Empowering AOC's Global Customer Service with AI-Driven Quality Assurance and Optimization Solutions

I. Customer Overview

Admiral Overseas Corporation (AOC), a multinational giant with decades of experience in the European and American markets, has a global business footprint, establishing an extensive sales network and service system, firmly positioning itself at the forefront of the global display manufacturing industry. Its product portfolio is extensive, encompassing Color Ray Tube (CRT) monitors, Liquid Crystal Display (LCD) monitors, LCD Televisions (LCD-TVs), and Plasma Display Panels (PDPs). In Mainland China, AOC operates multiple production bases stretching from north to south, including Beijing, Qingdao, Wuhan, Fuqing (Fujian), Xiamen (Fujian), and Beihai (Guangxi), employing nearly 30,000 talented individuals.

II. Business Environment

AOC's after-sales customer service team faces a daunting daily volume of inquiries and diverse product models, along with complex issues. To address these challenges, team members must be proficient in both domestic and international policies, leveraging standardized processes and tailored technical solutions to prioritize the resolution of non-hardware issues stemming from misconfiguration or misuse. When confronted with genuine quality concerns, the team precisely records customer information and efficiently generates repair orders to facilitate seamless progression through subsequent processes, ultimately aiming to establish an efficient and high-quality after-sales service system.

III. Challenges Faced by the Customer

1. Professional Skill Assessment Bottleneck: Given the broad scope and complexity of the product line, customer service personnel are required to possess exceptional expertise. However, traditional manual quality inspection methods suffer from inefficiency and limited coverage (less than 10%), making it difficult to comprehensively evaluate service quality.
2. Data Management Chaos: The lack of standardized procedures for recording and managing after-sales data often leads to information loss or misoperations, thereby affecting service efficiency and customer experience.
3. Insufficient Communication Depth: Some customer service representatives struggle to fully comprehend customer needs and provide satisfactory solutions, impacting customer satisfaction and brand image.

IV. Customer Needs Analysis

1. Achieve 100% coverage for quality inspection of omnichannel customer service data.
2. Leverage AI technology to intelligently match products and service scenarios, enhancing inspection efficiency.
3. Conduct in-depth analysis of key semantics based on call and conversation data, monitoring prohibited language, speaking speed, sentiment, etc., while ensuring the accuracy and completeness of work order information.
4. Elevate the ASR transcription accuracy for industry-specific terminology to over 95%.
5. Provide one-stop consulting services to accelerate customer service capabilities and effectively track quality inspector workloads.

V. Customized Solutions

1. Automated Quality Inspection System: Integrate advanced technologies such as ASR and NLP to achieve comprehensive data processing and intelligent analysis, complemented by manual spot checks and appeal review mechanisms to ensure rigor and efficiency in the inspection process.
2. SOP Quality Inspection Model: Adopt a canvas-based approach to build customized quality inspection models, strictly controlling every aspect of the customer service process.
3. Specialized Quality Inspection Configuration: Tailor 216 quality inspection operators and 102 quality inspection rules specifically for AOC, precisely aligning with its business requirements.
4. Cross-Check Inspection Process: Combine work order data with call and conversation records to implement dual verification, ensuring a dual enhancement in service quality and work order accuracy.
5. Customized Model Optimization: Deeply customize and optimize ASR and NLP models to improve recognition accuracy and semantic understanding capabilities.
6. Knowledge Base and Semantic Tag Construction: Jointly build over 3,000 knowledge base entries and 1,600+ semantic tags, comprehensively covering after-sales scenarios across products, providing a solid foundation for service quality.
7. Intelligent Analytics Application: Utilize speech recognition and semantic understanding technologies to deeply mine customer service conversation data, supporting service quality monitoring, public opinion risk warning, and service strategy optimization.

VI. Application Effects

1. Soaring Management Efficiency: Achieving 100% coverage for customer service quality inspection and multidimensional, quantitative monitoring of service quality significantly enhances team management efficiency.
2. Surge in Quality Inspection Efficiency: The application of AI quality inspection technology boosts the daily processing capacity of individual quality inspectors to 150 cases, nearly five times the previous level, leading to a notable improvement in work efficiency.
3. Climbing Customer Satisfaction: Through issue identification and specialized training, the customer service team's capabilities have been comprehensively strengthened, resulting in a 14% reduction in customer complaints and a 6% increase in customer satisfaction.
4. Business Optimization Feedback: Based on service data analysis, precise identification of high-frequency issues and business trends drives continuous optimization of business processes and service standards, fostering the ongoing upgrade of service quality.