Large Language Models in Retail CRM Systems: A Technical Evaluation of Improving Customer Support, Engagement, and Sales Strategies
Keywords:
large language models, retail CRM systemsAbstract
LLMs changed retail CRM. This extensive technical study reveals LLMs may boost retail CRM customer service, engagement, and sales. CRM enhances retail customer data management, support, and retention. Automate and personalize encounters using LLMs and robust NLP. LLM-CRM integration is evaluated throughout model training, fine-tuning, and retail deployment. Selecting and using LLMs of various dimensions involves comparing smaller, task-specific models' accuracy and responsiveness to larger, general-purpose models' contextual information.
Retail CRM Automation of customer service Research LLM. CRM-linked LLMs may assist retailers resolve customer complaints using chatbots. Computer context, emotion, and NLP enable real-time CRM. Multilingual LLMs globalize retail CRMs. It enables large multilingual stores give consistent service. Retail CRM LLMs help ability. LLMs customize CRM recommendations and promotions to customer behavior using sentiment analysis and personalized content. Customization boosts customer happiness and brand loyalty.
References
M. Brown, A. Narasimhan, and L. D. O’Rourke, "Large Language Models for Customer Service Automation," Journal of Retail Technology, vol. 22, no. 3, pp. 128-136, 2023.
T. Smith, "Advancements in Natural Language Processing for Retail CRM Systems," Proceedings of the 2022 International Conference on AI in Retail, pp. 45-53, 2022.
Ratnala, Anil Kumar, Rama Krishna Inampudi, and Thirunavukkarasu Pichaimani. "Evaluating Time Complexity in Distributed Big Data Systems: A Case Study on the Performance of Hadoop and Apache Spark in Large-Scale Data Processing." Journal of Artificial Intelligence Research and Applications 4.1 (2024): 732-773.
Zhu, Yue, and Johnathan Crowell. "Systematic Review of Advancing Machine Learning Through Cross-Domain Analysis of Unlabeled Data." Journal of Science & Technology 4.1 (2023): 136-155.
Sangaraju, Varun Varma, and Kathleen Hargiss. "Zero trust security and multifactor authentication in fog computing environment." Available at SSRN 4472055.
Machireddy, Jeshwanth Reddy. "ARTIFICIAL INTELLIGENCE-BASED APPROACH TO PERFORM MONITORING AND DIAGNOSTIC PROCESS FOR A HOLISTIC ENVIRONMENT." International Journal of Computer Science and Engineering Research and Development (IJCSERD) 14.2 (2024): 71-88.
Tamanampudi, Venkata Mohit. "AI-Driven Incident Management in DevOps: Leveraging Deep Learning Models and Autonomous Agents for Real-Time Anomaly Detection and Mitigation." Hong Kong Journal of AI and Medicine 4.1 (2024): 339-381.
S. Kumari, “Cloud Transformation and Cybersecurity: Using AI for Securing Data Migration and Optimizing Cloud Operations in Agile Environments”, J. Sci. Tech., vol. 1, no. 1, pp. 791–808, Oct. 2020.
Kurkute, Mahadu Vinayak, Anil Kumar Ratnala, and Thirunavukkarasu Pichaimani. "AI-Powered IT Service Management for Predictive Maintenance in Manufacturing: Leveraging Machine Learning to Optimize Service Request Management and Minimize Downtime." Journal of Artificial Intelligence Research 3.2 (2023): 212-252.
Pichaimani, T., Inampudi, R. K., & Ratnala, A. K. (2021). Generative AI for Optimizing Enterprise Search: Leveraging Deep Learning Models to Automate Knowledge Discovery and Employee Onboarding Processes. Journal of Artificial Intelligence Research, 1(2), 109-148.
Surampudi, Yeswanth, Dharmeesh Kondaveeti, and Thirunavukkarasu Pichaimani. "A Comparative Study of Time Complexity in Big Data Engineering: Evaluating Efficiency of Sorting and Searching Algorithms in Large-Scale Data Systems." Journal of Science & Technology 4.4 (2023): 127-165.
Kondaveeti, Dharmeesh, Rama Krishna Inampudi, and Mahadu Vinayak Kurkute. "Time Complexity Analysis of Graph Algorithms in Big Data: Evaluating the Performance of PageRank and Shortest Path Algorithms for Large-Scale Networks." Journal of Science & Technology 5.4 (2024): 159-204.
Tamanampudi, Venkata Mohit. "Generative AI Agents for Automated Infrastructure Management in DevOps: Reducing Downtime and Enhancing Resource Efficiency in Cloud-Based Applications." Journal of AI-Assisted Scientific Discovery 4.1 (2024): 488-532.
Inampudi, Rama Krishna, Thirunavukkarasu Pichaimani, and Yeswanth Surampudi. "AI-Enhanced Fraud Detection in Real-Time Payment Systems: Leveraging Machine Learning and Anomaly Detection to Secure Digital Transactions." Australian Journal of Machine Learning Research & Applications 2.1 (2022): 483-523.
Sangaraju, Varun Varma, and Senthilkumar Rajagopal. "Applications of Computational Models in OCD." In Nutrition and Obsessive-Compulsive Disorder, pp. 26-35. CRC Press.
S. Kumari, “Cybersecurity Risk Mitigation in Agile Digital Transformation: Leveraging AI for Real-Time Vulnerability Scanning and Incident Response ”, Adv. in Deep Learning Techniques, vol. 3, no. 2, pp. 50–74, Dec. 2023
Parida, Priya Ranjan, Rama Krishna Inampudi, and Anil Kumar Ratnala. "AI-Driven ITSM for Enhancing Content Delivery in the Entertainment Industry: A Machine Learning Approach to Predict and Automate Service Requests." Journal of Artificial Intelligence Research and Applications 3.1 (2023): 759-799.
J. Zhao and L. Li, "Integrating Chatbots and LLMs in Customer Service: A Case Study," International Journal of Customer Relations Management, vol. 11, no. 2, pp. 100-115, 2023.
B. Johnson and R. Green, "Personalized Customer Experience with LLMs in Retail," Retail AI Review, vol. 30, no. 1, pp. 45-55, 2023.
X. Wu, Y. Zhang, and P. S. Mahajan, "Real-Time Predictive Analytics for Retail CRM Using Large Language Models," Journal of Retail Analytics, vol. 14, pp. 202-215, 2021.
D. Patel, R. Gupta, and S. Kumar, "Machine Learning and LLMs in Retail CRM," IEEE Transactions on Artificial Intelligence, vol. 24, no. 5, pp. 2210-2220, 2022.
J. Doe and M. Anderson, "Scaling AI in Retail: Leveraging LLMs for Large-Scale Customer Support," AI in Retail Journal, vol. 18, no. 4, pp. 123-130, 2021.
H. Allen and K. Nguyen, "Sentiment Analysis with LLMs in Customer Engagement," Proceedings of the 2022 AI and Sentiment Analysis Conference, pp. 55-63, 2022.
S. Bhatia, "Data Privacy in Retail CRM: Challenges and Solutions," Journal of Privacy and Data Security, vol. 13, no. 2, pp. 48-60, 2023.
K. Tran, R. Singh, and T. L. Cooper, "Improving Sales Conversions Using Large Language Models in Retail CRM," Journal of Retail Marketing and Sales, vol. 12, pp. 78-89, 2022.
S. N. Patel and J. K. Soni, "The Role of Large Language Models in Upselling and Cross-Selling," Retail AI & Technology Journal, vol. 23, no. 1, pp. 22-35, 2023.
L. H. Garcia and P. Thakur, "Automating Customer Service with LLMs: From Chatbots to Virtual Assistants," IEEE Transactions on Consumer Electronics, vol. 55, no. 4, pp. 1001-1010, 2021.
D. Martin, A. K. Sharma, and M. S. Wood, "Multilingual Customer Support through LLMs in Global Retail," International Journal of Retail Technology, vol. 8, no. 3, pp. 88-102, 2023.
R. Kumar, M. Sharma, and D. Gupta, "Comparative Analysis of Rule-Based and LLM-Powered CRM Systems," Proceedings of the 2021 International Conference on AI in Business, pp. 112-118, 2021.
A. Clark, "Retail Customer Segmentation with Machine Learning and LLMs," Journal of Retail Data Science, vol. 19, no. 2, pp. 140-150, 2022.
A. Lee and T. H. Yuen, "Large Language Models for Predictive Analytics in Retail CRM," IEEE Transactions on Machine Learning Applications, vol. 17, no. 5, pp. 97-105, 2023.
L. Tan and S. J. Huang, "Bias in AI Systems: A Challenge for Retail CRM and Customer Interactions," AI Ethics Journal, vol. 10, no. 1, pp. 65-74, 2022.
R. Roy, "Customer Experience Transformation with AI-Powered LLMs in Retail CRM," Journal of Business Technology, vol. 14, no. 6, pp. 103-115, 2021.
T. Peterson, M. Sharma, and D. Chauhan, "Advancements in Retail CRM: From Rule-Based Systems to AI-Driven LLMs," Journal of Marketing Science, vol. 11, no. 4, pp. 22-30, 2021.
R. Singh, "Ethical and Privacy Considerations in LLM-Based Customer Interactions," Journal of Ethical AI in Retail, vol. 3, no. 1, pp. 12-25, 2023.
