Abstract
Benefits of customer support and service always prevail in the highly competitive banking industry. Customer segmentation underpins these values by grouping customers in accordance with their common needs and behaviors. By doing so, banks can apply their resources more efficiently and can act more proactively in improving segments that have been identified as profitable. The lack of understanding of customer behavior may lead to the waste of resources and opportunities. Establishing and recognizing the profiles of customer segments, therefore, always receives special attention to adopt better marketing strategies and communications. Similar to understanding the usage of products or services, knowledge of customer preferences and choices may lead to a high competitive edge. Tailored financial products and services based on different needs are attributed to the excellence of segmenting the customers.
References
Tamanampudi, Venkata Mohit. "Automating CI/CD Pipelines with Machine Learning Algorithms: Optimizing Build and Deployment Processes in DevOps Ecosystems." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 810-849.
Thuraka, Bharadwaj, et al. "Leveraging artificial intelligence and strategic management for success in inter/national projects in US and beyond." Journal of Engineering Research and Reports 26.8 (2024): 49-59.
Katari, Pranadeep, et al. "Remote Project Management: Best Practices for Distributed Teams in the Post-Pandemic Era." Australian Journal of Machine Learning Research & Applications 1.2 (2021): 145-167.
J. Singh, “AI-Driven Path Planning in Autonomous Vehicles: Algorithms for Safe and Efficient Navigation in Dynamic Environments ”, Journal of AI-Assisted Scientific Discovery, vol. 4, no. 1, pp. 48–88, Jan. 2024
Machireddy, Jeshwanth Reddy. "Assessing the Impact of Medicare Broker Commissions on Enrollment Trends and Consumer Costs: A Data-Driven Analysis." Journal of AI in Healthcare and Medicine 2.1 (2022): 501-518.
S. Chitta, S. Thota, S. Manoj Yellepeddi, A. Kumar Reddy, and A. K. P. Venkata, “Multimodal Deep Learning: Integrating Vision and Language for Real-World Applications”, Asian J. Multi. Res. Rev., vol. 1, no. 2, pp. 262–282, Nov. 2020
Ahmad, Tanzeem, et al. "Explainable AI: Interpreting Deep Learning Models for Decision Support." Advances in Deep Learning Techniques 4.1 (2024): 80-108.
Tamanampudi, Venkata Mohit. "Autonomous Optimization of DevOps Pipelines Using Reinforcement Learning: Adaptive Decision-Making for Dynamic Resource Allocation, Test Reordering, and Deployment Strategy Selection in Agile Environments." Distributed Learning and Broad Applications in Scientific Research 10 (2024): 360-398.
Kodete, Chandra Shikhi, et al. "Determining the efficacy of machine learning strategies in quelling cyber security threats: Evidence from selected literatures." Asian Journal of Research in Computer Science 17.8 (2024): 24-33.
Thota, Shashi, et al. "Few-Shot Learning in Computer Vision: Practical Applications and Techniques." Human-Computer Interaction Perspectives 3.1 (2023): 29-59.
Tamanampudi, Venkata Mohit. "Leveraging Machine Learning for Dynamic Resource Allocation in DevOps: A Scalable Approach to Managing Microservices Architectures." Journal of Science & Technology 1.1 (2020): 709-748.
J. Singh, “Autonomous Vehicles and Smart Cities: Integrating AI to Improve Traffic Flow, Parking, and Environmental Impact ”, Journal of AI-Assisted Scientific Discovery, vol. 4, no. 2, pp. 65–105, Aug. 2024
S. Kumari, “Cloud Transformation for Mobile Products: Leveraging AI to Automate Infrastructure Management, Scalability, and Cost Efficiency”, J. Computational Intel. & Robotics, vol. 4, no. 1, pp. 130–151, Jan. 2024.