Smart Data Systems for Retail Personalization
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. Adomavicius, G., Bockstedt, J., Curley, S., & Zhang, J. (2021). Effects of personalized and aggregate top-N recommendation lists on user preference ratings. ACM Transactions on Information Systems, 39(2), Article 13.
2. Al-Ghossein, M., Abdessalem, T., & Barre, A. (2021). A survey on stream-based recommender systems. ACM Computing Surveys, 54(5), Article 104.
3. Dadoun, A., Defoin-Platel, M., Fiig, T., Landra, C., & Troncy, R. (2021). How recommender systems can transform airline offer construction and retailing. Journal of Revenue and Pricing Management, 20(3), 301–315.
4. Kolla, S. K. (2022). Engineering Healthcare Data Infrastructures for Predictive Clinical Analytics and Evidence-Based Decision Making. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(5), 5370-5380.
5. Gao, C., Zheng, Y., Li, N., Li, Y., Qin, Y., Piao, J., Quan, Y., Chang, J., Jin, D., He, X., & Li, Y. (2021). A survey of graph neural networks for recommender systems: Challenges, methods, and directions. ACM Transactions on Information Systems.
6. Li, X., Grahl, J., & Hinz, O. (2021). How do recommender systems lead to consumer purchases? A causal mediation analysis of a field experiment. Information Systems Research, 33(2), 620–637.
7. Pallant, J. I., Pallant, J. L., Sands, S. J., Ferraro, C. R., & Afifi, E. (2022). When and how consumers are willing to exchange data with retailers: An exploratory segmentation. Journal of Retailing and Consumer Services, 64, Article 102774.
8. Sitar-Tăut, D.-A., Mican, D., & Buchmann, R. A. (2021). A knowledge-driven digital nudging approach to recommender systems built on a modified Onicescu method. Expert Systems with Applications, 181, Article 115170.
9. Kolla, S. H. (2022). Strategic Information Integration Models for Cross-Functional Service Optimization in Large-Scale Enterprises. International Journal of Emerging Trends in Engineering and Management Research, 7(3), 11811.
10. Silva, D. C. da, Manzato, M. G., & Durão, F. A. (2021). Exploiting personalized calibration and metrics for fairness recommendation. Expert Systems with Applications, 181, Article 115112.
11. Song, Y. W. G., & Oh, J. (2021). “We think you may like this”: An investigation of electronic commerce personalization for privacy-conscious consumers. Psychology & Marketing, 38(10), 1723–1740.
12. Steck, H. (2021). Deep learning for recommender systems: A Netflix case study. AI Magazine, 42(3).
13. Tian, Z., Liu, Y., Sun, J., Jiang, Y., & Zhu, M. (2022). Exploiting group information for personalized recommendation with graph neural networks. ACM Transactions on Information Systems, 40(2), 1–23.
14. Venkatesh, V., Hoehle, H., Aloysius, J. A., & Nikkhah, H. R. (2021). Being at the cutting edge of online shopping: Role of recommendations and discounts on privacy perceptions. Computers in Human Behavior, 121, Article 106785.
15. Wang, S., Cao, L., Wang, Y., Sheng, Q. Z., Orgun, M. A., & Lian, D. (2021). A survey on session-based recommender systems. ACM Computing Surveys, 54(7), Article 154.
16. Wang, C., Zhu, H., Wang, P., Zhu, C., Zhang, X., Chen, E., & Xiong, H. (2022). Personalized and explainable employee training course recommendations: A Bayesian variational approach. ACM Transactions on Information Systems, 40(4), Article 70.
17. Wang, C., Ma, W., Zhang, M., Chen, C., Liu, Y., & Ma, S. (2021). Toward dynamic user intention: Temporal evolutionary effects of item relations in sequential recommendation. ACM Transactions on Information Systems, 39(2), 1–27.
18. Davuluri, P. S. L. N. (2021). Event-Driven Compliance Systems: Modernizing Financial Crime Detection Without Machine Intelligence. Journal of International Crisis and Risk Communication Research, 339-354.
19. Yoon, N., & Lee, H.-K. (2021). AI recommendation service acceptance: Assessing the effects of perceived empathy and need for cognition. Journal of Theoretical and Applied Electronic Commerce Research, 16(5), 1912–1928.
20. Zangerle, E., & Bauer, C. (2022). Evaluating recommender systems: Survey and framework. ACM Computing Surveys, 55(8), 1–38, Article 170.
21. Zehlike, M., Yang, K., & Stoyanovich, J. (2022). Fairness in ranking, Part II: Learning-to-rank and recommender systems. ACM Computing Surveys, 55(6), Article 117, 1–41.
22. Zhou, B., & Zou, T. (2022). Competing for recommendations: The strategic impact of personalized product recommendations in online marketplaces. Marketing Science, 42(2), 360–376.
23. Chabane, N., Bouaoune, A., Tighilt, R., Abdar, M., Boc, A., Lord, E., et al. (2022). Intelligent personalized shopping recommendation using clustering and supervised machine learning algorithms. PLOS ONE, 17(12), Article e0278364.
24. Yandamuri, U. S. (2021). A Comparative Study of Traditional Reporting Systems versus Real-Time Analytics Dashboards in Enterprise Operations. Universal Journal of Business and Management, 1(1), 1-13.
25. Molaie, M. M., & Lee, W. (2022). Economic corollaries of personalized recommendations. Journal of Retailing and Consumer Services, 68, Article 103003.
26. Tomczyk, A. T., Buhalis, D., Fan, D. X. F., & Williams, N. L. (2022). Price-personalization: Customer typology based on hospitality business. Journal of Business Research, 147, 462–476.
27. Pizzi, G., Vannucci, V., Shukla, Y., & Aiello, G. (2022). Privacy concerns and justice perceptions with the disclosure of biometric versus behavioral data for personalized pricing: Tell me who you are, I’ll tell you how much you pay. Consumers’ fairness and privacy perceptions with personalized pricing. Journal of Business Research, 148, 420–432.
28. Whang, J.-B., Song, J. H., Lee, J.-H., & Choi, B. (2022). Interacting with chatbots: Message type and consumers’ control. Journal of Business Research, 153, 309–318.
29. Mangalampalli, B. M. "Scalable Data Warehouse Architecture for Population Health Management and Predictive Analytics." World Journal of Clinical Medicine Research 1, no. 1 (2021): 1-18.
30. Bartholomew, D. E., & Williamson, M. (2022). Retail media networks. Journal of Retailing and Consumer Services, 69, Article 103119.
Additional Files
Published
Data Availability Statement
None
Issue
Section
License
Copyright (c) 2023 Daniel Thompson (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
