AI-Powered Customer Retention Strategies in Insurance
Keywords:
AI-Powered, Customer Retention, Strategies, InsuranceAbstract
Building customer loyalty is a critical aspect of branding strategy. Insurance companies continue to pour resources into various avenues aimed at enhancing customer loyalty, though there is considerable debate on the influence of customer loyalty on brand success. However, raising levels of customer retention have become an increasingly important feature in highly competitive markets. Insurance firms are faced with a number of challenges from a variety of competitive threats. One particular consequence of an increasingly competitive market is that competition has served to erode the profitability of an increasing number of individual insurance products. As a result, customer retention has become a more important aspect of the profitability of the industry across a wide range of insurance products. As part of a broader aim of enhancing customer loyalty, this proposes an artificial intelligence customer retention innovation designed to improve firms' competitive positioning and available strategies in the highly competitive market for insurance.
Downloads
References
S. Kumari, “Cybersecurity in Digital Transformation: Using AI to Automate Threat Detection and Response in Multi-Cloud Infrastructures ”, J. Computational Intel. & Robotics, vol. 2, no. 2, pp. 9–27, Aug. 2022
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.
Machireddy, Jeshwanth Reddy. "Data-Driven Insights: Analyzing the Effects of Underutilized HRAs and HSAs on Healthcare Spending and Insurance Efficiency." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 450-470.
Singh, Jaswinder. "Social Data Engineering: Leveraging User-Generated Content for Advanced Decision-Making and Predictive Analytics in Business and Public Policy." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 392-418.
Tamanampudi, Venkata Mohit. "AI and DevOps: Enhancing Pipeline Automation with Deep Learning Models for Predictive Resource Scaling and Fault Tolerance." Distributed Learning and Broad Applications in Scientific Research 7 (2021): 38-77.
J. Singh, “Combining Machine Learning and RAG Models for Enhanced Data Retrieval: Applications in Search Engines, Enterprise Data Systems, and Recommendations ”, J. Computational Intel. & Robotics, vol. 3, no. 1, pp. 163–204, Mar. 2023.
Tamanampudi, Venkata Mohit. "AI Agents in DevOps: Implementing Autonomous Agents for Self-Healing Systems and Automated Deployment in Cloud Environments." Australian Journal of Machine Learning Research & Applications 3.1 (2023): 507-556.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
License Terms
Ownership and Licensing:
Authors of research papers submitted to Distributed Learning and Broad Applications in Scientific Research retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agree to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the journal. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this journal.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the journal. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. Scientific Research Canada disclaims any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
If you have any questions or concerns regarding these license terms, please contact us at editor@dlabi.org.
