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Earley, Seth. "Cognitive Computing, Analytics, and Personalization." IT Professional 17, no. 4 (2015): 12–18. http://dx.doi.org/10.1109/mitp.2015.55.
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Bello, Binaebi Gloria, Ganiyu Bolawale Omotoye, Azeez Jason Kess-Momoh, Sunday Tubokirifuruar Tula, and Andrew Ifesinachi Daraojimba. "ADVANCED DATA ANALYTICS IN E-COMMERCE: A REVIEW OF PERSONALIZATION TECHNIQUES AND BUSINESS GROWTH." Business, Organizations and Society 2, no. 1 (2024): 32–39. https://doi.org/10.26480/bosoc.01.2024.32.39.
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The rapid evolution of e-commerce platforms has intensified the need for effective data analytics strategies to enhance user experiences and drive business growth. This paper presents a comprehensive review of advanced data analytics techniques employed in the realm of e-commerce, with a specific focus on personalized strategies. The objective is to understand how personalization techniques contribute to business growth and customer satisfaction. The review encompasses a diverse range of personalization methods, including collaborative filtering, content-based filtering, and hybrid approaches. Each technique is examined in terms of its applicability, strengths, and limitations within the e-commerce context. Additionally, the paper explores the integration of emerging technologies such as machine learning and artificial intelligence in refining personalization strategies, thereby providing a forward-looking perspective on the future of e-commerce analytics. Furthermore, the impact of personalized approaches on key performance indicators, such as conversion rates, customer retention, and revenue generation, is thoroughly analyzed. Case studies and real-world examples are incorporated to illustrate successful implementations of personalization techniques in various e-commerce domains. The findings highlight the pivotal role of advanced data analytics in tailoring the online shopping experience to individual preferences, fostering customer loyalty, and ultimately driving business growth. The paper concludes with a discussion on the challenges and ethical considerations associated with implementing personalized strategies, offering insights into potential avenues for future research in this dynamic and evolving field. Overall, this review contributes to a deeper understanding of the symbiotic relationship between advanced data analytics, personalization techniques, and the sustained success of e-commerce enterprises.
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Pechenizkiy, Mykola, and Dragan Gasevic. "Introduction into Sparks of the Learning Analytics Future." Journal of Learning Analytics 1, no. 3 (2015): 145–49. http://dx.doi.org/10.18608/jla.2014.13.8.
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This section offers a compilation of 16 extended abstracts summarizing research of the doctoral students who participated in the Second Learning Analytics Summer Institute (LASI 2014) held at Harvard University in July 2014. The abstracts highlight the motivation, main goals and expected contributions to the field from the ongoing learning analytics doctoral research around the globe. These works cover several major topics in learning analytics including novel methods for automated annotations, longitudinal analytic studies, networking analytics, multi-modal analytics, dashboards, and data-driven feedback and personalization. The assumed settings include the traditional classroom, online and mobile learning, blended learning, and massive open online course education models.
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Divya, Chockalingam. "Personalization in Online Car Shopping: A Data-Driven Approach." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 10, no. 1 (2024): 1–3. https://doi.org/10.5281/zenodo.15087123.
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The online car shopping experience has evolved significantly with advancements in artificial intelligence (AI) and big data analytics. Personalization has become a crucial component in enhancing user experience, driving customer engagement, and improving conversion rates. This paper explores the role of personalization in online car shopping, the challenges faced, and the data-driven solutions that enable a tailored shopping experience. Various aspects such as machine learning algorithms, recommendation systems, and predictive analytics are discussed, along with their impact on the automotive retail industry. The paper also examines the scope of personalization in future advancements, highlighting emerging trends such as blockchain integration and AI-driven price negotiation.
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Prabin Adhikari, Prashamsa Hamal, and Francis Baidoo Jnr. "The role of big data analytics and management information systems in consumer personalization in U.S. Retail, banking and finance." International Journal of Science and Research Archive 14, no. 2 (2025): 1186–201. https://doi.org/10.30574/ijsra.2025.14.2.0388.
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The increasing reliance on Big Data Analytics (BDA) and Management Information Systems (MIS) has significantly transformed consumer personalization strategies across industries. Businesses are leveraging data-driven insights, artificial intelligence (AI), and predictive analytics to enhance customer experiences, optimize engagement strategies, and tailor services based on individual preferences. However, challenges such as data privacy, ethical concerns, and system integration issues remain critical considerations in the adoption of these technologies. This study aims to examine the role of BDA and MIS in consumer personalization, focusing on how businesses utilize these technologies to enhance customer engagement, predict behavior, and deliver personalized services in the retail, banking, and finance sectors. The study employs a systematic literature review to analyze existing research on BDA and MIS-driven personalization. It synthesizes findings from peer-reviewed journals, conference proceedings, and industry reports to provide a comprehensive understanding of technological advancements and their implications. The results indicate that BDA enhances real-time decision-making, predictive modeling, and hyper-personalization, while MIS enables seamless data integration and customer relationship management (CRM). However, concerns regarding data security, algorithmic bias, and compliance with privacy regulations remain significant challenges. BDA and MIS are critical enablers of consumer personalization, yet businesses must adopt ethical AI practices, strengthen cybersecurity measures, and ensure regulatory compliance to maximize their benefits. Organizations should invest in scalable AI-driven MIS platforms, enhance transparency in data usage, and leverage predictive analytics to create consumer-centric personalization strategies while prioritizing privacy and security.
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Gupta, Tarun, and Supriya Bansal. "Cognitive Computing and Emotional Intelligence in Content Personalization." Journal of Marketing & Supply Chain Management 1, no. 4 (2022): 1–7. http://dx.doi.org/10.47363/jmscm/2022(1)135.
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This research paper is focused upon the problem connected with cognitive computing and emotional intelligence from the point of view of content marketing, analyzing analytics, reporting and personalization in terms of content. In the literature review, we trace the history of content marketing and commercials throwing away a significant range of events such as milestones that have contributed to the current trend for analytics embedded in structuring content strategies.
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Infantino, Marta. "Big Data Analytics, Insurtech and Consumer Contracts: A European Appraisal." European Review of Private Law 30, Issue 4 (2022): 613–34. http://dx.doi.org/10.54648/erpl2022030.
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The article investigates, from the European perspective, to what extent the enhanced availability of granular data to insurance companies and the growing sophistication of insurers’ processing capabilities through big data analytics (BDA) are fostering the increasing personalization of insurance products and services for consumers. To this purpose, the article first explores the very notion of ‘automated personalization’ in insurance, and then delves into the institutional, epistemic, economic and legal factors that, in Europe, work as a constraint, at least in the short-term, to paradigmatic shifts in insurance consumers contracts. The analysis will hopefully demonstrate that automated personalization in consumer insurance contracts, in Europe, is for the time being more a myth than a reality. What does exist, by contrast, is a no less problematic trend towards mass customization and robotization of consumer insurance contracts, which fully deserves lawyers’ attention.
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Said, Saara. "The Role of Artificial Intelligence (AI) and Data Analytics in Enhancing Guest Personalization in Hospitality." Journal of Modern Hospitality 2, no. 1 (2023): 1–13. http://dx.doi.org/10.47941/jmh.1556.
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Purpose: The main objective of this study was to explore the role of Artificial Intelligence (AI) and data analytics in enhancing guest personalization.
 Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.
 Findings: The findings revealed that there exists a contextual and methodological gap relating to the role of Artificial Intelligence (AI) and data analytics in enhancing guest personalization. Preliminary empirical review revealed the significant potential of AI and data analytics in transforming the hospitality industry by enhancing guest personalization. By offering personalized experiences that align with individual preferences, hotels can not only improve guest satisfaction but also drive revenue growth and customer loyalty. However, it is imperative for the industry to navigate the ethical considerations associated with data privacy to ensure that the benefits of personalization are realized without compromising guest trust and privacy. The findings of this study provide valuable insights for hoteliers, service providers, and policymakers looking to harness the power of AI and data analytics to create exceptional guest experiences in the evolving landscape of hospitality.
 Unique Contribution to Theory, Practice and Policy: The Technology Acceptance Model (TAM), Service Quality theory and Customer Relationship Management theory (CRM) may be used to anchor future studies on Artificial Intelligence and data analytics in modern hospitality. This study recommended for investing in robust AI and data analytics infrastructure, gathering comprehensive guest data, implementing AI driven personalization algorithms, empowering staff with AI tools and continuously monitoring and adapting.
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Al-Zahrani, Abdulrahman M., and Talal Alasmari. "Learning Analytics for Data-Driven Decision Making." International Journal of Online Pedagogy and Course Design 13, no. 1 (2023): 1–18. http://dx.doi.org/10.4018/ijopcd.331751.
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This study examines the use of learning analytics to enhance instructional personalization and student engagement in online higher education. The research focuses on the engagement levels of students based on different access methods (mobile and non-mobile), the relationships among engagement indicators, and the strategies for instructional personalization. Quantitative research methodology is employed to analyse and measure students' engagement levels. The findings indicate that students using non-mobile devices exhibit higher engagement in terms of average minutes, item accesses, and content accesses, while mobile access shows higher engagement in terms of course accesses, course interactions, and average interactions. Significant correlations are observed among engagement indicators, highlighting the importance of course interactions, content accesses, and assessment accesses in promoting student engagement. Accordingly, a critical model for effective student engagement in online learning courses is proposed.
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Parab, Gautam Ulhas. "AI-DRIVEN PERSONALIZATION IN RETAIL ANALYTICS: TRANSFORMING CUSTOMER EXPERIENCES." INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND INFORMATION TECHNOLOGY 7, no. 2 (2024): 2387–96. https://doi.org/10.34218/ijrcait_07_02_176.
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Earley, Seth. "The Problem of Personalization: AI-Driven Analytics at Scale." IT Professional 19, no. 6 (2017): 74–80. http://dx.doi.org/10.1109/mitp.2017.4241471.
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Rajesh, Goyal. "Research Paper: Customer Segmentation and Personalization in Insurance: A Business Analysis Approach." International Journal of Leading Research Publication 5, no. 8 (2024): 1–9. https://doi.org/10.5281/zenodo.14905567.
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Customer segmentation and personalization have become essential strategies for insurers looking to remain competitive in a rapidly evolving market. With advancements in data analytics and technology, insurers are increasingly able to categorize their customers based on behaviors, needs, and preferences. This paper explores the role of business analysts in enhancing customer segmentation and personalization within the insurance industry. By using data analytics, customer insights, and business intelligence, business analysts can help insurers better understand customer profiles, optimize product offerings, improve marketing strategies, and ultimately increase customer satisfaction and retention.
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Shamaylah, Nancy, Suleiman Ibrahim Mohammad, Badrea Al Oraini, et al. "Data-Driven Decision-Making for Employee Training and Development in Jordanian Public Institutions." Data and Metadata 4 (April 4, 2025): 886. https://doi.org/10.56294/dm2025886.
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Introduction: AI-driven training and HR analytics have revolutionized employee development by offering personalized learning experiences and optimizing skill enhancement. Public institutions are increasingly leveraging AI-based recommendations and adaptive learning algorithms to improve workforce training. However, the effectiveness and challenges of these approaches in real-world applications require further investigation.Methods: This study employed a descriptive and analytical research design, utilizing both quantitative and qualitative methods. Data was collected from 385 employees in Jordanian public institutions using structured surveys and sentiment analysis of employee feedback. Statistical techniques, including regression analysis, ANOVA, and correlation analysis, were applied to assess the impact of HR data analytics, AI-based recommendations, and training personalization on training effectiveness.Results: The findings indicate that HR data analytics, AI-based recommendations, and training personalization significantly improve training effectiveness. Skill development emerged as the strongest predictor of training success (β = 0.7282, p < 0.001). Sentiment analysis revealed that 82% of employees responded positively to AI-driven training, while 10% expressed concerns about content relevance and interactivity. ANOVA results confirmed no significant differences in training effectiveness across job roles, indicating equitable learning experiences.Conclusion: AI-powered training is widely accepted but requires further refinement to address personalization challenges and employee engagement concerns. Organizations should adopt a hybrid approach, integrating AI-driven learning with instructor-led guidance. Future research should explore long-term impacts of AI-based training on employee performance and organizational success to enhance digital workforce strategies.
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Gómez Rodríguez, Dustin Tahisin. "The convergence between artificial intelligence and tourism: perspectives for sustainable development and operational efficiency." Investigación, Tecnología e Innovación 17, no. 23 (2025): 125–36. https://doi.org/10.53591/iti.v17i23.2239.
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Context: The convergence between artificial intelligence (AI) and tourism has driven digital transformation in the industry, creating opportunities to enhance operational efficiency and traveler experiences. This study aims to understand how AI contributes to the sustainable development of tourism through process automation, hyper-personalization, and predictive analytics. Objective: To analyze how artificial intelligence (AI) contributes to the sustainable development of tourism by optimizing hyper-personalization, process automation, and predictive analytics to enhance operational efficiency and traveler experience. Methodology and Method: A systematic literature review was conducted using the PRISMA method. A total of 45 articles were selected through a filtering process in academic databases such as Scopus, Web of Science, and Google Scholar, applying inclusion criteria such as language, open access, and relevance .Results: AI enables hyper-personalization in service offerings, automation to optimize operational management, and predictive analytics to anticipate demand patterns. However, it also presents challenges such as data privacy, digital inclusion, and ethical regulation. Conclusions: AI can sustainably transform tourism if appropriate regulatory frameworks, training strategies, and cross-sector collaboration are implemented to ensure inclusive and responsible development.
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Nan, Zhang, Chen Yuping, and Zhu Kongjue. "The Future of Marketing Analytics: Trends and Emerging Technologies." International Journal of Advances in Business and Management Research 01, no. 03 (2024): 23–32. http://dx.doi.org/10.62674/ijabmr.2024.v1i03.003.
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The study gives a summary of marketing analytics, its importance in modern companies, and the changing environment influenced by technological progress. It stresses the need to keep abreast of trends and approaches to improve organisational results. The literature review explores important elements of marketing analytics such as artificial intelligence, machine learning, predictive analytics, personalization, consumer segmentation, and the growing significance of augmented reality and virtual reality. It delves into how these technologies and approaches are changing marketing strategies. The discussion section analyses the consequences of new trends and technology in marketing analytics. The text delves into the impact of AI, machine learning, omni-channel analytics, predictive analytics, real-time analytics, voice and visual search, content personalisation, and ethical issues on the evolution of marketing tactics. The conclusion emphasises the importance of marketing analytics in comprehending client behaviour and fostering corporate growth. It stresses the need to keep abreast of developing trends and technology to be competitive in the changing marketing environment. This study provides future suggestions derived from the examination of present trends in marketing analytics. The recommendation includes adopting new technologies, customizing consumer interactions, using predictive analytics, investing in talent development, balancing data privacy with personalization, utilizing social media, and tracking ROI.
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Rahmawati, Rahmawati, Nursalim Nursalim, Agry Alfiah, Andi Hasyim, and Aldi Bastiatul Fawait. "Impact of Using Big Data Analisys in Increasing Personalization of Learning." Journal of Computer Science Advancements 2, no. 2 (2024): 54–72. http://dx.doi.org/10.70177/jsca.v2i2.906.
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In today’s digital era, big data analytics has become a very relevant topic to improve learning personalisation as it can collect and analyse very large and complex data. Big data analytics can lead to a more efficient learning system by collecting and analysing huge and complex data. In education, big data analytics can be used to understand students’ learning behaviour, their needs and preferences, so that learning and learning outcomes can be improved. This research is conducted with the aim of using big data analytics to improve learning personalisation. It also aims to find out the challenges of using big data analytics to improve learning personalisation. The method used in this research is quantitative method. This method is a way of collecting numerical data that can be tested. Data is collected through the distribution of questionnaires addressed to students. Furthermore, the data that has been collected from the distribution of the questionnaire, will be accessible in Excel format which can then be processed with SPSS. From the research results, it can be seen that the big data analysis has shown that the use of more detailed and accurate data can help teachers find students’ special needs and improve learning effectiveness. As a result, teachers can create learning strategies that are better suited to students’ needs and improve their learning outcomes. From this study, we can conclude that the use of big data analytics in improving personalisation allows teachers to understand better the individual needs and preferences of students, so that more suitable learning plans can be developed and student engagement can be improved.
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Zare, Zahra, Annur Islam Sifat, and Mumtaz Karatas. "A Review of Data Analytics and Machine Learning for Personalization in Tech Sector Marketing." Journal of Soft Computing and Decision Analytics 3, no. 1 (2025): 92–111. https://doi.org/10.31181/jscda31202562.
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This paper reviews the data analytics and machine learning applications in enhancing the personalization of digital marketing communications within the technology sector. Our review focuses on key areas such as customer segmentation, predictive analytics applications, real-time data processing, and behavioral and sentiment analysis. Using an exploratory and qualitative research approach, we examine 61 articles, reports, and case studies. Our study highlights how data-driven and machine learning methodologies improve customer responsiveness and marketing strategies. Our findings reveal that analytical techniques contribute to increased sales through more personalized advertising, fostering stronger customer relationships. Additionally, the growing adoption of these approaches to strengthen digital marketing is a key trend explored in this secondary data-based research.
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Sun, Chen. "Data Analysis of Customer Segmentation and Personalized Strategy in the Era of Big Data." Advances in Economics, Management and Political Sciences 92, no. 1 (2024): 46–52. http://dx.doi.org/10.54254/2754-1169/92/20231411.
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This article provides an overview of the use of data analytics for customer segmentation and personalization in marketing strategies. The article reviews the various approaches, advantages and challenges of using data analytics to gain insights into customer behavior and preferences. The paper also discusses the role of emerging technologies in improving data analytics capabilities for effective segmentation and personalization by examining a large body of literature. In this work, I have compiled this review by understanding and delving into the changing evolution of the traditional retail industry in the digital marketing era, the application of data analytics in modern marketing, and the impact of novel technologies such as artificial intelligence in informing strategic marketing decisions such as market segmentation and customer segmentation, and improving the efficiency of operations management. The results of the review highlight the importance of using data-driven approaches to shape modern marketing practices and provide practical insights for companies aiming to optimize customer engagement and maximize profits.
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Rohit Sharma. "Data-Driven Personalization : Revolutionizing User Experience." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 5 (2024): 868–77. http://dx.doi.org/10.32628/cseit241051075.
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Data-driven personalization has emerged as a transformative approach in digital user experience design, leveraging advanced analytics and machine learning to tailor content and interfaces to individual users. This article explores five key aspects of data-driven personalization: user behavior analysis, segmentation and targeting, machine learning algorithms, real-time adaptation, and privacy and ethical considerations. It examines the significant impact of personalization on business outcomes, including increased revenue and customer engagement, while also addressing implementation challenges, such as technological complexity and privacy concerns. The article provides insights into the methodologies, processes, and best practices for effective personalization, supported by industry statistics and case studies, offering a comprehensive overview of how organizations can harness this powerful approach to create more engaging, relevant, and effective digital experiences.
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Ifraheem, Sumaira, Muzna Rasheed, and Arfa Siddiqui. "Transforming Education Through Artificial Intelligence: Personalization, Engagement and Predictive Analytics." Journal of Asian Development Studies 13, no. 2 (2024): 250–66. http://dx.doi.org/10.62345/jads.2024.13.2.22.
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AI can boost education's efficiency and effectiveness in teaching and learning. In the first step, provide a summary of AI in the multipronged service to education, show the capacity of AI to tailor instruction to the interactive learning environments that it makes possible, and thereby urge its application in this area. Next, this paper uses the literature review, examples, and fictitious data commentaries to show how artificial intelligence tools and programming A and B above (including intelligent tutoring systems, adaptive learning platforms, automatic grading, and VR AR technology) reshape school outcomes and redefines student engagement. This study adopts a mixed-methods approach to investigate the impact of Artificial Intelligence (AI) on academic outcomes and engagement. By combining qualitative and quantitative research methods, this paper aims to comprehensively analyze AI's role in modern educational settings. The methodology is designed to gather data from various sources, including case studies, surveys, and experimental data, to offer a holistic view of AI's educational implications. Sampling was done from the 100 teachers and students about the public and private schools and universities of Central Karachi. The analysis highlights positive recognition of AI's value in lifelong learning and increased engagement. It also underscores the need to address existing challenges to ensure AI effectively delivers its potential benefits. Future enhancements should prioritize design aspects such as user experience, adaptability, and accuracy to optimize AI's impact on engagement and learning quality.
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Mouri, Kousuke, Hiroaki Ogata, Noriko Uosaki, and Erdenesaikhan Lkhagvasuren. "Context-aware and Personalization Method based on Ubiquitous Learning Analytics." JUCS - Journal of Universal Computer Science 22, no. (10) (2016): 1380–97. https://doi.org/10.3217/jucs-022-10-1380.
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In the past decades, ubiquitous learning (u-learning) has been the focus of attention in educational research across the world. Majority of u-learning systems have been constructed using ubiquitous technologies such as RFID tags and cards, wireless communication, mobile phones, and wearable computers. There is also a growing recognition that it can be improved by utilizing ubiquitous learning logs collected by the u-learning system to enhance and increase the interactions among a learner, contexts, and context-based knowledge. One of the issues of analytics based on u-learning is how to detect or mine learning logs collected by u-learning systems. Moreover, it is necessary to evaluate whether the recommendations detected by analysis are appropriate in terms of learning levels, contexts and learners' preference. To tackle the issues, we developed a system that could recommend useful learning logs at the right place in the right time in accordance with personalization of learners. An experiment was conducted to evaluate the system's performance and the recommendations' usefulness for learning. In the evaluation experiment, we found important criteria for recommending in the real-world language learning. In addition, the participants were able to increase their learning opportunities by our recommendation method.
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Qasim, Dhia, and Amin Khalifeh. "Implementing digital marketing using artificial intelligence." International Journal of Innovative Research and Scientific Studies 8, no. 3 (2025): 2377–84. https://doi.org/10.53894/ijirss.v8i3.6993.
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This paper aims to discuss the application of AI in digital marketing, its role in enhancing decision-making, personalization, and campaign performance along the customer's journey. This study employs a qualitative research approach to investigate the strategic application of AI in digital marketing, drawing on recent academic literature, industry reports, and case studies. Thematic coding was applied to identify key patterns and emerging themes such as predictive analytics, customer personalization, and performance optimization. The key applications are big data analytics, content personalization, omnichannel integration, automated content generation, and dynamic customer interaction. The evidence suggests that the strategic use of AI not only enhances customer satisfaction and conversion rates but also improves operating efficiency and fosters long-term brand loyalty. As digital ecosystems continue to evolve, AI emerges as a key driver of innovation and digital marketing competitiveness. The study leverages existing academic and practical knowledge to describe how AI equips organizations with the capacity to manage enormous datasets, automate marketing functions, and deliver hyper-personalized experiences.
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Singh, Navdeep. "AI-Driven Personalization in eCommerce Advertising." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 1692–98. http://dx.doi.org/10.22214/ijraset.2023.57695.
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Abstract: In the dynamic realm of eCommerce, the integration of Artificial Intelligence (AI) has revolutionized advertising strategies, forging a path towards highly personalized consumer experiences. This exploration delves into the multifaceted role of AI in eCommerce advertising, highlighting the efficacy of technologies such as machine learning, natural language processing, and predictive analytics. A thorough analysis of consumer behavior, underpinned by AI, reveals advancements in data collection, privacy concerns, and innovative data analysis techniques. Ethical considerations, including data privacy and bias in AI algorithms, emerge as pivotal in maintaining consumer trust. The paper presents an array of case studies, illustrating the successful application of AI across diverse industries.
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Banerjee, Syagnik. "Geosurveillance, Location Privacy, and Personalization." Journal of Public Policy & Marketing 38, no. 4 (2019): 484–99. http://dx.doi.org/10.1177/0743915619860137.
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As connected consumers expand their digital footprint, firms are legally purchasing location data generated by apps, sold to intermediaries, and cleaned by analytics vendors for personalized targeting, advertising, and risk profiling. Data storage and flow across multiple sectors and states cause increased variability in agency jurisdiction, legal standards, and premise for legal recourse to privacy violations. To better inform industries, policy makers, and consumers in this rapidly changing environment, the author develops a new construct, location privacy, articulating the rich impact of geosurveillance on the consumer. Analysis of studies conducted using car GPS and wearable devices find that data service provider familiarity (known, unknown) and georeferencing style (environment, movement) affect location privacy concerns and the adoption likelihood of personalized driving and health insurance policies underwritten with disclosed location data. The article discusses implications about potential marketer liabilities and regulators’ roles in moderating the market’s concerns regarding geosurveillance.
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Pandey, Dr Shilpa. "A Study on Role of AI in Transforming Digital Advertising Strategies." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04398.
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ABSTRACT The rapid evolution of artificial intelligence (AI) has significantly reshaped the digital advertising landscape, offering new opportunities for businesses to enhance efficiency, personalization, and customer engagement. This dissertation explores the transformative role of AI in digital advertising strategies, focusing on its impact on targeting precision, content creation, campaign automation, and real-time analytics. Through a combination of secondary data analysis and case studies, the study highlights how AI technologies—such as machine learning, natural language processing, and predictive analytics—enable marketers to understand consumer behavior better and deliver hyper-personalized experiences. The research also examines the ethical challenges and limitations of AI in advertising, including data privacy concerns and algorithmic bias. The findings underscore the importance of strategic AI integration to maintain competitiveness in the digital marketing arena. This study contributes to a deeper understanding of how businesses can leverage AI to revolutionize their advertising approaches and improve overall marketing performance. KEYWORDS: Artificial Intelligence, Digital Advertising, Personalization, Targeting Precision, Content Creation, Campaign Automation, Real-time Analytics, Machine Learning, Natural Language Processing, Predictive Analytics, Consumer Behaviour, Data Privacy, Algorithmic Bias, Marketing Performance
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Lakshmi Narayana Gupta Koralla. "Hyper-personalization: Transforming digital experiences through advanced data analytics and AI." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 333–45. https://doi.org/10.30574/wjaets.2025.15.1.0219.
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This comprehensive article explores the transformative impact of hyper-personalization strategies across diverse industries, examining the conceptual frameworks, technical infrastructure, implementation paradigms, advanced AI applications, privacy considerations, and business outcomes. Hyper-personalization represents a paradigm shift in customer experience, operating on the principle of dynamic identity recognition, where consumer preferences exist in constant contextual flux rather than as fixed attributes. The article presents key concepts including algorithmic decision architecture, precision engagement systems, signal intelligence ecosystems, and latency-optimized delivery systems that drive substantial improvements in conversion rates, customer retention, and operational efficiencies. The inquiry demonstrates how organizations leverage cognitive computing frameworks, multi-dimensional attribution systems, and privacy-enhancing computation to balance improved customer experiences with ethical considerations and regulatory compliance, ultimately achieving measurable business value through more precise targeting, enhanced customer journeys, and strengthened relationship durability.
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Julker Nain. "Ai-driven CRM systems in insurance: Personalization at scale." World Journal of Advanced Research and Reviews 23, no. 2 (2024): 2850. https://doi.org/10.30574/wjarr.2024.23.2.2523.
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The purpose of this research paper investigates artificial intelligence and data analytics phenomena which impact the financial services industry specifically in Customer Relationship Management systems implementation. This document examines contemporary CRM system development together with artificial intelligences in customer analytics and their practical and complex implementation challenges. This research explores how artificial intelligence enhances both personalization operations and customer information and decision-making through natural language processing and machine learning and predictive analysis study. The proof of AI-CRM system performance needs additional clarification based on current evidence from retail banking, wealth management businesses and insurance industries showing positive results. Data privacy aspects along with ethical AI utilization in finance and compliance requirements form essential points studied in the research. The research provides financial service providers with both present-time trends analysis and literature-validated guidelines about implementing AI into the CRM system to generate possibilities for future applications.
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Julker, Nain. "Ai-driven CRM systems in insurance: Personalization at scale." World Journal of Advanced Research and Reviews 23, no. 2 (2024): 2850–65. https://doi.org/10.5281/zenodo.14908920.
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The purpose of this research paper investigates artificial intelligence and data analytics phenomena which impact the financial services industry specifically in Customer Relationship Management systems implementation. This document examines contemporary CRM system development together with artificial intelligences in customer analytics and their practical and complex implementation challenges. This research explores how artificial intelligence enhances both personalization operations and customer information and decision-making through natural language processing and machine learning and predictive analysis study. The proof of AI-CRM system performance needs additional clarification based on current evidence from retail banking, wealth management businesses and insurance industries showing positive results. Data privacy aspects along with ethical AI utilization in finance and compliance requirements form essential points studied in the research. The research provides financial service providers with both present-time trends analysis and literature-validated guidelines about implementing AI into the CRM system to generate possibilities for future applications.
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Jaiswal, Sanjana. "The Role of Artificial Intelligence in Personalizing Digital Marketing Strategies." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04415.
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Abstract: AI as machine learning, predictive analytics, and natural language processing to create highly personalized and targeted marketing campaigns. The study investigates how AI contributes to understanding consumer preferences, segmenting audiences, automating content delivery, and optimizing user experiences in real-time. Through a combination of literature review, case studies, and data analysis, the research highlights the effectiveness of AI-driven personalization in improving customer engagement, conversion rates, and brand loyalty. The findings demonstrate that AI not only enables marketers to anticipate consumer needs but also fosters deeper, data-driven relationships between brands and their customers. The study concludes with recommendations for marketers to strategically implement AI technologies while ensuring ethical use of consumer data. Keywords: Artificial Intelligence, Digital Marketing, Personalization, Predictive Analytics, Customer Engagement, Consumer Behavior
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Raiyan Haider, Md Farhan Abrar Ibne Bari, Md. Farhan Israk Shaif, and Mushfiqur Rahman. "Engineering hyper-personalization: Software challenges and brand performance in AI-driven digital marketing management: An empirical study." International Journal of Science and Research Archive 15, no. 2 (2025): 1122–41. https://doi.org/10.30574/ijsra.2025.15.2.1525.
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In this empirical study, we delve into engineering hyper-personalization within AI-driven digital marketing management. We focus specifically on the software challenges encountered and their impact on brand performance. AI technologies are truly transforming marketing, offering capabilities like precise customer segmentation, personalized content delivery, and real-time analytics – essential tools for achieving hyper-personalization. While AI holds significant promise for creating highly relevant and effective campaigns, implementing it for hyper-personalization brings distinct software-related challenges. These include navigating data privacy, ensuring algorithmic transparency, and addressing biases. Overcoming these engineering obstacles becomes essential for leveraging AI effectively to enhance customer experiences, optimize campaign results, and ultimately build stronger brand loyalty and visibility. Our study offers insights into these specific challenges and their implications for businesses aiming to maximize brand performance through advanced AI personalization.
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Marzuki, Marzashifa, Siti Fatimah Zahra Azero, Nor Awaliss Alia Mohd Zamzuri, and Mohd Razilan Abdul Kadir. "A Systematic Literature Review of User Behavior and Personalization in Digital Libraries." International Journal of Research and Innovation in Social Science IX, no. I (2025): 4830–42. https://doi.org/10.47772/ijriss.2025.9010372.
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This systematic literature review explores the intersection of user behavior and personalization in digital libraries. The digital library environment is shaped by diverse user needs and expectations, making understanding user behavior a critical factor in enhancing personalization. Personalization has emerged as a key strategy to increase user satisfaction, engagement, and resource discovery. However, studies addressing privacy, biases, and scalability in digital library personalization are still scarce. Moreover, there is also limited existing study focusing on ethical and user-friendly personalization. A total of 45 articles were selected from 720 initial records retrieved from databases including Scopus, Web of Science, Emerald Insight, and IEEE Xplore, using predefined inclusion and exclusion criteria and adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The study underscores the role of user behavior analytics and machine learning in personalizing digital libraries, highlighting challenges such as privacy, algorithmic biases, and scalability. It emphasizes balancing personalization with ethical considerations and offers actionable insights for librarians and developers to create user-centric systems.
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Chaturvedi, Dr Pritha. "Personalization in Banking: The Key to Customer Retention." International Journal of Management and Humanities 11, no. 8 (2025): 14–17. https://doi.org/10.35940/ijmh.g1793.11080425.
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In the highly competitive banking industry, personalization has emerged as a crucial strategy for improving customer retention. By leveraging advanced technologies such as artificial intelligence (AI), predictive analytics, and customer relationship management (CRM) systems, banks can deliver tailored services that meet individual customer needs. As customers increasingly expect relevant and timely interactions, banks that prioritize personalization are better positioned to meet these expectations and secure long-term growth. Studies indicate that effective personalization can lead to a 10-15% increase in sales conversion rates and a 20-30% boost in customer satisfaction. This article explores the importance of personalization in banking, identifies key objectives, reviews existing literature, and presents findings from recent studies and methodologies. The analysis highlights the challenges and opportunities of implementing personalization strategies and concludes with actionable recommendations for banks to enhance customer loyalty and long-term engagement. The paper provides insights from industry reports, real-life case studies, and interviews with banking professionals, presenting a comprehensive understanding of how personalization shapes the banking landscape.
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Dr., Pritha Chaturvedi. "Personalization in Banking: The Key to Customer Retention." International Journal of Management and Humanities (IJMH) 11, no. 8 (2025): 14–17. https://doi.org/10.35940/ijmh.G1793.11080425.
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<strong>Abstract:</strong> In the highly competitive banking industry, personalization has emerged as a crucial strategy for improving customer retention. By leveraging advanced technologies such as artificial intelligence (AI), predictive analytics, and customer relationship management (CRM) systems, banks can deliver tailored services that meet individual customer needs. As customers increasingly expect relevant and timely interactions, banks that prioritize personalization are better positioned to meet these expectations and secure long-term growth. Studies indicate that effective personalization can lead to a 10-15% increase in sales conversion rates and a 20-30% boost in customer satisfaction. This article explores the importance of personalization in banking, identifies key objectives, reviews existing literature, and presents findings from recent studies and methodologies. The analysis highlights the challenges and opportunities of implementing personalization strategies and concludes with actionable recommendations for banks to enhance customer loyalty and long-term engagement. The paper provides insights from industry reports, real-life case studies, and interviews with banking professionals, presenting a comprehensive understanding of how personalization shapes the banking landscape.
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Guyo, Gamal. "Personalization in Marketing Communications and Purchase Intent in Egypt." International Journal of Marketing Strategies 6, no. 2 (2024): 46–55. http://dx.doi.org/10.47672/ijms.2134.
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Purpose: The aim of the study was to assess the personalization in marketing communications and purchase intent in Egypt. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study consistently showed that tailored messages, customized offers, and personalized experiences lead to higher levels of engagement, trust, and ultimately, conversion rates. Consumers respond positively to marketing content that addresses their specific needs, preferences, and pain points, leading to a greater likelihood of making a purchase. Additionally, the use of data-driven personalization techniques, such as behavioral targeting and predictive analytics, has further enhanced the effectiveness of personalized marketing campaigns in driving purchase intent. Overall, personalization is a powerful strategy that fosters stronger connections between brands and consumers, resulting in increased sales and customer satisfaction. Implications to Theory, Practice and Policy: Social identity theory, technology acceptance model and cognitive dissonance theory may be used to anchor future studies on assessing the personalization in marketing communications and purchase intent in Egypt. In terms of practical applications, marketers should invest in advanced data analytics and machine learning algorithms to gather and analyze consumer data effectively. From a policy perspective, advocating for data privacy regulations and ethical guidelines is crucial to ensure the responsible use of consumer data in personalization efforts.
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Campos, Josephine Diana S., and Jofrey R. Campos. "Unveiling the Nexus of Distribution Personalization and Content Decentralization: Exploring Brand Recall among Gen Z in Region III, Philippines." International Journal of Entrepreneurship, Business and Creative Economy 4, no. 2 (2024): 52–65. http://dx.doi.org/10.31098/ijebce.v4i2.2252.
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The study emphasized the pivotal role of distribution personalization, mediated by content decentralization, in influencing brand recall among Gen Z consumers, highlighting the importance of advanced data analytics, optimized distribution channels, user-generated content incorporation, and user-centric storytelling for effective brand engagement in the modern market. This research studied the effects of distribution personalization and content decentralization on brand recall among Gen-Z, focusing on causal relationship and the issue of attention competition. It used Partial Least Squares Structural Equation Modeling to analyze 385 responses from Region III, Philippines Gen Z participants. The results indicate that personalized distribution has direct influence on brand recall through an ability to remember brand features amid consumer attention competition. Content decentralization partially mediates this effect, suggesting challenges in maintaining consistent brand identity across diverse platforms. The research underscores the importance of distribution personalization in brand recall, highlighting how content decentralization and variety of content across platforms can dilute brand consistency and hinder Gen Z's brand recall. These suggest centralizing content from a single source to ensure uniformity in branding while also using advanced analytics for targeted personalization. The study recommends optimizing distribution channels and incorporating user-centric storytelling to enhance brand association. Researchers need to continuously analyze consumer trends and feedback to refine distribution personalization strategies and stay responsive to changing market conditions. Content decentralization makes it difficult to reach and engage with the entire audience effectively, as they may be spread out across numerous channels, thus future researchers may look into this aspect.
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Thiyagarajan, Gomathi. "Personalization and Visual Representation through Learning Analytics: A Meaningful Approach to Guide Self-Directed Learners." International Journal of Psychosocial Rehabilitation 24, no. 5 (2020): 3298–303. http://dx.doi.org/10.37200/ijpr/v24i5/pr202037.
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Venkateswaran Petchiappan. "AI-driven vehicle customization and personalization in automobile industry." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 157–68. https://doi.org/10.30574/wjaets.2025.15.3.0921.
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The automobile industry is experiencing a profound digital transformation with artificial intelligence emerging as a cornerstone technology reshaping customer experiences and operational paradigms. AI-powered vehicle selection and configuration systems represent transformative applications revolutionizing how consumers discover, personalize, and purchase vehicles. Modern automotive manufacturers leverage sophisticated data analytics platforms like SAP HANA, with in-memory computing capabilities processing configuration variables during real-time customer interactions. These systems analyze substantial volumes of customer data to deliver highly personalized vehicle configurations tailored to individual preferences, driving habits, and lifestyle requirements. The technological foundation incorporates machine learning, natural language processing, and big data analytics within unified customer data platforms, enabling remarkable improvements in customization accuracy and delivery timelines. The implementation methodologies span hyper-personalized vehicle configuration, dynamic pricing optimization, and fleet electrification strategies, resulting in significant operational efficiency improvements, customer experience enhancements, and sustainability impacts. Future directions include blockchain-verified vehicle customization, advanced AI methodologies, and integration of extended reality, promising to further revolutionize the automotive customization landscape through immutable configuration records, reinforcement learning models, and immersive configuration experiences.
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Komal, Komal. "The Role of AI in Predicting Consumer Behaviour and Optimizing Marketing Campaigns." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49515.
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1. Abstract Artificial Intelligence (AI) is redefining the marketing landscape by enabling businesses to forecast consumer behavior and optimize campaign strategies with unprecedented accuracy. This paper explores the transformative impact of AI-driven tools—including machine learning, predictive analytics, and automation—on modern marketing practices. Employing a mixed-method approach, the study gathers insights from digitally active Indian consumers through surveys and from industry experts via interviews. Findings reveal that AI enhances personalization, engagement, and overall marketing performance. However, challenges such as data privacy concerns, algorithmic opacity, and over-reliance on automation persist. This study underscores the importance of ethical AI integration and offers actionable guidelines for marketers to align AI use with consumer trust and campaign effectiveness. Keywords Artificial Intelligence (AI); Consumer Behavior; Predictive Analytics; Marketing Optimization; Personalization; Trust; India.
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Rudraraju, Prithvi Raju. "THE ROLE OF DATA ANALYTICS IN EDUCATION: TRANSFORMING LEARNING THROUGH PERSONALIZATION." INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY AND MANAGEMENT INFORMATION SYSTEMS 16, no. 1 (2025): 647–58. https://doi.org/10.34218/ijitmis_16_01_046.
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Ashrafuzzaman, Md, Rokhshana Parveen, Mahiya Akter Sumiya, and Anisur Rahman. "AI-POWERED PERSONALIZATION IN DIGITAL BANKING: A REVIEW OF CUSTOMER BEHAVIOR ANALYTICS AND ENGAGEMENT." American Journal of Interdisciplinary Studies 6, no. 1 (2025): 40–71. https://doi.org/10.63125/z9s39s47.
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The rapid evolution of digital banking has prompted financial institutions to integrate artificial intelligence (AI) technologies to deliver highly personalized and engaging customer experiences. As customer expectations grow increasingly dynamic, AI-powered personalization has emerged as a strategic imperative, enabling banks to tailor services in real time based on individual behaviors, preferences, and financial patterns. This study systematically reviews the literature on AI-powered personalization in digital banking, with a specific focus on how customer behavior analytics and intelligent algorithms contribute to enhanced engagement, satisfaction, retention, and trust. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 framework, a total of 111 peer-reviewed articles published between 2014 and 2024 were analyzed to identify core themes, methodologies, innovations, and conceptual gaps. The reviewed literature is thematically organized into seven key domains: foundational AI techniques, behavioral data modeling, predictive analytics, customer engagement outcomes, ethical and governance challenges, innovations in emerging markets, and research limitations. The findings reveal that AI-driven personalization not only improves operational efficiency and service quality but also fosters emotional loyalty and increases the lifetime value of banking customers. Advanced AI techniques—such as machine learning, natural language processing, recommender systems, and sentiment analysis—are widely applied to deliver seamless, context-aware experiences across mobile apps, web portals, and virtual assistants. However, the literature also highlights significant challenges, including inconsistent measurement frameworks, regulatory uncertainty, data privacy concerns, and insufficient attention to cultural diversity and longitudinal performance. Emerging markets, while constrained by infrastructural and regulatory limitations, exhibit innovative adaptations through alternative data use and hybrid AI-human service delivery models. This review offers a comprehensive synthesis of the academic discourse on AI personalization in digital banking and underscores critical areas for future research, industry practice, and policy intervention aimed at building inclusive, ethical, and scalable AI solutions.
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Juniarta, I. Gusti Made, and Aletta Dewi Maria Th. "Optimalisasi Pengambilan Keputusan Pemasaran melalui Analisis Data Analytics di Patra Cirebon Hotel & Convention." Journal of Education, Humaniora and Social Sciences (JEHSS) 7, no. 2 (2024): 547–53. https://doi.org/10.34007/jehss.v7i2.2414.
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This article aims to analyze the role of data analytics in optimizing marketing strategies at Patra Cirebon Hotel & Convention, particularly in enhancing service personalization and the effectiveness of marketing campaigns. Using the theoretical approach of digital marketing management and customer-based data analysis, this study collected data through interviews with the marketing manager and secondary data analysis of the hotel’s performance reports, which were then qualitatively analyzed. The study results indicate that data analytics contributes significantly to increasing sales conversion and customer loyalty through more targeted personalization strategies. However, this research also reveals several challenges, such as limited human resources and the complexity of integrating data from various sources. Nevertheless, these findings are relevant for marketing practices in the hospitality sector, offering hotel managers guidance on leveraging data analytics to drive business growth. This research is expected to serve as a reference for marketing practitioners in the hospitality industry, particularly in designing data-driven strategies that are more efficient and customer-focused, as well as adding value to the development of customer-oriented marketing practices.
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Xiao, Mengzhen, Yongchao Xu, and Zekai Gao. "Review on the Use of Data Analysis for Customer Segmentation and Personalization in Marketing Strategies." Advances in Economics, Management and Political Sciences 106, no. 1 (2024): 10–16. http://dx.doi.org/10.54254/2754-1169/106/20241362.
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This paper studies the application of data analysis in the field of customer segmentation and personalization. With the advent of the era of big data, enterprises and organizations have rich data resources that contain important information about customer behavior, preferences, and purchasing habits. Through data analytics, companies can better understand customer needs and provide personalized products and services, thereby improving customer satisfaction and loyalty. This study aims to explore the application of data analytics in the field of customer segmentation and personalization, and provides some common data analysis methods. Data analysis methods include probabilistic methods and Bayesian methods, among others. Through case studies, the application of data analysis in the field of e-commerce is explained. The research results show that data analytics has important value and potential applications in customer segmentation and personalized marketing. However, data analysis still faces some challenges and limitations in practice, including data security and quality issues. Future research can continue to explore methods and techniques for data analysis, solve problems such as data privacy and security, and apply data analysis in more fields.
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SIDDIQUE, MOHD ANAS. "A Study on the Role of AI Personalizing Digital Marketing Strategies." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50598.
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Abstract- This study examines the disruptive nature of Artificial intelligence (AI) in the personalization of digital marketing approaches and its effect in improving consumer response and business outcomes. As AI technologies including machine learning, predictive analytics, and natural language processing spread, marketers can bring an ever-greater level of personalization to the content, offers, and customer experiences based on individual preferences and behaviors in real time. The research design is descriptive and the data is gathered through structured questionnaires addressed to marketing professionals and consumers in order to evaluate the awareness, effectiveness, challenges, and ethical concerns related to the AI-driven personalization. The results indicate that AI greatly enhances the accuracy of targeting, customer satisfaction and efficiency in the process of marketing, and also notes the obstacles which include prohibitive cost of implementation, shortage of skills and privacy concerns. The concept of ethical use of data, transparency turned out to be the important factors affecting consumer acceptability and trust. The study provides empirical data to the emerging field of research about AI in marketing and provides practical suggestions about how businesses can get the best of AI integration without causing any harm. The present paper contributes to the strategic significance of AI in the future of digital marketing and preconditions the additional research on the rising AI technologies and ethical considerations. Keywords- Artificial Intelligence, Digital Marketing, Personalization, Consumer Engagement, Machine Learning, Predictive Analytics, Ethical Considerations, Customer Experience, Marketing Strategy.
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Ibrahim Adedeji Adeniran, Christianah Pelumi Efunniyi, Olajide Soji Osundare, and Angela Omozele Abhulimen. "Transforming marketing strategies with data analytics: A study on customer behavior and personalization." International Journal of Scholarly Research in Engineering and Technology 4, no. 1 (2024): 041–51. http://dx.doi.org/10.56781/ijsret.2024.4.1.0022.
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In the rapidly evolving digital landscape, data-driven marketing has emerged as a pivotal strategy for businesses seeking to enhance customer engagement and optimize marketing outcomes. This review paper delves into the transformation from traditional to data-centric marketing, highlighting the historical evolution and technological advancements that have propelled this shift. It examines the critical role of data analytics in understanding customer behavior, detailing the tools and techniques employed to interpret vast amounts of customer data. Furthermore, the paper explores personalization as a key marketing strategy, discussing methods to achieve it through data analytics and emphasizing the importance of customer segmentation. Ethical considerations and challenges in personalized marketing are also addressed. The impact of data-driven marketing is evaluated through successful case studies, and the future trajectory of marketing in the age of analytics is predicted. The review summarizes key insights and their strategic implications for marketers, underscoring the necessity of balancing innovation with ethical responsibility.
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Yadav, Avinash Kumar. "“Al Personalization on Purchase Intention”." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50669.
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ABSTRACT In today’s digital-first economy, Artificial Intelligence (AI) is not merely a tool but a transformative force revolutionizing the way consumers interact with online platforms. This study, titled “An Analysis of the Impact of AI Personalization on Consumer Purchase Intention in Online Retail Platforms,” critically examines how AI-driven personalization techniques—such as predictive recommendations, real-time dynamic pricing, tailored content, and conversational interfaces—affect consumer psychology, behavior, and decision- making in the context of e-commerce. With the growing demand for convenience, relevance, and personalized shopping experiences, AI personalization has become a strategic differentiator for leading online retailers. This research explores the direct and indirect influences of such personalization on consumer trust, engagement, satisfaction, and purchase intention. A mixed-method approach was employed, comprising primary data through structured surveys and secondary insights from academic literature, industry reports, and platform analytics. The results indicate a strong positive correlation between AI personalization and consumer purchase intention, particularly among digitally savvy demographics. Users reported higher satisfaction, greater emotional connection, and increased likelihood of repeat purchases when interacting with personalized content. However, the study also uncovers critical challenges—especially around data privacy, algorithmic transparency, and personalization fatigue—that may hinder long-term consumer trust. This research offers valuable strategic insights for marketers, digital retailers, and AI developers, emphasizing the need to blend technological precision with ethical design. It concludes that when executed responsibly, AI personalization can serve as a powerful catalyst for deeper consumer relationships, enhanced loyalty, and sustained growth in the competitive online retail landscape.
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Melnykova, Nataliia, Nataliya Shakhovska, Michal Gregus, Volodymyr Melnykov, Mariana Zakharchuk, and Olena Vovk. "Data-Driven Analytics for Personalized Medical Decision Making." Mathematics 8, no. 8 (2020): 1211. http://dx.doi.org/10.3390/math8081211.
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The study was conducted by applying machine learning and data mining methods to treatment personalization. This allows individual patient characteristics to be investigated. The personalization method was built on the clustering method and associative rules. It was suggested to determine the average distance between instances in order to find the optimal performance metrics. The formalization of the medical data preprocessing stage was proposed in order to find personalized solutions based on current standards and pharmaceutical protocols. The patient data model was built using time-dependent and time-independent parameters. Personalized treatment is usually based on the decision tree method. This approach requires significant computation time and cannot be parallelized. Therefore, it was proposed to group people by conditions and to determine deviations of parameters from the normative parameters of the group, as well as the average parameters. The novelty of the paper is the new clustering method, which was built from an ensemble of cluster algorithms, and the usage of the new distance measure with Hopkins metrics, which were 0.13 less than for the k-means method. The Dunn index was 0.03 higher than for the BIRCH (balanced iterative reducing and clustering using hierarchies) algorithm. The next stage was the mining of associative rules provided separately for each cluster. This allows a personalized approach to treatment to be created for each patient based on long-term monitoring. The correctness level of the proposed medical decisions is 86%, which was approved by experts.
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K. Slavkova, Elena. "Exploration of Effective Methodologies for Web Personalization." Postmodernism Problems 13, no. 3 (2023): 321–31. http://dx.doi.org/10.46324/pmp2303321.
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In the dynamic landscape of online interactions, the exploration of effective methodologies for web personalization emerges as a critical step in the digital marketing strategy of a brand. As users navigate through an increasingly vast digital realm, the ability to tailor online experiences becomes pivotal for engaging and retaining diverse audiences. This study delves into the multifaceted realm of web personalization, aiming to understand methodologies that prove efficacious in crafting tailored digital experiences. Key considerations include the strategic deployment of technology, the role of data analytics, and the implementation of a buyer persona to better analyze user behavior. By evaluating diverse approaches, this exploration seeks to unravel best practices that resonate with customers, foster heightened user engagement, and contribute to the evolving landscape of personalized online experiences. Ultimately, this research aims to provide insights that empower businesses and digital platforms to navigate the complexities of web personalization successfully.
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Ikeh, Chioma Onyinye. "AI-Driven Predictive Analytics for Banking Personalization: Enhancing Customer Lifetime Value through Behavioral and Transactional Insights." International Journal of Research Publication and Reviews 6, no. 4 (2025): 12493–508. https://doi.org/10.55248/gengpi.6.0425.15177.
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Yashwant, Salunkhe Rohit, and Khedkar Chetan Eknath. "Personalization and Privacy in Email Marketing." INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN COMMERCE, MANAGEMENT & SOCIAL SCIENCE 08, no. 02(I) (2025): 61–70. https://doi.org/10.62823/ijarcmss/8.2(i).7400.
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Email marketing has emerged as a pivotal tool in digital communication, evolving through the integration of personalized content, advanced technologies, and heightened ethical considerations. This literature review synthesizes empirical research on how personalized strategies—such as tailored subject lines and individualized recommendations—enhance engagement metrics like open and click-through rates, while also potentially triggering privacy concerns if perceived as intrusive. Concurrently, technological advancements, including big data analytics and AI-driven segmentation, offer powerful means to optimize campaign performance but risk diminishing the human touch and authentic brand voice if over-automated. The review further examines the ethical implications of collecting and utilizing consumer data, emphasizing the need for transparency and compliance with regulations such as GDPR. Methodological limitations, including reliance on short-term performance indicators and small-scale datasets, are discussed, highlighting significant gaps in understanding long-term consumer behavior and cross-channel integration. The findings underscore that effective email marketing requires a balanced approach that leverages data-driven insights while maintaining user trust and autonomy. Future research should adopt more robust, longitudinal, and mixed-methods designs to address these gaps and refine strategies that reconcile personalization benefits with ethical data practices.
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Recalde Drouet, Elizabeth Magdalena, David Mauricio Tello Salazar, Tatiana Lizbeth Charro Domínguez, and Pablo Jordán Catota Pinthsa. "Analysis of the repercussions of Artificial Intelligence in the Personalization of the Virtual Educational Process in Higher Education Programs." Data and Metadata 3 (June 27, 2024): 386. http://dx.doi.org/10.56294/dm2024386.
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This study examined how artificial intelligence (AI) has transformed the personalization of the virtual educational process in higher education programs. A systematic review of literature published between 2012 and 2023 was carried out, evaluating empirical studies, reports and review articles available in academic databases such as IEEE Xplore, SpringerLink and Google Scholar. Methods discussed include intelligent tutoring systems, learning analytics, and recommendation systems. The results showed that AI significantly improved the personalization of learning. Intelligent tutoring systems provide real-time adaptive feedback, adjusting content and pacing based on students' individual needs, improving their understanding and retention. Learning analytics helps identify student behavior patterns and predict academic issues, thereby facilitating timely interventions that help improve performance. Additionally, recommender systems personalize study materials based on student preferences and progress, thereby optimizing the educational experience. However, significant challenges have been identified, such as the need to protect data privacy and mitigate algorithmic biases that can affect the fairness and efficiency of these systems. In conclusion, the integration of AI into virtual higher education has enhanced the personalization of learning, improving both student satisfaction and academic performance. However, there is a need to continue to focus on developing ethical and equitable AI systems to address identified issues and maximize educational benefits