Adaptive Data Ecosystems for AI-Driven Retail Intelligence
Keywords:
Intelligent Data Platforms (IDPs), Customer Journey Personalization, Automated Data-Driven Experiences, Customer Data Integration, Customer 360 Generation, Segmentation and Profiling, Behavior-Based Personalization, Experimentation Frameworks, Data Quality and Lineage, Privacy and Trust Applications, Enterprise Data Governance, Retail Personalization Architecture, Personalized Breadth Depth and Freshness, Customer-Centric Analytics, Demand-Driven Personalization, Multidimensional Segmentation, Omnichannel Experience Delivery, Scalable Personalization Platforms, IT Landscape Integration, Experience Optimization.Abstract
Intelligent Data Platforms (IDPs) represent a transformative concept for enterprises striving to continually personalize product, message, and experience offerings across the customer journey, at scale. The notion reflects the data and analytic capabilities enabling organizations to drive personalization through automated, data-driven processes dedicated to delivering tailored experiences across all interactions with customers—both online and offline.
These platforms incorporate specialized components for customer data integration, customer 360-degree generation, segmentation and profiling, behavior-based personalization, experimentation, and trust-related applications—ranging from data quality and lineage to compliance with privacy policies. They integrate with the IT landscape for support functions such as enterprise service management and data governance, thereby helping retail organizations establish processes for a systemic delivery of personalized experiences.
Alongside a description of the key components of an IDP, the relevant wholesale architecture principles for supporting personalized breadth, depth, and freshness of individual offerings are explored. The discussion covers the essential ability to exploit customer-centric personalization implicitly throughout a retail business model—alongside demand-driven forms of personalization involving active multidimensional segmentation.
References
1. Hudnurkar, M., & Dhanesh, G. (2021). Experiential retailing leveraged by data analytics. International Journal of Business Intelligence Research, 12(1), 1–16.
2. Zeng, A., Yu, H., He, H., Ni, Y., Li, Y., Zhou, J., & Miao, C. (2021). Enhancing e-commerce recommender system adaptability with online deep controllable learning-to-rank. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15214–15222.
3. Sriram, H. K., Challa, K., Gadi, A. L., & Singreddy, S. (2025). AI and Cloud-Driven Transformation in Finance, Insurance, and the Automotive Ecosystem: A Multi-Sectoral Framework for Credit Risk, Mobility Services, and Consumer Protection. Anil Lokesh and singreddy, Sneha, AI and Cloud-Driven Transformation in Finance, Insurance, and the Automotive Ecosystem: A Multi-Sectoral Framework for Credit Risk, Mobility Services, and Consumer Protection (March 15, 2025).
4. Zeng, D., Liu, Y., Yan, P., & Yang, Y. (2021). Location-aware real-time recommender systems for brick-and-mortar retailers. INFORMS Journal on Computing, 33(4), 1608–1623.
5. Viniski, A. D., Barddal, J. P., Britto, A. de S., Enembreck, F., & de Campos, H. V. A. (2021). A case study of batch and incremental recommender systems in supermarket data under concept drifts and cold start. Expert Systems with Applications, 176, 114890.
6. Seenu, A., Sheelam, G. K., Motamary, S., Meda, R., Koppolu, H. K. R., & Inala, R. (2025). AI-Driven Innovations in Infrastructure Management with 6G Technology. In 2025 2nd International Conference on Computing and Data Science (ICCDS) (pp. 1–6). IEEE. 2025 2nd International Conference on Computing and Data Science (ICCDS). https://doi.org/10.1109/iccds64403.2025.11209649
7. Ahmed, A., Saleem, K., Khalid, O., & Rashid, U. (2021). On deep neural network for trust aware cross domain recommendations in e-commerce. Expert Systems with Applications, 174, 114757.
8. Li, X., Grahl, J., & Hinz, O. (2022). How do recommender systems lead to consumer purchases? A causal mediation analysis of a field experiment. Information Systems Research, 33(2), 620–637.
9. Narasareddy Annapareddy, V., Pamisetty, A., Malempati, M., Kaulwar, P. K., & Bhardwaj Komaragiri, V. (2025, April). Enhancing Solar Power System Efficiency Through AI-Driven Predictive Maintenance and Cloud-Based Infrastructure Stability Solutions. In International Conference on Smart Computing and Informatics (pp. 328-337). Cham: Springer Nature Switzerland.
10. Fildes, R., Kolassa, S., & Ma, S. (2022). Post-script—Retail forecasting: Research and practice. International Journal of Forecasting, 38(4), 1319–1324.
11. Lutfi, A., Alrawad, M., Alsyouf, A., Almaiah, M. A., Al-Khasawneh, A., Al-Khasawneh, A. L., Alshira’h, A. F., Alshirah, M. H., Saad, M., & Ibrahim, N. (2023). Drivers and impact of big data analytic adoption in the retail industry: A quantitative investigation applying structural equation modeling. Journal of Retailing and Consumer Services, 70, 103129.
12. Dutta, P., Adusupalli, B., Koppolu, H. K. R., Dodda, A., Yağanoğlu, M., Banerjee, J. S., & Chakraborty, A. (2025, March). Textual Social Data Disinformation Analysis Using a Hybrid Context-Enhanced Deep Learning Model. In Doctoral Symposium on Human Centered Computing (pp. 342-352). Singapore: Springer Nature Singapore.
13. Pascucci, F., Nardi, L., Marinelli, L., Paolanti, M., Frontoni, E., & Gregori, G. L. (2022). Combining sell-out data with shopper behaviour data for category performance measurement: The role of category conversion power. Journal of Retailing and Consumer Services, 65, 102880.
14. Hallikainen, H., Luongo, M., Dhir, A., & Laukkanen, T. (2022). Consequences of personalized product recommendations and price promotions in online grocery shopping. Journal of Retailing and Consumer Services, 69, 103088.
15. Challa, S. R., Burugulla, J. K. R., Pamisetty, A., Challa, K., & Paleti, S. (2025, April). AI and ML-Powered Cybersecurity Strategies for Cloud Computing: Ensuring Infrastructure Stability in Financial and Retail Sectors. In International Conference on Smart Computing and Informatics (pp. 315-327). Cham: Springer Nature Switzerland.
16. Wang, Z., Wang, L., Ji, Y., Zuo, L., & Qu, S. (2022). A novel data-driven weighted sentiment analysis based on information entropy for perceived satisfaction. Journal of Retailing and Consumer Services, 68, 103038.
17. Saha, P., Gudheniya, N., Mitra, R., Das, D., Narayana, S., & Tiwari, M. K. (2022). Demand forecasting of a multinational retail company using deep learning frameworks. IFAC-PapersOnLine, 55(10), 395–399.
18. Davuluri, P. S. L. (2023). AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems (December 15, 2023).
19. Joseph, R. V., Mohanty, A., Tyagi, S., Mishra, S., et al. (2022). A hybrid deep learning framework with CNN and Bi-directional LSTM for store item demand forecasting. Computers & Electrical Engineering, 103, 108358.
20. Zhang, Q., Lu, J., & Jin, Y. (2021). Artificial intelligence in recommender systems. Complex & Intelligent Systems, 7, 439–457.
21. Singireddy, J., Kaulwar, P. K., Somu, B., Meda, R., Dodda, A., & Yellanki, S. K. (2025, August). Reinforcement Learning-Based Asset Allocation in Algorithmic Trading for Banking Institutions. In International Conference on Artificial Intelligence: Theory and Applications (pp. 357-369). Cham: Springer Nature Switzerland.
22. Huang, M.-H., & Rust, R. T. (2021). Engaged to a robot? The role of AI in service. Journal of Service Research, 24(1), 30–41.
23. Muhammad, A., Yu, C. K., Qadir, A., Ahmed, W., Yousuf, Z., & Fan, G. (2022). Big data analytics capability as a major antecedent of firm innovation performance. Journal of Organizational Computing and Electronic Commerce, 32(4), 270–289.
24. Brackmann, C., Hütsch, M., & Wulfert, T. (2023). Identifying application areas for machine learning in the retail sector: A literature review and interview study. SN Computer Science, 4, 426.
25. Ande, R., Mehta, D., Pandugula, C., Krishna AzithTejaGanti, V., & Kalisetty, S. (2025). Modeling the Som Kamla Amba sub-watershed using Artificial Neural Networks (ANN) and the SWAT tool. Available at SSRN 5341755.
26. Deng, Y., Zhang, X., Wang, T., Wang, L., Zhang, Y., Wang, X., Zhao, S., Qi, Y., Yang, G., & Peng, X. (2023). Alibaba realizes millions in cost savings through integrated demand forecasting, inventory management, price optimization, and product recommendations. INFORMS Journal on Applied Analytics, 53(1), 32–46.
27. Ahmadov, Y., & Helo, P. (2023). Deep learning-based approach for forecasting intermittent online sales. Discover Artificial Intelligence, 3, 45.
28. ADUSUPALLI, B. (2025). Redefining Financial Risk Strategies: The Integration of Smart Automation, Secure Access Systems, and Predictive Intelligence in Insurance, Lending, and Asset Management. JAIBDD.
29. Pande, A., Ghosh, K., & Park, R. (2023). Personalized category frequency prediction for Buy It Again recommendations. Proceedings of the 17th ACM Conference on Recommender Systems, 730–736.
30. Kamoonpuri, S. Z., & Sengar, A. (2023). Hi, May AI help you? An analysis of the barriers impeding the implementation and use of artificial intelligence-enabled virtual assistants in retail. Journal of Retailing and Consumer Services, 72, 103258.
31. Jan, I. U., Ji, S., & Kim, C. (2023). What (de)motivates customers to use AI-powered conversational agents for shopping? The extended behavioral reasoning perspective. Journal of Retailing and Consumer Services, 75, 103440.
32. KOTHAPALLI, S. L., SEENU, A., DILEEP, V., & YASMEEN, Z. (2025). THE FUTURE OF CUSTOMER ENGAGEMENT IN RETAIL BANKING: EXPLORING THE POTENTIAL OF AUGMENTED REALITY AND IMMERSIVE TECHNOLOGIES. INTERNATIONAL JOURNAL, 73(1), 72-79.
33. Fu, H.-P., Chang, T.-H., Lin, S.-W., & Teng, Y.-H. (2023). Evaluation and adoption of artificial intelligence in the retail industry. International Journal of Retail & Distribution Management, 51(6), 773–790.
34. Agag, G., Shehawy, Y. M., Almoraish, A., Eid, R., Chaib Lababdi, H., Gherissi Labben, T., & Abdo, S. S. (2024). Understanding the relationship between marketing analytics, customer agility, and customer satisfaction: A longitudinal perspective. Journal of Retailing and Consumer Services, 77, 103663.
35. Challa, S. R., Kaulwar, P. K., Koppolu, H. K. R., Adusupalli, B., & Suura, S. R. (2025, April). Big Data and AI in Wealth Management: Leveraging Cloud Computing for Secure and Scalable Financial Infrastructure. In International Conference on Smart Computing and Informatics (pp. 307-318). Cham: Springer Nature Switzerland.
36. Alghamdi, O. A., & Agag, G. (2024). Competitive advantage: A longitudinal analysis of the roles of data-driven innovation capabilities, marketing agility, and market turbulence. Journal of Retailing and Consumer Services, 76, 103547.
37. Vhatkar, M. S., Raut, R. D., Gokhale, R., Kumar, M., Akarte, M., & Ghoshal, S. (2024). Leveraging digital technology in retailing business: Unboxing synergy between omnichannel retail adoption and sustainable retail performance. Journal of Retailing and Consumer Services, 81, 104047.
38. Aversa, J., Azmy, A., & Hernandez, T. (2024). Untapping the potential of mobile location data: The opportunities and challenges for retail analytics. Journal of Retailing and Consumer Services, 81, 103? .
39. Pandugula, C., Ganti, V. K. A. T., Kalisetty, S., Ande, R., & Mehta, D. (2025). Modeling the Som Kamla Amba Sub-Watershed Using Artificial Neural Networks (ANN) and the SWAT Tool. Available at SSRN 5341614.
40. Grewal, D., Roggeveen, A. L., Benoit, S., Osorio Andrade, M. L., Wetzels, R., & Wetzels, M. (2024). A new era of technology-infused retailing. Journal of Business Research, 176, 114737.
41. Srivastava, K., & Pal, D. (2024). Importance of AI attributes in Indian retail stores: A conjoint analysis approach. International Journal of Retail & Distribution Management, 52(3), 355–371.
42. Vadisetty, R., Nuka, S. T., Kalisetty, S., Pandugula, C., Burugulla, J. K. R., & Annapareddy, V. N. (2025, February). Generative AI for Advanced Recycling Processes in Polyethylene and Polypropylene Manufacturing. In International Ethical Hacking Conference (pp. 269-284). Singapore: Springer Nature Singapore.
43. Gupta, A. S., & Mukherjee, J. (2024). Framework for adoption of generative AI for information search of retail products and services. International Journal of Retail & Distribution Management, 53(2), 165–181.
44. Kholod, M., Celani, A., & Ciaramella, G. (2024). The analysis of customers’ transactions based on POS and RFID data using big data analytics tools in the retail space of the future. Applied Sciences, 14(24), 11567.
45. Li, J., & Liu, X. (2024). An agent-based simulation model for analyzing and optimizing omni-channel retailing operation decisions. Journal of Retailing and Consumer Services, 79, 103? .
46. Maguluri, K. K. (2025). Ethical challenges in artificial intelligence. How Artificial Intelligence is Transforming Healthcare IT: Applications in Diagnostics, Treatment Planning, and Patient Monitoring, 132.
47. Chakraborty, D., Kar, A. K., Patre, S., & Gupta, S. (2024). Enhancing trust in online grocery shopping through generative AI chatbots. Journal of Business Research, 180, 114737.
48. Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2024). Generative AI. Business & Information Systems Engineering, 66, 111–126.
49. Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642.
50. Welbotha, D., & van den Berg, C. (2024). Big data analytics capabilities and the organisational performance of South African retailers. South African Computer Journal, 36(2).
51. Singireddy, S., Pandiri, L., Motamary, S., Kummari, D. N., Somu, B., & Lakkarasu, P. (2025, August). Blockchain-Powered Claims Validation for Enhancing Trust in Health and Auto Insurance Ecosystems. In International Conference on Artificial Intelligence: Theory and Applications (pp. 26-38). Cham: Springer Nature Switzerland.
52. Kameswari, J., Ramesh, P., Bhavikatti, V., Omnamasivaya, B., Chaitanya, G., Bastray, T., Hiremath, S., & Gondesi, S. S. (2024). Analyzing the role of big data and its effects on the retail industry. Web Intelligence, 22(1), 45–63.
53. Matharoo, S. (2024). Transforming retail: Elevating customer experience and efficiency with generative AI technique. Asian Journal of Research in Computer Science, 17(12), 179–184.
54. Kim, D. J., & Cha, S. S. (2025). Digital transformation in retail: A systematic review on omnichannel, AI, and big data. Journal of Distribution Science, 23(6), 111–124.
55. Velásquez, J. D. (2025). An analysis of trends, challenges, and opportunities in retail analytics. Marketing Intelligence & Planning, 67(4).
56. Braving digital retail frontier through artificial intelligence: Rhetoric, reality, institutionalization. (2025). International Journal of Retail & Distribution Management, 53(6), 485–499.
57. Human-centred AI vs machine-centred AI: Navigating AI ethical crises in luxury retail. (2025). International Journal of Retail & Distribution Management, 53(7), 639–653.
58. Highly autonomous AI impact on the demand for physical goods. (2025). International Journal of Retail & Distribution Management, 54(2), 167–182.
59. Improved collaborative filtering for cross-store demand forecasting. (2024). Computers & Industrial Engineering, 190, 110067.
60. Optimizing retail operations through hybrid machine learning and deep learning techniques in demand forecasting. (2025). Cybernetics and Systems, 57(1), 1–43.
Additional Files
Published
Issue
Section
License
Copyright (c) 2025 Dimitris Plexousakis (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.
The American Online Journal of Science and Engineering (AOJSE) is an open-access journal, and all published articles are made freely available to readers worldwide under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license governs the use, sharing, adaptation, and distribution of all content published in AOJSE and is designed to promote the broadest possible dissemination and reuse of scholarly research.
Creative Commons Attribution 4.0 International License (CC BY 4.0)
Under the Creative Commons Attribution 4.0 International License, any user is free to share, copy, and redistribute the published material in any medium or format, and to adapt, remix, transform, and build upon the material for any purpose, including commercial use, provided that appropriate credit is given to the original authors and source. The license cannot be revoked as long as the terms are followed.
When using or redistributing content published in AOJSE, users must provide proper attribution by citing the original authors' names, the article title, the journal name (AOJSE), the volume and issue number, the year of publication, and the DOI or URL of the article. Users must also indicate if any changes were made to the original work. Attribution must be provided in a reasonable manner but must not suggest that the authors or AOJSE endorse the user or their use of the material.
Author Rights and Copyright Retention
AOJSE respects and upholds the intellectual property rights of all contributing authors. Authors who publish in AOJSE retain full copyright over their work. By submitting a manuscript to AOJSE, authors grant the journal a non-exclusive, worldwide, royalty-free license to publish, reproduce, distribute, publicly display, and archive the article in print and electronic formats. This license allows AOJSE to make the article freely accessible to readers globally while ensuring that the authors remain the rightful owners of their work.
Authors are permitted to deposit their published articles in institutional repositories, personal websites, academic networking platforms, and preprint servers, provided that the original publication in AOJSE is acknowledged and properly cited. Authors may also reuse their published content in subsequent works, presentations, teaching materials, and grant applications without requiring prior permission from the journal.
Third-Party Content
Authors are solely responsible for obtaining written permission to reproduce any third-party material, including figures, tables, images, data, or excerpts from other publications, included in their manuscript. Evidence of such permissions must be provided to the editorial office upon request. AOJSE does not assume any responsibility for copyright infringement arising from the unauthorized inclusion of third-party content in published articles.
Permitted Uses
Under the CC BY 4.0 license, the following uses of AOJSE published content are freely permitted without prior written permission from the journal or authors, provided that proper attribution is given. Users may read, download, print, and distribute articles for personal, educational, or research purposes. Researchers may reuse data, figures, and findings for meta-analyses, systematic reviews, and secondary research. Educators may incorporate published articles into course materials, syllabi, and academic presentations. Journalists, policymakers, and practitioners may reference and cite published findings for professional and public interest purposes.
Prohibited Uses
While the CC BY 4.0 license is broad and permissive, users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. Users may not misrepresent the original authorship of published content or imply endorsement by the original authors or AOJSE without explicit consent. Plagiarism, data fabrication, and any form of academic misconduct in the reuse of published material are strictly prohibited and are a violation of publication ethics standards.
Institutional and Repository Deposit Policy
AOJSE fully supports self-archiving and green open access. Authors are encouraged to deposit the final published version of their article, also known as the version of record, in institutional repositories, subject repositories, and open-access databases immediately upon publication. There is no embargo period. Authors should always link back to the original article on the AOJSE website and include the full citation and DOI when depositing their work in any repository.
Digital Preservation and Archiving
AOJSE is committed to the long-term digital preservation of all published content to ensure its permanent availability and accessibility. The journal utilizes the Open Journal Systems (OJS) platform, which supports internationally recognized digital archiving standards. AOJSE encourages participation in archiving networks such as LOCKSS (Lots of Copies Keep Stuff Safe) and CLOCKSS (Controlled LOCKSS) to safeguard published articles against data loss and ensure continued access for future generations of researchers and readers.
Disclaimer
The views, opinions, findings, and conclusions expressed in articles published in AOJSE are solely those of the authors and do not necessarily reflect the official position, policy, or views of the journal, its editorial board, or its affiliated organizations. AOJSE makes every effort to ensure the accuracy and integrity of published content but does not accept legal responsibility for any errors, omissions, or claims arising from the use of information published in the journal.
Contact for Licensing Inquiries
For any questions regarding the licensing terms, copyright, or permitted use of content published in AOJSE, please contact the editorial office through the journal website at https://aojse.org. The editorial team will be happy to assist authors, readers, and institutions with any licensing-related queries.