10.33168/JSMS.2022.0630. DOI: https://doi.org/10.33168/JSMS.2022.0630
M.H. Santoso, Application of Association Rule Method Using Apriori Algorithm to Find Sales Patterns Case Study of Indomaret Tanjung Anom, Brill. Res. Artif. Intell. 1 (2021) 54–66. https://doi.org/10.47709/brilliance.v1i2.1228. DOI: https://doi.org/10.47709/brilliance.v1i2.1228
A.M. Zaki, N. Khodadadi, W.H. Lim, S.K. Towfek, Predictive Analytics and Machine Learning in Direct Marketing for Anticipating Bank Term Deposit Subscriptions, Am. J. Bus. Oper. Res. 11 (2024) 79–88. https://doi.org/10.54216/ajbor.110110. DOI: https://doi.org/10.54216/AJBOR.110110
M.C. Iban, S.S. Bilgilioglu, Snow avalanche susceptibility mapping using novel tree-based machine learning algorithms (XGBoost, NGBoost, and LightGBM) with eXplainable Artificial Intelligence (XAI) approach, Stoch. Environ. Res. Risk Assess. 37 (2023) 2243–2270. https://doi.org/10.1007/s00477-023-02392-6. DOI: https://doi.org/10.1007/s00477-023-02392-6
X. Hao, C. Cheng, Telecom Big Data based Precision Marketing Algorithms for Financial Industry, Proc. - 2021 20th Int. Conf. Ubiquitous Comput. Commun. 2021 20th Int. Conf. Comput. Inf. Technol. 2021 4th Int. Conf. Data Sci. Comput. Intell. 20. (2021) 579–585. https://doi.org/10.1109/IUCC-CIT-DSCI-SmartCNS55181.2021.00095. DOI: https://doi.org/10.1109/IUCC-CIT-DSCI-SmartCNS55181.2021.00095
V. Nunavath, M. Goodwin, The Role of Artificial Intelligence in Social Media Big data Analytics for Disaster Management -Initial Results of a Systematic Literature Review, 2018 5th Int. Conf. Inf. Commun. Technol. Disaster Manag. ICT-DM 2018. (2018) 1–4. https://doi.org/10.1109/ICT-DM.2018.8636388. DOI: https://doi.org/10.1109/ICT-DM.2018.8636388
A. Aldoseri, K.N. Al-Khalifa, A.M. Hamouda, Re-Thinking Data Strategy and Integration for Artificial Intelligence: Concepts, Opportunities, and Challenges, Appl. Sci. 13 (2023). https://doi.org/10.3390/app13127082. DOI: https://doi.org/10.20944/preprints202305.1565.v2
M. Rithani, R.P. Kumar, S. Doss, A review on big data based on deep neural network approaches, Springer Netherlands, 2023. https://doi.org/10.1007/s10462-023-10512-5. DOI: https://doi.org/10.1007/s10462-023-10512-5
T. Lin, J. Jiang, Anomaly Detection with Autoencoder and Random Forest, (2020) 96–99. https://doi.org/10.1109/ICS51289.2020.00028. DOI: https://doi.org/10.1109/ICS51289.2020.00028
J. Hartmann, O. Netzer, Natural Language Processing in Marketing, 20 (2023) 191–215. https://doi.org/10.1108/s1548-643520230000020011. DOI: https://doi.org/10.1108/S1548-643520230000020011
A. Jaiswal, S. Tarar, Real-Time Biometric System for Security and Surveillance Using Face Recognition, 2020. https://doi.org/10.1007/978-981-15-6634-9_27. DOI: https://doi.org/10.1007/978-981-15-6634-9_27
F. Nkinahamira, A. Feng, L. Zhang, H. Rong, P. Ndagijimana, D. Guo, B. Cui, H. Zhang, Machine learning approaches for monitoring environmental metal pollutants: Recent advances in source apportionment, detection, quantification, and risk assessment, TrAC Trends Anal. Chem. 180 (2024) 117980. https://doi.org/10.1016/j.trac.2024.117980. DOI: https://doi.org/10.1016/j.trac.2024.117980
U. Sirisha, S.P. Praveen, P.N. Srinivasu, P. Barsocchi, A.K. Bhoi, Statistical Analysis of Design Aspects of Various YOLO-Based Deep Learning Models for Object Detection, Int. J. Comput. Intell. Syst. 16 (2023) 126. https://doi.org/10.1007/s44196-023-00302-w. DOI: https://doi.org/10.1007/s44196-023-00302-w
A.F. Del Carpio, Analyzing computer vision models for detecting customers: practical experience in a mexican retail, Int. J. Adv. Intell. Informatics. 10 (2024) 131–147. https://doi.org/10.26555/ijain.v10i1.1112. DOI: https://doi.org/10.26555/ijain.v10i1.1112
V. Singh, B. Nanavati, A.K. Kar, A. Gupta, How to Maximize Clicks for Display Advertisement in Digital Marketing? A Reinforcement Learning Approach, Inf. Syst. Front. 25 (2023) 1621–1638. https://doi.org/10.1007/s10796-022-10314-0. DOI: https://doi.org/10.1007/s10796-022-10314-0
F. Lu, Adaptive Recommendation System Strategies : An Exploration of Online Machine Learning Algorithms, (2024) 447–451. https://doi.org/10.5220/0012949200004508. DOI: https://doi.org/10.5220/0012949200004508
J. Li, T. Chen, S. Teng, A comprehensive survey on client selection strategies in federated learning, Comput. Networks. 251 (2024). https://doi.org/10.1016/j.comnet.2024.110663. DOI: https://doi.org/10.1016/j.comnet.2024.110663
R. Das, T.D. Singh, Multimodal Sentiment Analysis: A Survey of Methods, Trends, and Challenges, ACM Comput. Surv. 55 (2023). https://doi.org/10.1145/3586075. DOI: https://doi.org/10.1145/3586075
M.S.E. Kasem, M. Hamada, I. Taj-Eddin, Customer profiling, segmentation, and sales prediction using AI in direct marketing, Neural Comput. Appl. 36 (2024) 4995–5005. https://doi.org/10.1007/s00521-023-09339-6. DOI: https://doi.org/10.1007/s00521-023-09339-6
M. Mandapuram, S.S. Gutlapalli, M. Reddy, A. Bodepudi, Application of Artificial Intelligence (AI) Technologies to Accelerate Market Segmentation, Glob. Discl. Econ. Bus. 9 (2020) 141–150. https://doi.org/10.18034/gdeb.v9i2.662. DOI: https://doi.org/10.18034/gdeb.v9i2.662
J. Chen, Z. Liu, X. Huang, C. Wu, Q. Liu, G. Jiang, Y. Pu, Y. Lei, X. Chen, X. Wang, K. Zheng, D. Lian, E. Chen, When large language models meet personalization: perspectives of challenges and opportunities, World Wide Web. 27 (2024) 1–24. https://doi.org/10.1007/s11280-024-01276-1. DOI: https://doi.org/10.1007/s11280-024-01276-1
L. Wu, X. He, X. Wang, K. Zhang, M. Wang, A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation, IEEE Trans. Knowl. Data Eng. 35 (2023) 4425–4445. https://doi.org/10.1109/TKDE.2022.3145690. DOI: https://doi.org/10.1109/TKDE.2022.3145690
H. Ghorbantanhaei, P. Boozary, S. Sheykhan, M. Rabiee, F. Rahmani, I. Hosseini, Results in Control and Optimization Predictive analytics in customer behavior : Anticipating trends and preferences, Results Control Optim. 17 (2024) 100462. https://doi.org/10.1016/j.rico.2024.100462. DOI: https://doi.org/10.1016/j.rico.2024.100462
Y.Y. Raghav, R.K. Tipu, R. Bhakhar, T. Gupta, K. Sharma, The future of digital marketing: Leveraging artificial intelligence for competitive strategies and tactics, Use Artif. Intell. Digit. Mark. Compet. Strateg. Tactics. (2023) 249–274. https://doi.org/10.4018/978-1-6684-9324-3.ch011. DOI: https://doi.org/10.4018/978-1-6684-9324-3.ch011
M. Gigli, F. Stella, Multi-armed bandits for performance marketing, Int. J. Data Sci. Anal. (2024). https://doi.org/10.1007/s41060-023-00493-7. DOI: https://doi.org/10.1007/s41060-023-00493-7
M. Liu, L.I. Jiaxing, Z. Hu, J. Liu, X. Nie, on Model-Free Reinforcement Learning in Display Advertising, (2020) 213587–213601. https://doi.org/10.1109/ACCESS.2020.3037940. DOI: https://doi.org/10.1109/ACCESS.2020.3037940
K.R.P. Vittala, K.M. Kiran, R. Seranmadevi, A.K. Tyagi, Artificial intelligence-internet of things integration for smart marketing: Challenges and opportunities, Adv. Softw. Eng. Through AI, Fed. Learn. Large Lang. Model. (2024) 295–307. https://doi.org/10.4018/979-8-3693-3502-4.ch019. DOI: https://doi.org/10.4018/979-8-3693-3502-4.ch019
Md Sumon Gazi, Md Rokibul Hasan, Nisha Gurung, Anik Mitra, Ethical Considerations in AI-driven Dynamic Pricing in the USA: Balancing Profit Maximization with Consumer Fairness and Transparency, J. Econ. Financ. Account. Stud. 6 (2024) 100–111. https://doi.org/10.32996/jefas.2024.6.2.8. DOI: https://doi.org/10.32996/jefas.2024.6.2.8
Y. Kayikci, S. Demir, S.K. Mangla, N. Subramanian, B. Koc, Data-driven optimal dynamic pricing strategy for reducing perishable food waste at retailers, J. Clean. Prod. 344 (2022) 131068. https://doi.org/10.1016/j.jclepro.2022.131068. DOI: https://doi.org/10.1016/j.jclepro.2022.131068
A. Rese, L. Ganster, D. Baier, Chatbots in retailers’ customer communication: How to measure their acceptance?, J. Retail. Consum. Serv. 56 (2020) 102176. https://doi.org/10.1016/j.jretconser.2020.102176. DOI: https://doi.org/10.1016/j.jretconser.2020.102176
S.Z. Kamoonpuri, A. Sengar, Hi, May AI help you? An analysis of the barriers impeding the implementation and use of artificial intelligence-enabled virtual assistants in retail, J. Retail. Consum. Serv. 72 (2023) 103258. https://doi.org/10.1016/j.jretconser.2023.103258. DOI: https://doi.org/10.1016/j.jretconser.2023.103258
A. Miklosik, N. Evans, A.M.A. Qureshi, The Use of Chatbots in Digital Business Transformation: A Systematic Literature Review, IEEE Access. 9 (2021) 106530–106539. https://doi.org/10.1109/ACCESS.2021.3100885. DOI: https://doi.org/10.1109/ACCESS.2021.3100885
A.P. Chaves, M.A. Gerosa, How should my chatbot interact? A survey on social characteristics in human-chatbot interaction design (preprint), (2020). DOI: https://doi.org/10.1080/10447318.2020.1841438
H.N. DURMUŞ ŞENYAPAR, The Future of Marketing: The Transformative Power of Artificial Intelligence, Int. J. Manag. Adm. 8 (2024) 1–19. https://doi.org/10.29064/ijma.1412272. DOI: https://doi.org/10.29064/ijma.1412272
S. Gupta, T. Justy, S. Kamboj, A. Kumar, E. Kristoffersen, Big data and firm marketing performance: Findings from knowledge-based view, Technol. Forecast. Soc. Change. 171 (2021). https://doi.org/10.1016/j.techfore.2021.120986. DOI: https://doi.org/10.1016/j.techfore.2021.120986
K. Abrokwah-Larbi, Y. Awuku-Larbi, The impact of artificial intelligence in marketing on the performance of business organizations: evidence from SMEs in an emerging economy, J. Entrep. Emerg. Econ. 16 (2024) 1090–1117. https://doi.org/10.1108/JEEE-07-2022-0207. DOI: https://doi.org/10.1108/JEEE-07-2022-0207
D.M. Dave, S. Mandvikar, Augmented Intelligence: Human-Ai Collaboration in the Era of Digital Transformation, Int. J. Eng. Appl. Sci. Technol. 8 (2023) 24–33. https://doi.org/10.33564/ijeast.2023.v08i06.003. DOI: https://doi.org/10.33564/IJEAST.2023.v08i06.003
M. Wedel, P.K. Kannan, Marketing analytics for data-rich environments, J. Mark. 80 (2016) 97–121. https://doi.org/10.1509/jm.15.0413. DOI: https://doi.org/10.1509/jm.15.0413
S. Mukhopadhyay, R.K. Singh, T. Jain, Developing artificial intelligence enabled Marketing 4.0 framework: an Industry 4.0 perspective, Qual. Mark. Res. (2024). https://doi.org/10.1108/QMR-06-2023-0086. DOI: https://doi.org/10.1108/QMR-06-2023-0086
P.K. Kannan, H.A. Li, US CR, Int. J. Res. Mark. (2016). https://doi.org/10.1016/j.ijresmar.2016.11.006. DOI: https://doi.org/10.1016/j.ijresmar.2016.11.006
P. Akhtar, A.M. Ghouri, A. Ashraf, J.J. Lim, N.R. Khan, M.A. Shuang, Smart product platforming powered by AI and generative AI: Personalization for the circular economy, Int. J. Prod. Econ. 273 (2024) 109283. https://doi.org/10.1016/j.ijpe.2024.109283. DOI: https://doi.org/10.1016/j.ijpe.2024.109283
A.T. Rosário, J.C. Dias, How has data-driven marketing evolved: Challenges and opportunities with emerging technologies, Int. J. Inf. Manag. Data Insights. 3 (2023). https://doi.org/10.1016/j.jjimei.2023.100203. DOI: https://doi.org/10.1016/j.jjimei.2023.100203
G. Stalidis, D. Karapistolis, A. Vafeiadis, Marketing Decision Support Using Artificial Intelligence and Knowledge Modeling: Application to Tourist Destination Management, Procedia - Soc. Behav. Sci. 175 (2015) 106–113. https://doi.org/10.1016/j.sbspro.2015.01.1180. DOI: https://doi.org/10.1016/j.sbspro.2015.01.1180
M. Paolanti, E. Frontoni, Multidisciplinary Pattern Recognition applications: A review, Comput. Sci. Rev. 37 (2020) 100276. https://doi.org/10.1016/j.cosrev.2020.100276. DOI: https://doi.org/10.1016/j.cosrev.2020.100276
D.U. Puterisari, Strategic Management in Industry 4.0: Digital Transformation in NIKE Inc. Using the Dynamic Capability Approach, Int. J. Business, Humanit. Educ. Soc. Sci. 4 (2022) 103–108. https://doi.org/10.46923/ijbhes.v4i2.204. DOI: https://doi.org/10.46923/ijbhes.v4i2.204
A. Mathews, How Target Used AI To Become A Retail Giant, AIM Media House LLC. (2024). https://aimresearch.co/market-industry/how-target-used-ai-to-become-a-retail-giant.
GarimaKaneria, Artificial Intelligence in Banking Industry, Impact Artif. Intell. Organ. Transform. (2022) 327–348. https://doi.org/10.1002/9781119710301.ch19. DOI: https://doi.org/10.1002/9781119710301.ch19
S. Punia, S. Shankar, Predictive analytics for demand forecasting: A deep learning-based decision support system, Knowledge-Based Syst. 258 (2022) 109956. https://doi.org/10.1016/j.knosys.2022.109956. DOI: https://doi.org/10.1016/j.knosys.2022.109956
A. Islam, M. Islam, M.U. Hossain Uzir, S. Abd Wahab, A.S. Abdul Latiff, The panorama between COVID-19 pandemic and Artificial Intelligence (AI): Can it be the catalyst for Society 5.0?, Int. J. Sci. Res. Manag. 8 (2020) 2011–2025. https://doi.org/10.18535/ijsrm/v8i12.em02. DOI: https://doi.org/10.18535/ijsrm/v8i12.em02
M. Rodgers, S. Mukherjee, B. Melamed, A. Baveja, A. Kapoor, Solving business problems: the business-driven data-supported process, Ann. Oper. Res. 332 (2024) 705–741. https://doi.org/10.1007/s10479-023-05770-z. DOI: https://doi.org/10.1007/s10479-023-05770-z
S.-Y. Lee, Study on Digital Transformation Strategies: Starbucks Case Study, J. Digit. Contents Soc. 21 (2020) 1809–1816. https://doi.org/10.9728/dcs.2020.21.10.1809. DOI: https://doi.org/10.9728/dcs.2020.21.10.1809
A. Kumari, A. Golyan, R. Shah, N. Raval, Introduction to Data Analytics, Recent Trends Futur. Dir. Data Anal. (2024) 1–14. https://doi.org/10.4018/9798369336090.ch001. DOI: https://doi.org/10.4018/979-8-3693-3609-0.ch001
Md. Aminul Islam, Impact of Big Data Analytics on Digital Marketing: Academic Review, J. Electr. Syst. 20 (2024) 786–820. https://doi.org/10.52783/jes.2327. DOI: https://doi.org/10.52783/jes.2327
P. Bauch, Data Privacy and Regulation, SSRN Electron. J. (2024). https://doi.org/10.2139/ssrn.4723359. DOI: https://doi.org/10.2139/ssrn.4723359
R.Y. Wong, A. Chong, R. Cooper Aspegren, Privacy Legislation as Business Risks: How GDPR and CCPA are Represented in Technology Companies’ Investment Risk Disclosures, Proc. ACM Human-Computer Interact. 7 (2023) 1–26. https://doi.org/10.1145/3579515. DOI: https://doi.org/10.1145/3579515
M.S. De Carvalho, G.L. Da Silva, Inside the black box: Using Explainable AI to improve Evidence-Based Policies, Proc. - 2021 IEEE 23rd Conf. Bus. Informatics, CBI 2021 - Main Pap. 2 (2021) 57–64. https://doi.org/10.1109/CBI52690.2021.10055. DOI: https://doi.org/10.1109/CBI52690.2021.10055
J. Liu, J. Lou, J. Liu, L. Xiong, J. Pei, J. Sun, Dealer: An end-to-end model marketplace with differential privacy, Proc. VLDB Endow. 14 (2021) 957–969. https://doi.org/10.14778/3447689.3447700. DOI: https://doi.org/10.14778/3447689.3447700
T.D. Cao, H.L. Truong, T. Truong-Huu, M.T. Nguyen, Enabling Awareness of Quality of Training and Costs in Federated Machine Learning Marketplaces, Proc. - 2022 IEEE/ACM 15th Int. Conf. Util. Cloud Comput. UCC 2022. (2022) 41–50. https://doi.org/10.1109/UCC56403.2022.00015. DOI: https://doi.org/10.1109/UCC56403.2022.00015
V. Hassija, V. Chamola, A. Mahapatra, A. Singal, D. Goel, K. Huang, S. Scardapane, I. Spinelli, M. Mahmud, A. Hussain, Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence, Cognit. Comput. 16 (2024) 45–74. https://doi.org/10.1007/s12559-023-10179-8. DOI: https://doi.org/10.1007/s12559-023-10179-8
T. Speith, A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods, ACM Int. Conf. Proceeding Ser. (2022) 2239–2250. https://doi.org/10.1145/3531146.3534639. DOI: https://doi.org/10.1145/3531146.3534639
M. Obschonka, D.B. Audretsch, Artificial intelligence and big data in entrepreneurship: a new era has begun, Small Bus. Econ. 55 (2020) 529–539. https://doi.org/10.1007/s11187-019-00202-4. DOI: https://doi.org/10.1007/s11187-019-00202-4
S. Akter, S. Sultana, M. Mariani, S.F. Wamba, K. Spanaki, Y.K. Dwivedi, Advancing algorithmic bias management capabilities in AI-driven marketing analytics research, Ind. Mark. Manag. 114 (2023) 243–261. https://doi.org/10.1016/j.indmarman.2023.08.013. DOI: https://doi.org/10.1016/j.indmarman.2023.08.013
Y.K. Dwivedi, L. Hughes, E. Ismagilova, G. Aarts, C. Coombs, T. Crick, Y. Duan, R. Dwivedi, J. Edwards, A. Eirug, V. Galanos, P.V. Ilavarasan, M. Janssen, P. Jones, A.K. Kar, H. Kizgin, B. Kronemann, B. Lal, B. Lucini, R. Medaglia, K. Le Meunier-FitzHugh, L.C. Le Meunier-FitzHugh, S. Misra, E. Mogaji, S.K. Sharma, J.B. Singh, V. Raghavan, R. Raman, N.P. Rana, S. Samothrakis, J. Spencer, K. Tamilmani, A. Tubadji, P. Walton, M.D. Williams, Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy, Int. J. Inf. Manage. 57 (2021). https://doi.org/10.1016/j.ijinfomgt.2019.08.002. DOI: https://doi.org/10.1016/j.ijinfomgt.2019.08.002
P.P. Angelov, E.A. Soares, R. Jiang, N.I. Arnold, P.M. Atkinson, Explainable artificial intelligence: an analytical review, Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 11 (2021) 1–13. https://doi.org/10.1002/widm.1424. DOI: https://doi.org/10.1002/widm.1424
M. Aledhari, R. Razzak, R.M. Parizi, S. Member, Federated Learning : A Survey on Enabling Technologies , Protocols , and Applications, 8 (2020) 140699–140725. https://doi.org/10.1109/ACCESS.2020.3013541. DOI: https://doi.org/10.1109/ACCESS.2020.3013541
U. Ahmed, G. Srivastava, J.C. Lin, Reliable customer analysis using federated learning and exploring deep-attention edge intelligence, Futur. Gener. Comput. Syst. 127 (2022) 70–79. https://doi.org/10.1016/j.future.2021.08.028. DOI: https://doi.org/10.1016/j.future.2021.08.028
T.M. Khan, A. Robles-Kelly, Machine Learning: Quantum vs Classical, IEEE Access. 8 (2020) 219275–219294. https://doi.org/10.1109/ACCESS.2020.3041719. DOI: https://doi.org/10.1109/ACCESS.2020.3041719
K. Mullangi, N. Dhameliya, S. Kumar, R. Anumandla, V. Krishna, AI-Augmented Decision-Making in Management Using Quantum Networks AI-Augmented Decision-Making in Management Using Quantum Networks, (2023). https://doi.org/10.18034/abr.v13i2.718. DOI: https://doi.org/10.18034/abr.v13i2.718
Y. Gao, H. Liu, Artificial intelligence-enabled personalization in interactive marketing: a customer journey perspective, J. Res. Interact. Mark. 17 (2023) 663–680. https://doi.org/10.1108/JRIM-01-2022-0023. DOI: https://doi.org/10.1108/JRIM-01-2022-0023
P.K. Kopalle, M. Gangwar, A. Kaplan, D. Ramachandran, W. Reinartz, A. Rindfleisch, Examining artificial intelligence (AI) technologies in marketing via a global lens: Current trends and future research opportunities, Int. J. Res. Mark. 39 (2022) 522–540. https://doi.org/10.1016/j.ijresmar.2021.11.002. DOI: https://doi.org/10.1016/j.ijresmar.2021.11.002
M. Wedel, E. Bigné, J. Zhang, Virtual and augmented reality : Advancing research in consumer marketing, Int. J. Res. Mark. 37 (2020) 443–465. https://doi.org/10.1016/j.ijresmar.2020.04.004. DOI: https://doi.org/10.1016/j.ijresmar.2020.04.004
R. Gs, M.S. Lingam, Blockchain Technology In Digital Advertising : Transparency , Fraud Prevention And Trust, 30 (2024) 3041–3049. https://doi.org/10.53555/kuey.v30i4.1477.
A. Khandekar, S. Fahad, Secured IoT architecture for personalized marketing using blockchain framework with deep learning technology, Cluster Comput. 27 (2024) 11069–11084. https://doi.org/10.1007/s10586-024-04372-z. DOI: https://doi.org/10.1007/s10586-024-04372-z
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in our journal are licensed under CC-BY 4.0, which permits authors to retain copyright of their work. This license allows for unrestricted use, sharing, and reproduction of the articles, provided that proper credit is given to the original authors and the source.