Using Machine Learning for Intelligent Case Routing in Salesforce Service Cloud
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I1P112Keywords:
Machine Learning, Intelligent Case Routing, Salesforce Service Cloud, Automated Case Assignment, AI-Driven Customer Support, Predictive Case Management, Customer Service Optimization, Service Automation, Case Prioritization, Intelligent WorkflowAbstract
Innovations in modern businesses customer support is shifting towards automation with an aim of simplifying ticket management and promote customers satisfaction. Salesforce conventional rule-based systems Service Clouds have proved non-flexible and lack scalability, which is the usual misroutes or delayed cases. The current paper discusses intelligent machine learning (ML) usage. Salesforce and cases classification and routing. Natural language processing (NLP) can be used. can adjust themselves to new conditions, ML-based systems may change according to new needs, historical case data, supervised learning algorithms, and ML-based systems. detect trends in support, forecast all-time optimum agents or queues and boost up resolution times. This study offers a comparative study of the rule-based and the ML-driven techniques. performance measures that include accuracy of classification, time of resolution and customer satisfaction scores. Besides, we suggest a hybrid design, which will involve human feedback loops. active learning in order to retain long-term routing accuracy. The results point to the fact that ML not only enhances the efficiency of routing as well as lowers the operational cost and enhances support team effectiveness
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