Challenging the Status Quo
In the current self-service consultation scenarios, text robots have several issues:
- The answers are not precise, complex questions are difficult to understand, leading to repeated consultations from customers; the language patterns are rigid, unable to vary according to individuals, resulting in poor communication experience;
- Business processing is limited by formatted procedures, with insufficient understanding of intentions, and inaccurate process push;
- The knowledge base is difficult to optimize, and frequent questions are hard to consolidate, causing the robots to continuously fail to answer and relying on human intervention.
Cloopen ai Copilot Solution
Cloopen ai Virtual Agent, leveraging the "large model intelligent knowledge base + large model conversation mining + automated task process" capabilities, redefines the automatic service experience. It can comprehensively extract massive document information, automatically mine the best conversation scripts, intelligently extract task nodes, construct task processes, accurately understand customer needs, improve the accuracy of automatic responses, reduce the occurrence of manual transfers due to unclear processes, and quickly resolve business inquiries and processing issues.
Strategy for reducing the rate of manual intervention
- 1. Business consultation scenarios
Comprehensive knowledge base coverage: Traditional small model text robots have limited knowledge. The Chenxi customer service agent uses a large model to automatically extract information, has a wide knowledge base, can handle common issues, and reduces the number of cases requiring manual intervention due to knowledge gaps.
Precise semantic understanding: Traditional small models have poor understanding. The Chenxi customer service agent combines customer data and context semantics to conduct multiple rounds of conversations, improving service quality.
Rapid learning and iteration: Traditional knowledge bases have low optimization efficiency. The Chenxi customer service agent learns and upgrades in real time based on customer feedback, reducing the number of unanswered questions and enhancing self-service.
- 2. Business Processing Scenarios
Automatic Process Engine: Traditional text robot business configuration is time-consuming and labor-intensive. The agent of Cloopen ai Call Center can automatically optimize the task process, flexibly switch nodes, and enhance the automation processing capability.
Process Insight Optimization: Traditional small models cannot determine which processes are suitable for automation. The agent of Cloopen ai Call Center analyzes conversation data and helps enterprises find suitable automated processes, reducing reliance on manual work.
- 3. Flexible manual transfer
Provide a flexible manual transfer strategy to ensure that manual service is only transferred when necessary.
Conclusion
Intelligent assistants have great potential in modern customer service, which can enhance service efficiency and quality, and help enterprises stand out in the competition. With the advancement of technology, intelligent assistants will continue to lead the future of customer service and create more value.