Dynamic routing algorithms in customer support: Revolutionizing contact center operations
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| Title: | Dynamic routing algorithms in customer support: Revolutionizing contact center operations |
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| Authors: | null Amaresha Prasad Sahoo |
| Source: | World Journal of Advanced Engineering Technology and Sciences. 15:2976-2983 |
| Publisher Information: | GSC Online Press, 2025. |
| Publication Year: | 2025 |
| Description: | Dynamic routing algorithms have transformed contact center operations through AI-driven decision-making and real-time data analytics. These systems optimize customer-agent matching while enhancing operational efficiency through intelligent queue management and predictive analytics. The integration of machine learning, natural language processing, and sentiment analysis capabilities has revolutionized how contact centers handle customer interactions, leading to improved resolution rates and customer satisfaction. Cloud-native solutions and emerging technologies continue to advance routing capabilities, offering scalable and adaptable systems that respond to changing customer needs and business requirements. The sophisticated architecture of these systems incorporates multiple layers of data processing and decision intelligence, enabling real-time adaptation to changing interaction patterns and customer preferences. Advanced analytics components process vast amounts of historical and real-time data to create comprehensive customer profiles and interaction histories, facilitating more precise routing decisions. The implementation of dynamic routing algorithms has demonstrated significant improvements across key performance indicators, including reduced handling times, improved first-contact resolution rates, and enhanced customer experience metrics. Furthermore, the integration of artificial intelligence and machine learning continues to push the boundaries of routing sophistication, enabling more nuanced understanding of customer intent and emotional states, while cloud-based infrastructure ensures scalability and reliability across diverse operational environments. |
| Document Type: | Article |
| ISSN: | 2582-8266 |
| DOI: | 10.30574/wjaets.2025.15.2.0880 |
| Accession Number: | edsair.doi...........b38bbef099f19c16733f9ab5c1f23d9a |
| Database: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | Dynamic routing algorithms have transformed contact center operations through AI-driven decision-making and real-time data analytics. These systems optimize customer-agent matching while enhancing operational efficiency through intelligent queue management and predictive analytics. The integration of machine learning, natural language processing, and sentiment analysis capabilities has revolutionized how contact centers handle customer interactions, leading to improved resolution rates and customer satisfaction. Cloud-native solutions and emerging technologies continue to advance routing capabilities, offering scalable and adaptable systems that respond to changing customer needs and business requirements. The sophisticated architecture of these systems incorporates multiple layers of data processing and decision intelligence, enabling real-time adaptation to changing interaction patterns and customer preferences. Advanced analytics components process vast amounts of historical and real-time data to create comprehensive customer profiles and interaction histories, facilitating more precise routing decisions. The implementation of dynamic routing algorithms has demonstrated significant improvements across key performance indicators, including reduced handling times, improved first-contact resolution rates, and enhanced customer experience metrics. Furthermore, the integration of artificial intelligence and machine learning continues to push the boundaries of routing sophistication, enabling more nuanced understanding of customer intent and emotional states, while cloud-based infrastructure ensures scalability and reliability across diverse operational environments. |
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| ISSN: | 25828266 |
| DOI: | 10.30574/wjaets.2025.15.2.0880 |