Relevant bibliographies by topics / Personalization Analytics

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Personalization Analytics'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Personalization Analytics"

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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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Gogokhia, Temur, and Luka Nadirashvili. "Data Analytics and Personalization: The Future of Successful Marketing." International Journal of Social Sciences 5, no. 1 (2025): 131–51. https://doi.org/10.55367/ujui5930.

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Modern marketing increasingly relies on data analytics and personalization, which have become critical factors for enhancing customer experience and maintaining brand competitiveness. Data analytics enables companies to better understand consumer behavior, predict their needs, and develop highly targeted marketing strategies. At the same time, personalization has evolved from basic segmentation to hyper-personalization, where artificial intelligence and machine learning generate unique offers tailored to individual users. From Amazon’s recommendation system to Netflix’s content personalization
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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.
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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-dri
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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
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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 BD
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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
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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
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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
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Dissertations / Theses on the topic "Personalization Analytics"

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Munyengeterwa, Tariro S. "The Impact of Artificial Intelligence in the Customer Journey: A Case Study of Bosch USA and Defy South Africa." Digital Commons @ East Tennessee State University, 2021. https://dc.etsu.edu/etd/3870.

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Artificial intelligence (AI) continues to gain traction and is increasingly reshaping the media and marketing communications field. While significant research has been conducted on the impact of AI in other fields, there is little empirical evidence on how AI is affecting the customer journey. The present study explored both organizations’ current use of AI tools and how customer perceptions about AI affect AI usage and adoption through the lens of diffusion of innovation theory. The research was conducted using mixed-method qualitative research. In-depth interviews and a case study content an
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Books on the topic "Personalization Analytics"

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Bucy, Erik P., and Patrick Stewart. The Personalization of Campaigns: Nonverbal Cues in Presidential Debates. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228637.013.52.

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Nonverbal cues are important elements of persuasive communication whose influence in political debates are receiving renewed attention. Recent advances in political debate research have been driven by biologically grounded explanations of behavior that draw on evolutionary theory and view televised debates as contests for social dominance. The application of biobehavioral coding to televised presidential debates opens new vistas for investigating this time-honored campaign tradition by introducing a systematic and readily replicated analytical framework for documenting the unspoken signals tha
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Book chapters on the topic "Personalization Analytics"

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Schneider, Johannes, and Michalis Vlachos. "Personalization of Deep Learning." In Data Science – Analytics and Applications. Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-32182-6_14.

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Solberg Söilen, Klaus. "Customer Analytics; Profiles, Personas, Personalization." In Springer Texts in Business and Economics. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-69518-6_4.

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Beheshti, Amin, Samira Ghodratnama, Mehdi Elahi, and Helia Farhood. "Social Data and Recommender Systems: The Future of Personalization." In Social Data Analytics. CRC Press, 2022. http://dx.doi.org/10.1201/9781003260141-8.

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Wang, Qihua, and Hongxia Jin. "Social Analytics for Personalization in Work Environments." In Web-Age Information Management. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23535-1_28.

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Bennett, Mike, and Aaron Quigley. "Creating Personalized Digital Human Models of Perception for Visual Analytics." In User Modeling, Adaption and Personalization. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22362-4_3.

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Singh, Gyanendra, Himanshu Singh, and Sonika Shriwastav. "Improving Email Marketing Campaign Success Rate Using Personalization." In Advances in Analytics and Applications. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1208-3_8.

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Amyrotos, Christos, Panayiotis Andreou, Panagiotis Germanakos, and Irene Polycarpou. "Personalization and Individual Differences in Business Data Analytics." In Human–Computer Interaction Series. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-55109-3_5.

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Samarawickrama, Sameendra, Shanika Karunasekera, Aaron Harwood, and Ramamohanarao Kotagiri. "Search Result Personalization in Twitter Using Neural Word Embeddings." In Big Data Analytics and Knowledge Discovery. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64283-3_18.

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Liu, Danny Yen-Ting, Kathryn Bartimote-Aufflick, Abelardo Pardo, and Adam J. Bridgeman. "Data-Driven Personalization of Student Learning Support in Higher Education." In Learning Analytics: Fundaments, Applications, and Trends. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52977-6_5.

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Zhang, Xiaoyan, and Zhuoxiang Liu. "Research and Application of Online Learning Platform Resource Recommendation Based on Personalization." In Learning and Analytics in Intelligent Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-95252-4_23.

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Conference papers on the topic "Personalization Analytics"

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Al Balushi, Maha Yaqoob, and Alia Salim Al Harthi. "Adaptive Learning Pathways: Advancing Personalization in Online Education Platforms." In 2024 2nd International Conference on Computing and Data Analytics (ICCDA). IEEE, 2024. https://doi.org/10.1109/iccda64887.2024.10867273.

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Hosain, Md Tanzib, Asif Zaman, Md Shahriar Sajid, Shadman Sakeeb Khan, and Shanjida Akter. "Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learning." In 2023 4th International Conference on Data Analytics for Business and Industry (ICDABI). IEEE, 2023. http://dx.doi.org/10.1109/icdabi60145.2023.10629638.

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M, Vaneeta, Kusuma T, Shruthi G, Avanish Rao BS, Nikhil Anand, and Abhishek M. "Data Analytics on Blitz E-sports: Enhancing Gaming Experience through Personalization and Prediction." In 2025 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE). IEEE, 2025. https://doi.org/10.1109/iitcee64140.2025.10915318.

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Shukla, Tushar Dhar, Shaik Balkhis Banu, Malik Bader Alazzam, Vijay Kumar, Ajmeera Kiran, and A. Balakumar. "Revolutionizing Athletic Training with Machine Learning: Injury Prediction Using Predictive Analytics and Customized Workouts Through Personalization Algorithms." In 2025 3rd International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC). IEEE, 2025. https://doi.org/10.1109/isacc65211.2025.10969283.

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Lopes, Cláudio, Bruno Cabral, and Jorge Bernardino. "Personalization using Big Data Analytics Platforms." In the Ninth International C* Conference. ACM Press, 2016. http://dx.doi.org/10.1145/2948992.2949000.

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Sy, Bon, Jin Chen, Magdalen Beiting-Parrish, and Connor Brown. "Behavioral Predictive Analytics towards Personalization for Self-management." In 14th International Conference on Health Informatics. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010231801110121.

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Amyrotos, Christos, Panayiotis Andreou, and Panagiotis Germanakos. "Human-centred Persona Driven Personalization in Business Data Analytics." In UMAP '21: 29th ACM Conference on User Modeling, Adaptation and Personalization. ACM, 2021. http://dx.doi.org/10.1145/3450614.3462241.

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Lang, Charles. "Opportunities for personalization in modeling students as Bayesian learners." In LAK '17: 7th International Learning Analytics and Knowledge Conference. ACM, 2017. http://dx.doi.org/10.1145/3027385.3027410.

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Saeed, Aaqib, Tanir Ozcelebi, Johan Lukkien, Jan B. F. van Erp, and Stojan Trajanovski. "Model Adaptation and Personalization for Physiological Stress Detection." In 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2018. http://dx.doi.org/10.1109/dsaa.2018.00031.

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Germanakos, Panagiotis, Zacharias Lekkas, Christos Amyrotos, and Panayiotis Andreou. "Proposing a Perceived Expertise Tool in Business Data Analytics." In UMAP '21: 29th ACM Conference on User Modeling, Adaptation and Personalization. ACM, 2021. http://dx.doi.org/10.1145/3450614.3462236.

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Reports on the topic "Personalization Analytics"

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Pasupuleti, Murali Krishna. AI-Driven Marketing Innovations: Personalization and Ethics in the Digital Era. National Education Services, 2025. https://doi.org/10.62311/nesx/rr625.

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Abstract: This article explores the transformative impact of artificial intelligence (AI) on digital marketing, focusing on strategies for delivering personalized content and ensuring ethical advertising. By leveraging AI, marketers can now analyze consumer behavior with precision, enabling targeted content, automated ad placement, and real-time adjustments that enhance user engagement and conversions. The Article examines foundational AI techniques, such as recommendation engines, predictive analytics, and natural language processing, which drive personalization at scale. Additionally, it add
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Spirin, Oleg, and Mariia Shyshkina. Artificial Intelligence in Education and Educational Research: Challenges, Risks, and Prospects for Integration. Institute for Digitalisation of Education of the NAES of Ukraіne, 2025. https://doi.org/10.33407/lib.naes.id/eprint/745119.

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Artificial intelligence (AI) is playing an increasingly important role in education, contributing to the personalization of learning, the automation of administrative processes and educational research. The use of AI allows for increased learning efficiency, improved management of educational resources and improved analytics of educational data. However, the integration of AI into education is accompanied by challenges related, in particular, to technical limitations, pedagogical risks, ethical aspects and security issues. Future research should focus on the integration of AI and augmented and
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Journal articles
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