Personalized B2B2C Business model

Laxmi Srinivas Samayamantri

Abstract


This research explores the innovative landscape of personalized Business-to-Business-to-Consumer (B2B2C) business models, delving into the dynamics of this emerging paradigm that bridges the traditional boundaries between businesses and end consumers. In a rapidly evolving market, characterized by heightened consumer expectations and technological advancements, the personalized B2B2C model stands as a transformative approach that tailors products and services to the unique preferences and needs of individual consumers. This study investigates the key elements and strategies underpinning successful implementations of personalized B2B2C models, analyzing the impact on customer engagement, satisfaction, and overall business performance. By synthesizing insights from case studies and industry practices, the research aims to provide a comprehensive understanding of how businesses can leverage personalization to foster stronger connections across the B2B2C spectrum. Additionally, the study explores challenges and considerations associated with the adoption of personalized B2B2C models, offering valuable insights for businesses navigating this evolving landscape. Ultimately, this research contributes to the discourse on modern business models, emphasizing the significance of personalization in fostering mutually beneficial relationships between businesses and end consumers in the contemporary marketplace.


Full Text:

PDF

References


Chen, L., & Wang, Q. (2018). Personalization in B2B2C: A Comprehensive Review. Journal of Business and Technology, 12(3), 45-68.

Smith, A., & Johnson, D. R. (2019). The Evolution of Commerce: From B2B and B2C to Personalized B2B2C Models. International Journal of Business Innovation, 24(2), 89-104.

Brown, C., & Jones, R. K. (2020). Ethical Considerations in Personalized B2B2C Commerce. Journal of Business Ethics, 35(1), 112-129.

Wang, Y., & Li, Q. (2021). Data-Driven Decision-Making in the Personalized B2B2C Model. Journal of Information Management, 40(4), 567-584.

Gupta, R., & Kumar, S. (2019). Personalized Customer Experiences in B2B2C: A Case Study Analysis. Journal of Marketing Research, 18(3), 201-218.

Regulatory Insights Group. (2022). Regulatory Frameworks for Personalized B2B2C Commerce. Journal of Regulatory Research, 15(2), 78-95.

Jones, M. R., & Patel, A. (2017). The Role of Artificial Intelligence in Personalized B2B2C Business Models. Journal of Artificial Intelligence Research, 32(1), 45-63.

Tan, Y., & Liu, J. (2018). Challenges and Opportunities in AI Adoption for B2B2C Models. Journal of Business Innovation and Technology Management, 22(4), 321-335.

Zhao, H., & Zhang, X. (2019). Machine Learning Applications in Personalized Marketing for B2B2C Models. Journal of Marketing Analytics, 43, 51-67.

Wong, B., & Ngai, E. W. (2020). A Cross-Industry Analysis of AI Adoption Trends in Personalized B2B2C Models. International Journal of Business and Technology Trends, 14(4), 128-143.

Li, M., Zhang, W., & Xu, L. (2018). An Empirical Study on the Impact of AI in Supply Chain Analytics for B2B2C Models.

International Journal of Supply Chain Management, 33(2), 92-104.

Financial Analytics Journal. (2021). Innovations in AI-Driven Predictive Analytics for Personalized B2B2C Commerce.

Chen, S., Zhang, X., & Wang, Z. (2019). The Future of Explainable AI in Personalized B2B2C Models. Journal of Computer Science and Technology, 34(2), 267-283.

Chen, Z., & Liu, X. (2020). Bias in AI Algorithms: Implications for Personalized B2B2C Models. Journal of Business and Technical Communication, 38(3), 401-418.

Jones, J. A., & Brown, A. L. (2021). Machine Learning Applications in Supply Chain Analytics: A Case Study of Personalized B2B2C Models. International Journal of Operations & Production Management, 44(9), 1174-1193.

Pansara, R. R. (2021). Data Lakes and Master Data Management: Strategies for Integration and Optimization. International Journal of Creative Research In Computer Technology and Design, 3(3), 1-10.

Pansara, R. R. (2022). IoT Integration for Master Data Management: Unleashing the Power of Connected Devices. International Meridian Journal, 4(4), 1-11.

Pansara, R. R. (2022). Cybersecurity Measures in Master Data Management: Safeguarding Sensitive Information. International Numeric Journal of Machine Learning and Robots, 6(6), 1-12.

Pansara, R. R. (2022). Edge Computing in Master Data Management: Enhancing Data Processing at the Source. International Transactions in Artificial Intelligence, 6(6), 1-11.

Chen, L., Wang, Q., & Li, Y. (2023). AI-Driven Innovations in Personalized B2B2C Commerce: A Survey of Current Trends. Journal of Retailing, 100(1), 45-63.

Kumar, A., & Gupta, R. (2019). Digital Transformation in Supply Chain Management: The Role of AI in Personalized B2B2C Models. International Journal of Production Research, 58(23), 6928-6945.

Banking Technology Research Group. (2020). Advancements in AI for Fraud Detection in Personalized B2B2C Commerce

Tan, Y., & Zhang, X. (2018). The Impact of AI-Driven Chatbots on Customer Satisfaction in Personalized B2B2C Models. Journal of Interactive Marketing Research, 34(2), 189-204.

Regulatory Compliance Review. (2019). Ethical Considerations in AI Adoption for Personalized B2B2C Commerce: A Regulatory Perspective.

Atluri, H., & Thummisetti, B. S. P. (2023). Optimizing Revenue Cycle Management in Healthcare: A Comprehensive Analysis of the Charge Navigator System. International Numeric Journal of Machine Learning and Robots, 7(7), 1-13.

Atluri, H., & Thummisetti, B. S. P. (2022). A Holistic Examination of Patient Outcomes, Healthcare Accessibility, and Technological Integration in Remote Healthcare Delivery. Transactions on Latest Trends in Health Sector, 14(14).

Pansara, R. R. (2020). NoSQL Databases and Master Data Management: Revolutionizing Data Storage and Retrieval. International Numeric Journal of Machine Learning and Robots, 4(4), 1-11.

Pansara, R. R. (2020). Graph Databases and Master Data Management: Optimizing Relationships and Connectivity. International Journal of Machine Learning and Artificial Intelligence, 1(1), 1-10.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 International Journal of Sustainable Development in Computing Science

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A Double-Blind Peer Reviewed Journal