Home / Current Issue / Paper 1709051
Designing Hyper-Personalized Digital Marketing Frameworks Using AI-Based Segmentation Techniques
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
The integration of Artificial Intelligence (AI) into digital marketing practices has led to unprecedented opportunities for personalization, offering marketers the capability to target consumers with high precision and relevance. This paper proposes a novel framework for designing hyper-personalized digital marketing strategies by leveraging AI-based segmentation techniques. Through a systematic synthesis of existing literature and application of machine learning and deep learning algorithms, the study outlines how AI can enhance customer segmentation beyond traditional demographic and behavioral markers. The methodology includes the implementation of unsupervised learning models, natural language processing, and neural network-based clustering to develop dynamically evolving customer segments. The results reveal substantial improvements in engagement metrics, conversion rates, and customer retention, validating the efficacy of AI-driven segmentation. The paper concludes with practical implications for digital marketing practitioners and recommendations for future research on ethical AI deployment in personalization.
Keywords
AI segmentation, hyper-personalization, digital marketing, customer targeting, machine learning, data-driven strategy
References
[1] O. E. Akpe, J. C. Ogeawuchi, A. A. Abayomi, O. A. Agboola, and E. Ogbuefi, “A Conceptual Framework for Strategic Business Planning in Digitally Transformed Organizations,” Iconic Res. Eng. J., vol. 5, no. 4, pp. 310–325, Oct. 2021.
[2] S. Chakraborty, M. S. Hoque, and S. M. Surid, “A COMPREHENSIVE REVIEW ON IMAGE BASED STYLE PREDICTION AND ONLINE FASHION RECOMMENDATION.,” J. Mod. Technol. Eng., vol. 5, no. 3, 2020, [Online]. Available: https://www.researchgate.net/profile/Samit- Chakraborty/publication/347936740_A_COM PREHENSIVE_REVIEW_ON_IMAGE_BAS ED_STYLE_PREDICTION_AND_ONLINE _FASHION_RECOMMENDATION/links/5fe a606e299bf14088563ef3/A- COMPREHENSIVE-REVIEW-ON-IMAGE- BASED-STYLE-PREDICTION-AND- ONLINE-FASHION- RECOMMENDATION.pdf
[3] P. I. Egbumokei, I. N. Dienagha, W. N. Digitemie, and E. C. Onukwulu, “Advanced pipeline leak detection technologies for enhancing safety and environmental sustainability in energy operations,” Int. J. Sci. Res. Arch., vol. 4, no. 1, pp. 222–228, 2021.
[4] V. Patil, T. Padale, G. Waghmare, and D. Kulkarni, “A study on understanding future of Artificial Intelligence in the various functions of marketing.,” Turk. Online J. Qual. Inq., vol. 12, no. 6, 2021, [Online]. Available: https://www.researchgate.net/profile/Ganesh- Waghmare- 2/publication/360889528_A_study_on_unders tanding_future_of_Artificial_Intelligence_in_t he_various_functions_of_marketing/links/629 07a408d19206823dec507/A-study-on- understanding-future-of-Artificial- Intelligence-in-the-various-functions-of- marketing.pdf
[5] T. P. Gbenle, J. C. Ogeawuchi, A. A. Abayomi, O. A. Agboola, and A. C. Uzoka, “Advances in Cloud Infrastructure Deployment Using AWS Services for Small and Medium Enterprises,” Iconic Res. Eng. J., vol. 3, no. 11, pp. 365–381, May 2020.
[6] S. Singh, Adoption and implementation of AI in customer relationship management. IGI Global, 2021. [Online]. Available: https://books.google.com/books?hl=en&lr=&i d=UuREEAAAQBAJ&oi=fnd&pg=PP1&dq= AI+segmentation,+hyper- personalization,+digital+marketing,+customer +targeting,+machine+learning,+data- driven+strategy&ots=itukBkQ6Q6&sig=wD6 hOxBUvSGd24rvRq-yO967NAY
[7] O. E. Akpe, J. C. Ogeawuchi, A. A. Abayomi, and O. A. Agboola, “Advances in Stakeholder- Centric Product Lifecycle Management for Complex, Multi-Stakeholder Energy Program Ecosystems,” Iconic Res. Eng. J., vol. 4, no. 8, pp. 179–188, Feb. 2021.
[8] D. Anny, “AI-Driven Innovations in Sales and Marketing,” 2018, [Online]. Available: https://www.researchgate.net/profile/Dave- Anny/publication/389648890_AI- Driven_Innovations_in_Sales_and_Marketing /links/67cb159c32265243f58366a7/AI- Driven-Innovations-in-Sales-and- Marketing.pdf
[9] A. A. Abayomi, A. C. Mgbame, O. E. Akpe, E. Ogbuefi, and O. O. Adeyelu, “Advancing Equity Through Technology: Inclusive Design of BI Platforms for Small Businesses,” Iconic Res. Eng. J., vol. 5, no. 4, pp. 235–250, Oct. 2021.
[10] J. Prosper, “AI-Driven Innovations in Sales and Marketing,” 2018, [Online]. Available: https://www.researchgate.net/profile/James- Prosper-2/publication/389650408_AI- Driven_Innovations_in_Sales_and_Marketing /links/67cb278de62c604a0dd61a4c/AI- Driven-Innovations-in-Sales-and- Marketing.pdf
[11] O. M. Oluoha, A. Odeshina, O. Reis, F. Okpeke, V. Attipoe, and O. H. Orieno, “Development of a Compliance-Driven Identity Governance Model for Enhancing Enterprise Information Security,” Iconic Res. Eng. J., vol. 4, no. 11, pp. 310–324, May 2021.
[12] J. Prosper, “AI-Powered Enterprise Architectures for Omni-Channel Sales: Enhancing Scalability, Security, and Performance,” 2018, [Online]. Available: https://www.researchgate.net/profile/James- Prosper-2/publication/389735586_AI- Powered_Enterprise_Architectures_for_Omni - _Channel_Sales_Enhancing_Scalability_Secu rity_and_Performance/links/67d01217d75970 006507ab16/AI-Powered-Enterprise- Architectures-for-Omni-Channel-Sales- Enhancing-Scalability-Security-and- Performance.pdf
[13] OLUWATOSIN ILORI, COMFORT IYABODE LAWAL, SOLOMON CHRISTOPHER FRIDAY, NGOZI JOAN ISIBOR, EZINNE C CHUKWUMA -EKE, “Blockchain-Based Assurance Systems: Opportunities and Limitations in Modern Audit Engagements.” [Online]. Available: https://scholar.google.com/citations?view_op= view_citation&hl=en&user=sJAYP0YAAAA J&cstart=20&pagesize=80&citation_for_view =sJAYP0YAAAAJ:kNdYIx-mwKoC
[14] H. Agoro, S. Templer, and M. Maddog, “AI- Powered Personalization: Enhancing User Experience through Customized Shopping Recommendations,” 2021, [Online]. Available: https://www.researchgate.net/profile/Habeeb_ Agoro/publication/389211538_AI- Powered_Personalization_Enhancing_User_E xperience_through_Customized_Shopping_Re commendations/links/67b8f6cd461fb56424e4f ea6/AI-Powered-Personalization-Enhancing- User-Experience-through-Customized- Shopping-Recommendations.pdf
[15] A. C. Mgbame, O. E. Akpe, A. A. Abayomi, E. Ogbuefi, and O. O. Adeyelu, “Barriers and Enablers of BI Tool Implementation in Underserved SME Communities,” Iconic Res. Eng. J., vol. 3, no. 7, pp. 211–226, Jan. 2020.
[16] B. Rathore, “Allure of style: The impact of contemporary fashion marketing on consumer behaviour,” nature, vol. 9, p. 11, 2018.
[17] G. Fredson, B. Adebisi, O. B. Ayorinde, E. C. Onukwulu, O. Adediwin, and A. O. Ihechere, “Driving organizational transformation: Leadership in ERP implementation and lessons from the oil and gas sector,” Int J Multidiscip Res Growth Eval Internet, 2021, [Online]. Available: https://scholar.google.com/scholar?cluster=10 240535623829030426&hl=en&oi=scholarr
[18] OLUWATOSIN ILORI, , COMFORT IYABODE LAWAL, , SOLOMON CHRISTOPHER FRIDAY, , NGOZI JOAN ISIBOR, and , EZINNE C. CHUKWUMA - EKE, “Enhancing Auditor Judgment and Skepticism through Behavioral Insights: A Systematic Review.” [Online]. Available: https://www.researchgate.net/profile/Oluwatos in-Ilori- 3/publication/391739345_Enhancing_Auditor _Judgment_and_Skepticism_through_Behavio ral_Insights_A_Systematic_Review/links/682 5167a026fee1034f82ffc/Enhancing-Auditor- Judgment-and-Skepticism-through- Behavioral-Insights-A-Systematic-Review.pdf
[19] Ekene Cynthia Onukwulu, Mercy Odochi Agho, and Nsisong Louis Eyo -Udo, “Framework for sustainable supply chain practices to reduce carbon footprint in energy,” Open Access Res. J. Sci. Technol., vol. 1, no. 2, pp. 012–034, Jul. 2021, 10.53022/oarjst.2021.1.2.0032.
[20] P. Chojecki, Artificial Intelligence Business: How you can profit from AI. Przemek Chojecki, 2020. [Online]. Available: https://books.google.com/books?hl=en&lr=&i d=sRbxDwAAQBAJ&oi=fnd&pg=PA5&dq= AI+segmentation,+hyper- personalization,+digital+marketing,+customer +targeting,+machine+learning,+data- driven+strategy&ots=JtdRgGY4fp&sig=- EtiBezVOvEOTKmN3gthLNNzkmM
[21] P. Chima, J. Ahmadu, and O. G. Folorunsho, “Implementation of Digital Integrated Personnel and Payroll Information System: Lesson from Kenya, Ghana and Nigeria,” vol. 4, no. 2, 2019.
[22] L. Cao, “Artificial intelligence in retail: applications and value creation logics,” Int. J. Retail Distrib. Manag., vol. 49, no. 7, pp. 958– 976, 2021.
[23] Adesemoye O.E., Chukwuma-Eke E.C., Lawal C.I., Isibor N.J., Akintobi A.O., Ezeh F.S., “Improving Financial Forecasting Accuracy through Advanced Data Visualization Techniques.” [Online]. Available: https://scholar.google.com/citations?view_op= view_citation&hl=en&user=Zm0csPMAAAA J&authuser=1&citation_for_view=Zm0csPM AAAAJ:Se3iqnhoufwC
[24] J. Olatunde Omisola, E. Augustine Etukudoh, O. Kingsley Okenwa, G. I. Tokunbo Olugbemi, and E. Ogu, “Innovating Project Delivery and Piping Design for Sustainability in the Oil and Gas Industry: A Conceptual Framework,” Int. J. Adv. Multidiscip. Res. Stud., vol. 4, no. 6, pp. 1772–1777, Dec. 2024, 10.62225/2583049X.2024.4.6.4109.
[25] L. Chitra, Artificial intelligence meets augmented reality. BPB Publications, 2019. [Online]. Available: https://books.google.com/books?hl=en&lr=&i d=vb5IEAAAQBAJ&oi=fnd&pg=PT16&dq= AI+segmentation,+hyper- personalization,+digital+marketing,+customer +targeting,+machine+learning,+data- driven+strategy&ots=h_U72rzbVb&sig=CbY HcEdC63f3cCrOPfLirl7dDNM
[26] J. C. Ogeawuchi, A. C. Uzoka, A. A. Abayomi, O. A. Agboola, T. P. Gbenle, and O. O. Ajayi, “Innovations in Data Modeling and Transformation for Scalable Business Intelligence on Modern Cloud Platforms,” Iconic Res. Eng. J., vol. 5, no. 5, pp. 406–415, Nov. 2021.
[27] Oluchukwu Modesta Oluoha, Abisola Odeshina, Oluwatosin Reis, and Friday Okpeke, “Project Management Innovations for Strengthening Cybersecurity Compliance across Complex Enterprises | Request PDF,” ResearchGate, Apr. 2025, 10.54660/.IJMRGE.2021.2.1.871-881.
[28] B. Rathore, “Exploring the intersection of fashion marketing in the metaverse: leveraging artificial intelligence for consumer engagement and brand innovation,” Int. J. New Media Stud. Int. Peer Rev. Sch. Index. J., vol. 4, no. 2, pp. 51–60, 2017.
[29] G. Fredson, B. Adebisi, O. B. Ayorinde, E. C. Onukwulu, O. Adediwin, and A. O. Ihechere, “Revolutionizing procurement management in the oil and gas industry: Innovative strategies and insights from high-value projects,” Int J Multidiscip Res Growth Eval Internet, 2021, [Online]. Available: https://scholar.google.com/scholar?cluster=36 97942392653502481&hl=en&oi=scholarr
[30] G. K. Agarwal, M. Magnusson, and A. Johanson, “Edge AI Driven Technology Advancements Paving Way Towards New Capabilities,” Int. J. Innov. Technol. Manag., vol. 18, no. 01, p. 2040005, Feb. 2021, 10.1142/S0219877020400052.
[31] A. I. Daraojimba, J. C. Ogeawuchi, A. A. Abayomi, O. A. Agboola, and E. Ogbuefi, “Systematic Review of Serverless Architectures and Business Process Optimization,” Iconic Res. Eng. J., vol. 5, no. 4, pp. 284–309, Oct. 2021.
[32] T. K. Vashishth, V. Sharma, K. K. Sharma, B. Kumar, S. Chaudhary, and R. Panwar, “Embracing AI and Machine Learning for the Future of Digital Marketing,” in AI, Blockchain, and Metaverse in Hospitality and Tourism Industry 4.0, Chapman and Hall/CRC, pp. 90–117. [Online]. Available: https://www.taylorfrancis.com/chapters/edit/1 0.1201/9781032706474-7/embracing-ai- machine-learning-future-digital-marketing- tarun-kumar-vashishth-vikas-sharma-kewal- krishan-sharma-bhupendra-kumar-sachin- chaudhary-rajneesh-panwar
[33] O. E. Akpe, J. C. Ogeawuchi, A. A. Abayomi, O. A. Agboola, and E. Ogbuefi, “Systematic Review of Last-Mile Delivery Optimization and Procurement Efficiency in African Logistics Ecosystems,” Iconic Res. Eng. J., vol. 5, no. 6, pp. 377–388, Dec. 2021.
[34] J. Prosper, “Deploying Scalable Deep Learning Models for Real-Time Customer Insight,” 2019, [Online]. Available: https://www.researchgate.net/profile/James- Prosper- 2/publication/389516189_Deploying_Scalable _Deep_Learning_Models_for_Real- _Time_Customer_Insight_AUTHOR_James_ Prosper/links/67c61843645ef274a49a2982/De ploying-Scalable-Deep-Learning-Models-for- Real-Time-Customer-Insight-AUTHOR- James-Prosper.pdf
[35] O. E. Akpe, J. C. Ogeawuchi, A. A. Abayomi, O. A. Agboola, and E. Ogbuefi, “Systematic Review of Last-Mile Delivery Optimization and Procurement Efficiency in African Logistics Ecosystems,” Iconic Res. Eng. J., vol. 5, no. 6, pp. 377–388, Dec. 2021.
[36] O. H. Olayinka, “Data driven customer segmentation and personalization strategies in modern business intelligence frameworks,” World J. Adv. Res. Rev., vol. 12, no. 3, pp. 711– 726, 2021.
[37] B. I. Adekunle, E. C. Chukwuma-Eke, E. D. Balogun, and K. O. Ogunsola, “Predictive Analytics for Demand Forecasting: Enhancing Business Resource Allocation Through Time Series Models,” J. Front. Multidiscip. Res., vol. 2, no. 1, pp. 32 –42, 2021, 10.54660/.IJFMR.2021.2.1.32-42.
[38] S. Ahmed and S. Miskon, “IoT Driven Resiliency with Artificial Intelligence, Machine Learning and Analytics for Digital Transformation,” in 2020 International Conference on Decision Aid Sciences and Application (DASA), Nov. 2020, pp. 1205– 1208. do i: 10.1109/DASA51403.2020.9317177.
[39] K. Borne, “Customer Experience Management,” in Demystifying AI for the Enterprise, Productivity Press, 2021, pp. 121– 148. [Online]. Available: https://www.taylorfrancis.com/chapters/edit/1 0.4324/9781351032940-5/customer- experience-management-kirk-borne
[40] K. O. Ogunsola and E. D. Balogun, “Enhancing Financial Integrity Through an Advanced Internal Audit Risk Assessment and Governance Model,” Int. J. Multidiscip. Res. Growth Eval., vol. 2, no. 1, pp. 781–790, 2021,
[41] M. Kihn and C. B. O’Hara, Customer data platforms: Use people data to transform the future of marketing engagement. John Wiley & Sons, 2020. [Online]. Available: https://books.google.com/books?hl=en&lr=&i d=odUGEAAAQBAJ&oi=fnd&pg=PA1&dq= AI+segmentation,+hyper- personalization,+digital+marketing,+customer +targeting,+machine+learning,+data- driven+strategy&ots=YDl21OU6pP&sig=u- kBEH2tT3h-x7_U4LredwKAmGg
[42] A. Malik, M. T. T. De Silva, P. Budhwar, and N. R. Srikanth, “Elevating talents’ experience through innovative artificial intelligence- mediated knowledge sharing: Evidence from an IT-multinational enterprise,” J. Int. Manag., vol. 27, no. 4, p. 100871, Dec. 2021, 10.1016/j.intman.2021.100871.
[43] B. I. Adekunle, E. C. Chukwuma-Eke, E. D. Balogun, and K. O. Ogunsola, “A Predictive Modeling Approach to Optimizing Business Operations: A Case Study on Reducing Operational Inefficiencies through Machine Learning,” Int. J. Multidiscip. Res. Growth Eval., vol. 2, no. 1, pp. 791–799, 2021, 10.54660/.IJMRGE.2021.2.1.791-799.
[44] A. I. How, “Connect the dots,” 2003, [Online]. Available: https://www.personalispossible.com/retail/ass ets/how-ai-is-powering-the-future-of-retail.pdf
[45] Enoch Oluwadunmininu Ogunnowo, Musa Adekunle Adewoyin, Joyce Efekpogua Fiemotongha, Thompson Odion Igunma, Adeniyi K Adeleke, “Systematic Review of Non-Destructive Testing Methods for Preventive Failure Analysis in Mechanical Systems.” [Online]. Available: https://scholar.google.com/citations?view_op= view_citation&hl=en&user=6SQ3ZwQAAAA J&citation_for_view=6SQ3ZwQAAAAJ:Tyk- 4Ss8FVUC
[46] S. Jain, “BIG DATA MANAGEMENT USING ARTIFICIAL IN℡LIGENCE IN THE APPAREL SUPPLY CHAIN”, [Online]. Available: https://www.diva - portal.org/smash/get/diva2:1466445/INSIDE0 1.pdf
[47] G. Agho, M. O., Ezeh, M., Isong, D., Iwe, K. A., and Oluseyi, “Sustainable Pore Pressure Prediction and its Impact on Geo-mechanical Modelling for Enhanced Drilling Operations.” [Online]. Available: https://scholar.google.com/citations?view_op= view_citation&hl=en&user=COMxOPwAAA AJ&citation_for_view=COMxOPwAAAAJ:Y 0pCki6q_DkC
[48] David Ajiga, “Strategic Framework for Leveraging Artificial Intelligence to Improve Financial Reporting Accuracy and Restore Public Trust.” [Online]. Available: https://scholar.google.com/citations?view_op= view_citation&hl=en&user=zC5wizQAAAAJ &citation_for_view=zC5wizQAAAAJ:hqOjcs 7Dif8C
[49] B. Libai et al., “Brave New World? On AI and the Management of Customer Relationships,” J. Interact. Mark., vol. 51, no. 1, pp. 44–56, Aug. 2020,
[50] D. I. Ajiga, “Strategic Framework for Leveraging Artificial Intelligence to Improve Financial Reporting Accuracy and Restore Public Trust,” Int. J. Multidiscip. Res. Growth Eval., vol. 2, no. 1, pp. 882–892, 2021, 10.54660/.IJMRGE.2021.2.1.882-892.
[51] G. Bruno, “How artificial intelligence is affecting marketing and human behaviour: Filoblu case study,” 2021, [Online]. Available: https://thesis.unipd.it/bitstream/20.500.12608/ 21468/1/Bruno_Gianluca.pdf
[52] F. U. Ojika, W. O. Owobu, O. A. Abieba, O. J. Esan, B. C. Ubamadu, and A. Ifesinachi, “Optimizing AI Models for Cross-Functional Collaboration: A Framework for Improving Product Roadmap Execution in Agile Teams,” vol. 5, no. 1, 2021.
[53] C. I. Okolie, O. Hamza, A. Eweje, A. Collins, G. O. Babatunde, and B. C. Ubamadu, “Leveraging digital transformation and business analysis to improve healthcare provider portal,” Iconic Res. Eng. J., vol. 4, no. 10, pp. 253–257, 2021.
[54] D. R. Grzelska, “How is Artificial Intelligence redefining modern international marketing?,” 2021, [Online]. Available: https://osuva.uwasa.fi/handle/10024/12767
[55] O. E. Adesemoye, E. C. Chukwuma-Eke, C. I. Lawal, N. J. Isibor, A. O. Akintobi, and F. S. Ezeh, “Integrating Digital Currencies into Traditional Banking to Streamline Transactions and Compliance”.
[56] S. Mercan et al., “Improving the service industry with hyper-connectivity: IoT in hospitality,” Int. J. Contemp. Hosp. Manag., vol. 33, no. 1, pp. 243–262, 2021.
[57] P. E. Odio, E. Kokogho, T. A. Olorunfemi, M. O. Nwaozomudoh, I. E. Adeniji, and A. Sobowale, “Innovative Financial Solutions: A Conceptual Framework for Expanding SME Portfolios in Nigeria’s Banking Sector,” Int. J. Multidiscip. Res. Growth Eval., vol. 2, no. 1, pp. 495 –507, 2021, 10.54660/.IJMRGE.2021.2.1.495-507.
[58] D. Anny, “Integrating AI into ERP Systems: A Framework for Enhancing Sales and Customer Insights,” 2015, [Online]. Available: https://www.researchgate.net/profile/Dave- Anny/publication/389516319_Integrating_AI_ into_ERP_Systems_A_Framework_for_Enha ncing_Sales_and_Customer_Insights/links/67 c60d978311ce680c7b8ac8/Integrating-AI- into-ERP-Systems-A-Framework-for- Enhancing-Sales-and-Customer-Insights.pdf
[59] Adesemoye O.E., Chukwuma-Eke E.C., Lawal C.I., Isibor N.J., Akintobi A.O., Ezeh F.S., “Improving Financial Forecasting Accuracy through Advanced Data Visualization Techniques.” [Online]. Available: https://scholar.google.com/citations?view_op= view_citation&hl=en&user=Zm0csPMAAAA J&authuser=1&citation_for_view=Zm0csPM AAAAJ:Se3iqnhoufwC
[60] D. Anny, “Integrating AI -Driven Personalization in Enterprise Marketing Applications,” 2013, [Online]. Available: https://www.researchgate.net/profile/Dave- Anny/publication/389515695_Integrating_AI- Driven_Personalization_in_Enterprise_Market ing_Applications/links/67c60ccdf5cb8f70d5c 67dfa/Integrating-AI-Driven-Personalization- in-Enterprise-Marketing-Applications.pdf
[61] Kolade Olusola Ogunsola 1*, , Emmanuel Damilare Balogun 2, and , Adebanji Samuel Ogunmokun 3, “Enhancing Financial Integrity Through an Advanced Internal Audit Risk Assessment and Governance Model.” [Online]. Available: https://www.allmultidisciplinaryjournal.com/u ploads/archives/20250329154826_MGE- 2025-2-141.1.pdf
[62] ENOCH OLUWABUSAYO ALONGE1 et al., “Digital Transformation in Retail Banking to Enhance Customer Experience and Profitability.” [Online]. Available: https://www.researchgate.net/profile/Enoch- Alonge/publication/390023729_Digital_Trans formation_in_Retail_Banking_to_Enhance_C ustomer_Experience_and_Profitability/links/6 7dc385772f7f37c3e750efa/Digital- Transformation-in-Retail-Banking-to- Enhance-Customer-Experience-and- Profitability.pdf
[63] J. P. Onoja, O. Hamza, A. Collins, U. B. Chibunna, A. Eweja, and A. I. Daraojimba, “Digital Transformation and Data Governance: Strategies for Regulatory Compliance and Secure AI-Driven Business Operations,” J. Front. Multidiscip. Res., vol. 2, no. 1, pp. 43– 55, 2021, 55.
[64] M. O. Nwaozomudoh, P. E. Odio, E. Kokogho, T. A. Olorunfemi, I. E. Adeniji, and A. Sobowale, “Developing a Conceptual Framework for Enhancing Interbank Currency Operation Accuracy in Nigeria’s Banking Sector,” Int. J. Multidiscip. Res. Growth Eval., vol. 2, no. 1, pp. 481–494, 2021, 10.54660/.IJMRGE.2021.2.1.481-494.
[65] A. I. Daraojimba and E. D. Balogun, “118 PUBLICATIONS 6,307 CITATIONS SEE PROFILE,” vol. 4, no. 9, 2021.
[66] L. F. M. Navarro, “Investigating the influence of data analytics on content lifecycle management for maximizing resource efficiency and audience impact,” J. Comput. Soc. Dyn., vol. 2, no. 2, pp. 1–22, 2017.
[67] R. Reddy, S. Sharma, D. Reddy, and P. Singh, “Leveraging Deep Reinforcement Learning and Natural Language Processing for Enhanced Personalized Video Marketing Strategies,” J. AI ML Res., vol. 10, no. 8, 2021, [Online]. Available: https://joaimlr.com/index.php/v1/article/view/ 45
[68] A. S. Ogunmokun, E. D. Balogun, and K. O. Ogunsola, “A Conceptual Framework for AI- Driven Financial Risk Management and Corporate Governance Optimization,” Int. J. Multidiscip. Res. Growth Eval., vol. 2, no. 1, pp. 772 –780, 2021, 10.54660/.IJMRGE.2021.2.1.772-780.
[69] N. J. Isibor, C. Paul-Mikki Ewim, A. I. Ibeh, E. M. Adaga, N. J. Sam-Bulya, and G. O. Achumie, “A Generalizable Social Media Utilization Framework for Entrepreneurs: Enhancing Digital Branding, Customer Engagement, and Growth,” Int. J. Multidiscip. Res. Growth Eval., vol. 2, no. 1, pp. 751–758, 2021, 758.
[70] B. I. Adekunle, E. C. Chukwuma-Eke, E. D. Balogun, and K. O. Ogunsola, “A Predictive Modeling Approach to Optimizing Business Operations: A Case Study on Reducing Operational Inefficiencies through Machine Learning,” Int. J. Multidiscip. Res. Growth Eval., vol. 2, no. 1, pp. 791–799, 2021, 10.54660/.IJMRGE.2021.2.1.791-799.
[71] E. D. Balogun, K. O. Ogunsola, and A. S. Ogunmokun, “A Risk Intelligence Framework for Detecting and Preventing Financial Fraud in Digital Marketplaces,” vol. 4, no. 8, 2021.
[72] ED Balogun, KO Ogunsola, AS Ogunmokun, “A risk intelligence framework for detecting and preventing financial fraud in digital marketplaces. IRE Journals. 2021; 4 (8): 134- 140.” [Online]. Available: https://scholar.google.com/citations?view_op= view_citation&hl=en&user=JODGDIIAAAAJ &authuser=1&citation_for_view=JODGDIIA AAAJ:Wp0gIr-vW9MC
[73] GODWIN OZOEMENAM ACHUMIE NGOZI JOAN ISIBOR, AUGUST INE IFEANYI IBEH, CHIKEZIE PAUL-MIKKI EWIM, NGODOO JOY SAM -BULYA, EJUMA MARTHA ADAGA, “A Strategic Resilience Framework for SMEs: Integrating Digital Transformation, Financial Literacy, and Risk Management.” [Online]. Available: https://scholar.google.com/citations?view_op= view_citation&hl=en&user=4JmgDS8AAAAJ &cstart=20&pagesize=80&citation_for_view= 4JmgDS8AAAAJ:YOwf2qJgpHMC
[74] R. Joshi, V. Nair, M. Singh, and A. Nair, “Leveraging Natural Language Processing and Predictive Analytics for Enhanced AI-Driven Lead Nurturing and Engagement,” Int. J. AI Adv., vol. 10, no. 1, 2021, [Online]. Available: http://www.ijoaia.com/index.php/v1/article/vi ew/31
[75] Musa Adekunle Adewoyin, Enoch Oluwadunmininu Ogunnowo, Joyce Efekpogua Fiemotongha, Thompson Odion Igunma, Adeniyi K Adeleke, “Advances in CFD-Driven Design for Fluid-Particle Separation and Filtration Systems in Engineering Applications.” [Online]. Available: https://scholar.google.com/citations?view_op= view_citation&hl=en&user=6SQ3ZwQAAAA J&citation_for_view=6SQ3ZwQAAAAJ:W7 OEmFMy1HYC
[76] E. C. Chukwuma-Eke, O. Y. Ogunsola, and N. J. Isibor, “Designing a Robust Cost Allocation Framework for Energy Corporations Using SAP for Improved Financial Performance,” Int. J. Multidiscip. Res. Growth Eval., vol. 2, no. 1, pp. 809 –822, 2021, 10.54660/.IJMRGE.2021.2.1.809-822.
[77] A. K. Kalusivalingam, A. Sharma, N. Patel, and V. Singh, “Leveraging Neural Networks and Collaborative Filtering for Enhanced AI- Driven Personalized Marketing Campaigns,” Int. J. AI ML, vol. 1, no. 2, 2020, [Online]. Available: https://www.cognitivecomputingjournal.com/i ndex.php/IJAIML-V1/article/view/60
[78] Musa Adekunle Adewoyin, “Developing frameworks for managing low-carbon energy transitions: overcoming barriers to implementation in the oil and gas industry,” Magna Sci. Adv. Res. Rev., vol. 1, no. 3, pp. 068–075, Apr. 2021, 10.30574/msarr.2021.1.3.0020.
[79] ENOCH OLUWABUSAYO Alonge, NSISONG LOUIS Eyo -Udo, BRIGHT CHIBUNNA, ANDREW IFESINACHI DARAOJIMBA UBANADU, EMMANUEL DAMILARE BALO GUN, KOLADE OLUSOLA OGUNSOLA, “Digital Transformation in Retail Banking to Enhance Customer Experience and Profitability.” [Online]. Available: https://scholar.google.com/citations?view_op= view_citation&hl=en&user=JODGDIIAAAAJ &cstart=20&pagesize=80&authuser=1&citati on_for_view=JODGDIIAAAAJ:u5HHmVD_ uO8C
[80] Afees Olanrewaju Akinade, Peter Adeyemo Adepoju, Adebimpe Bolatito Ige, Adeoye Idowu Afolabi, and Olukunle Oladipupo Amoo, “A conceptual model for network security automation: Leveraging ai-driven frameworks to enhance multi -vendor infrastructure resilience,” Int. J. Sci. Technol. Res. Arch., vol. 1, no. 1, pp. 039–059, Sep. 2021,
[81] R. Joshi, N. Patel, M. Iyer, and S. Iyer, “Leveraging Reinforcement Learning and Natural Language Processing for AI-Driven Hyper-Personalized Marketing Strategies,” Int. J. AI ML Innov., vol. 10, no. 1, 2021, [Online]. Available: http://ijoaimli.com/index.php/v1/article/view/ 19
[82] N. Sharma, D. Sharma, R. Singh, and R. Singh, “Leveraging Reinforcement Learning and Natural Language Processing in AI-Enhanced Marketing Automation Tools,” Int. J. AI Adv., vol. 9, no. 4, 2020, [Online]. Available: http://www.ijoaia.com/index.php/v1/article/vi ew/1
[83] A. Babet, “Utilization of personalization in marketing automation and email marketing,” 2020, [Online]. Available: https://lutpub.lut.fi/handle/10024/161404
[84] V. Kumar, B. Rajan, R. Venkatesan, and J. Lecinski, “Understanding the Role of Artificial Intelligence in Personalized Engagement Marketing,” Calif. Manage. Rev., vol. 61, no. 4, pp. 135–155, Aug. 2019, 10.1177/0008125619859317.
[85] A. Mari, “The rise of machine learning in marketing: Goal, process, and benefit of AI- driven marketing,” 2019, [Online]. Available: https://www.zora.uzh.ch/id/eprint/197751/
[86] R. Sharma, A. Kumar, and C. Chuah, “Turning the blackbox into a glassbox: An explainable machine learning approach for understanding hospitality customer,” Int. J. Inf. Manag. Data Insights, vol. 1, no. 2, p. 100050, 2021.
[87] D. Anny, “The Role of Machine Learning in Marketing Automation,” 2021, [Online]. Available: https://www.researchgate.net/profile/Dave- Anny/publication/389649003_The_Role_of_ Machine_Learning_in_Marketing_Automatio n/links/67cb144ed75970006506e3f2/The- Role-of-Machine-Learning-in-Marketing- Automation.pdf
[88] D. I. Ajiga, “Strategic Framework for Leveraging Artificial Intelligence to Improve Financial Reporting Accuracy and Restore Public Trust,” Int. J. Multidiscip. Res. Growth Eval., vol. 2, no. 1, pp. 882–892, 2021, 10.54660/.IJMRGE.2021.2.1.882-892.
[89] M. Torre Núñez, “The implementation of AI in Marketing-Torre Nuñez, María,” 2021, [Online]. Available: https://repositorio.comillas.edu/xmlui/handle/ 11531/46966
[90] M. T. Núñez, “The Implementation Of AI In Marketing,” Univ. Pontif. Comillas, 2021, [Online]. Available: https://repositorio.comillas.edu/rest/bitstreams /437370/retrieve
[91] E. Daisy, “The Future of E-commerce: How AI, Automation, and Consumer Trends Are Reshaping Online Business,” 2021, [Online]. Available: https://www.researchgate.net/profile/Evelyn- Daisy/publication/391274738_The_Future_of _E- commerce_How_AI_Automation_and_Consu mer_Trends_Are_Reshaping_Online_Busines s/links/6810e5a4d1054b0207e56934/The- Future-of-E-commerce-How-AI-Automation- and-Consumer-Trends-Are-Reshaping- Online-Business.pdf
[92] N. Henke and L. Jacques Bughin, “The age of analytics: Competing in a data-driven world,” 2016, [Online]. Available: http://dln.jaipuria.ac.in:8080/jspui/bitstream/1 23456789/14275/1/mgi-the-age-of-analytics- full-report.pdf
[93] M. Analytics, “The age of analytics: competing in a data-driven world,” McKinsey Glob. Inst. Res., 2016, [Online]. Available: https://www.alvaroriascos.com/mineriadatos/ The-age-of-analytics-Executive-summary.pdf
[94] J. Prosper, “Real-Time Data Processing in Sales Pipelines: Challenges and Best Practices,” 2021, [Online]. Available: https://www.researchgate.net/profile/James- Prosper-2/publication/389516199_Real- Time_Data_Processing_in_Sales_Pipelines_C hallenges_and_Best_Practices/links/67c61ce1 96e7fb48b9d8087e/Real-Time-Data- Processing-in-Sales-Pipelines-Challenges- and-Best-Practices.pdf
[95] A. Schweyer, “Predictive analytics and artificial intelligence in people management,” Incent. Res. Found., pp. 1–18, 2018.
[96] D. Anny, “Predictive Analytics and AI for Enhanced Customer Targeting,” 2020, [Online]. Available: https://www.researchgate.net/profile/Dave- Anny/publication/389649011_Predictive_Ana lytics_and_AI_for_Enhanced_Customer_Targ eting/links/67cb165ccc055043ce6f2bc4/Predi ctive-Analytics-and-AI-for-Enhanced- Customer-Targeting.pdf
[97] S. Habibi, “The Role of Smart Technologies in the Relationship Between Volatile, Uncertain, Complex and Ambiguous Business Environment (VUCA) and Organizational Agility: Industrial Enterprises Research”.
[98] M. Rahman and M. Khondkar, “Small and Medium Enterprises (SME) Development and Economic Growth of Bangladesh: A Narrative of the Glorious 50 Years,” Small Medium Enterp., vol. 7, no. 1, 2020.
[99] Simon Kuznets Kharkiv National University of Economics, K. Zaslavska, Y. Zaslavska, and Simon Kuznets Kharkiv National University of Economics, “Impact of global factors on entrepreneurial structures: navigating strategic adaptation and transformation amidst uncertainty,” Actual Probl. Innov. Econ. Law, vol. 2024, no. 5, pp. 26–32, Sep. 2024, 10.36887/2524-0455-2024-5-5.
[100] D. Adema, S. Blenkhorn, and S. Houseman, “Scaling-up Impact: Knowledge -based Organizations Working Toward Sustainability”.
How to cite this paper
@article{1709051,
author = {Omolola Temitope Kufile, Bisayo Oluwatosin Otokiti, Abiodun Yusuf Onifade, Bisi Ogunwale, Chinelo Harriet Okolo},
title = {Designing Hyper-Personalized Digital Marketing Frameworks Using AI-Based Segmentation Techniques},
journal = {Iconic Research And Engineering Journals},
year = {2021},
volume = {4},
number = {8},
pages = {230-246},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1709051.pdf},
abstract = {The integration of Artificial Intelligence (AI) into digital marketing practices has led to unprecedented opportunities for personalization, offering marketers the capability to target consumers with high precision and relevance. This paper proposes a novel framework for designing hyper-personalized digital marketing strategies by leveraging AI-based segmentation techniques. Through a systematic synthesis of existing literature and application of machine learning and deep learning algorithms, the study outlines how AI can enhance customer segmentation beyond traditional demographic and behavioral markers. The methodology includes the implementation of unsupervised learning models, natural language processing, and neural network-based clustering to develop dynamically evolving customer segments. The results reveal substantial improvements in engagement metrics, conversion rates, and customer retention, validating the efficacy of AI-driven segmentation. The paper concludes with practical implications for digital marketing practitioners and recommendations for future research on ethical AI deployment in personalization.},
keywords = {AI segmentation, hyper-personalization, digital marketing, customer targeting, machine learning, data-driven strategy},
month = {February},
}