● 张乐弛 董事会的质询如同定期响起的警铃："你是否已经部署AI客服？" 他的公司虽然不算大，但凭借多年细致入微的电话热线答疑，已在巨头夹缝中赢得口碑，拥有了坚固的 护城河。当客户致电时，接听电话的是公司的人工顾问。他们不仅熟知业务条款、能处理复杂案例，更 懂得合规分寸，还能在专业之外，提供满满的情绪价值。同时，对于金融业务，一次错误绝非简单的失 误。一个由AI"幻觉"生成的错误利率解释，或是一次不合规的承诺，都可能引发连锁反应，从客户投诉 升级为监管审查乃至法律纠纷。 其实，这是众多出海中小企业在AI转型十字路口的一个缩影。效率与客户体验，在他们尝试客服智能 化转型的实践中激烈交锋。这场转型的核心矛盾，不是要不要使用人工智能，而是如何科学地去理解和 评估智能技术对企业的影响，在计算收入提升、成本缩减的同时，也不可忽视AI对客户信任和品牌价 值的影响。 市场本质、科技赋能与政策环境 许多市场困局的根源在于"信息不对称"。对于出海的中小企业而言，这种不对称被距离与法规放大：企 业管理层对自身服务的专业细节与合规架构了然于胸，但跨越地理、法律与文化产生的隔阂，对品牌的 陌生，使海外客户在认知上如同"盲人摸象"。这 ...

Core Insights - The hospitality and travel industry is facing a critical challenge where customer service is no longer just a response center but a key operational node that impacts conversion rates, repurchase rates, and service efficiency [3] - Travelers are increasingly impatient, prioritizing immediate responses over cleanliness or food quality, leading to rising labor costs exceeding 30% [3][4] Group 1: AI Integration in Customer Service - The integration of AI in customer service is evolving from a simple response system to a comprehensive concierge service that can drive repurchase [7] - AI can reduce ineffective inquiries and compress interaction rounds through dual engines of itinerary and location [7] - The boundaries of proactive care are defined by time thresholds, types of benefits, and push frequency [7] Group 2: Human-AI Collaboration in Customer Service - There is a need to identify which services should be handled by AI and which should remain human-operated across pre-stay, in-stay, and post-stay phases [8] - Examples of both successful and failed cases of intelligent distribution and human-machine collaboration are discussed [8] Group 3: Unique Challenges of AI in Hospitality - The hospitality sector cannot simply adopt retail models for AI customer service due to different operational needs [8] - The effectiveness boundary between scene-level knowledge graphs and Q&A knowledge bases is highlighted [8] - Compliance and safety issues arise when agents transition from "suggestion" to "execution" [8]

Core Viewpoint - The article discusses the critical issue of declining birth rates in China, emphasizing the need for policies to stabilize and encourage population growth, particularly through marriage and childbirth incentives [4][10]. Group 1: Population Statistics - As of the end of 2025, China's total population is projected to be 1.405 billion, a decrease of 3.39 million from the previous year [6]. - The birth rate for the year is expected to be 7.92 million, with a birth rate of 5.63‰ and a death rate of 8.04‰, resulting in a natural growth rate of -2.41‰ [7]. Group 2: Policy Initiatives - Recent meetings have highlighted the importance of promoting a positive marriage and childbirth culture, with a specific focus on stabilizing the number of new births [10][14]. - The government is implementing various measures to stimulate childbirth, including financial incentives and educational reforms, aiming to create a more supportive environment for families [35][39]. Group 3: Marriage Trends - There has been a notable increase in marriage rates, with 5.152 million couples marrying in the first three quarters of 2025, representing an 8.5% year-on-year increase [22]. - Major cities are experiencing significant growth in marriage registrations, with Shanghai seeing a 38.7% increase and Shenzhen a 28.7% increase [24]. Group 4: Future Projections - The article suggests that the number of births may rebound in 2026, driven by the increase in marriage rates and supportive policies [18][32]. - While short-term improvements are expected, the article cautions that reversing the long-term decline in birth rates will require sustained efforts and comprehensive policy measures [33][42].

Core Insights - The upgrade of cross-border services and customer experience is driving more SMEs to adopt AI customer service systems, which are no longer exclusive to large enterprises but are essential for improving response efficiency and optimizing operational structures [1][3] - By 2025, the AI customer service market has shifted from "function stacking" to "scenario adaptation" and "cost control," with companies focusing on deployment flexibility, actual resolution rates, and long-term operational costs [1][3] AI Customer Service Value and Selection Logic - The value of AI customer service lies not only in "replacing human labor" but also in "enhancing service," enabling 24/7 responses, multi-turn dialogue guidance, and automatic ticket assignment, significantly reducing first response times and reliance on human resources [3] - SMEs should prioritize three dimensions when selecting AI customer service solutions: alignment with IT capabilities, support for core business scenarios, and transparency of total cost of ownership (TCO) [6] Recommended AI Customer Service Systems - **Lingyang Quick Service**: An AI customer service product from Alibaba Cloud, designed for quick deployment and effectiveness, suitable for SMEs in e-commerce, retail, and SaaS software [4] - **Ronglian Qimo**: Offers a multi-channel intelligent customer service platform, ideal for businesses with call center foundations needing to integrate various communication channels [8] - **Zhichi Technology**: Focuses on full-scenario intelligent customer service, suitable for B2C enterprises with high customer conversion rate requirements [9] - **Xiaoneng Technology**: Provides a closed-loop customer service solution tailored for e-commerce and retail, particularly effective for sellers with high daily inquiry volumes [10] - **Yijie Cloud Customer Service**: Emphasizes elastic scalability and high availability, suitable for SMEs with significant business fluctuations [12] Cost Control Strategies for SMEs - Cost control does not mean sacrificing capabilities; it is essential to configure according to needs and implement in phases [15] - Initial phase: Choose SaaS annual fee products like Lingyang Quick Service to avoid large upfront investments [15] - Mid-phase: Evaluate private deployment or hybrid architecture if inquiry volumes surge or internal system integration is needed [15] - Long-term: Continuously optimize knowledge bases and intent recognition rules through dialogue data analysis to enhance self-service rates and indirectly reduce labor costs [15] FAQs on AI Customer Service - AI customer service can handle over 70% of repetitive inquiries, freeing human resources for more complex issues [16] - SaaS versions are generally more cost-effective and easier to manage for SMEs unless there are specific data isolation requirements [17] - Mainstream SaaS products support zero-code configuration, allowing business personnel to maintain systems after brief training [18]

Core Insights - The article highlights the challenges faced by users when trying to transition from AI customer service to human representatives, indicating that AI systems often struggle to understand user requests, leading to a frustrating experience for consumers [1][2] - It points out that some companies intentionally create barriers to accessing human customer service, prioritizing cost-cutting over user experience, which contradicts the original intention of implementing AI [1][2] Group 1: AI Customer Service Limitations - AI customer service systems exhibit significant limitations in understanding user inquiries, which complicates the process of transitioning to human support [1] - Companies may opt for lower-cost AI solutions that lack the capability to handle complex customer needs, resulting in inadequate service [1] - The reluctance of some companies to address these issues reflects a mindset focused on cost reduction rather than enhancing service quality [1] Group 2: Industry Recommendations - The industry should recognize the importance of effective human-AI collaboration, ensuring that AI handles simple inquiries while complex issues are promptly escalated to human agents [2] - Companies risk damaging their brand reputation and consumer trust by overly focusing on cost-cutting measures, which can lead to a negative feedback loop affecting customer satisfaction [2] - There is a call for clearer standards regarding AI customer service, emphasizing that AI should complement human efforts rather than replace them, with mechanisms in place for automatic escalation to human support when AI fails [2]

Core Insights - The focus of customer service has shifted from enhancing efficiency to allowing AI to take over tasks and execute them in a closed loop, demonstrating verifiable ROI by 2025 [1] - Companies are increasingly asking how much human labor AI can replace rather than if AI can be implemented [1] Group 1: Research Findings - The white paper is based on in-depth research of four leading customer service companies: Sierra, Decagon, ASAPP, and Cognigy [1] - It serves as a practical guide for those responsible for customer service system construction, SOP implementation, automation project advancement, cost optimization, or service experience enhancement [1] Group 2: Key Questions Addressed - The white paper addresses critical questions such as the actual automation rate, whether delivery costs have truly decreased, and how AI can achieve end-to-end task closure across different business processes [2] - It also explores the relationship between automation rates, resolution rates, and token costs, as well as how mature companies measure AI ROI beyond just hit rates [2] Group 3: Future Considerations - The document discusses the future structure and role reconstruction of customer service organizations, how to assess the current system's value, and how to choose the technology roadmap for the next two years [2] - It emphasizes the importance of demonstrating to management that AI investments can yield real returns and how to drive the replacement of human labor in a controlled manner [2]

Core Viewpoint - The article highlights the challenges faced by consumers in the instant retail sector, particularly regarding customer service and communication issues when problems arise with orders [1][3]. Group 1: Consumer Experience - Consumers are often left without adequate support when issues occur, such as receiving incorrect orders or experiencing automatic refunds without prior notification [1][3]. - The lack of effective communication from platforms, particularly through automated customer service, leads to frustration and a poor consumer experience [3]. Group 2: Industry Practices - The article criticizes the practice of platforms using automated refunds without informing consumers, which undermines their rights to be informed and to choose alternatives like waiting, exchanging, or refunding [3]. - Some platforms, like Meituan and JD.com, have implemented better practices by offering consumers options when items are out of stock, demonstrating that service quality can be improved through better communication [3]. Group 3: Regulatory Environment - There is a call for regulatory bodies to establish standards for the use of intelligent customer service, including response times for transferring to human agents and ensuring consumer rights are protected [3]. - The current increase in complaints related to intelligent customer service indicates a need for improved industry standards and oversight [3]. Group 4: Market Dynamics - Despite ongoing losses, major delivery platforms continue to invest heavily in subsidies and market competition, raising questions about the sustainability of such practices [3]. - The article emphasizes that the ultimate success in the delivery market will depend on the quality of service provided to consumers, rather than just order volume or technological efficiency [3].

Core Viewpoint - Ant Group's subsidiary, Digital Mali, has launched a "Double 11 Service Guarantee Plan" aimed at addressing e-commerce customer service pain points through a performance-based payment model, promising brands tangible commercial growth [1] Group 1: Service Guarantees - The plan includes commitments such as "If you can't connect, use it for free," where if the service response time does not meet the agreed SLA standards, the service fee will be waived [4] - The "No Growth, No Charge" promise ensures that only stores managed entirely by Digital Mali will receive additional "value-added services" aimed at enhancing customer conversion [5] Group 2: Performance Metrics - During the "6.18" shopping festival, Digital Mali's AI cloud customer service handled over 26 million service requests, with a peak of nearly 550,000 requests in one day, maintaining an average service satisfaction rate of 94% [6] - The ROI of AI cloud customer service is highlighted by improved efficiency, with customer service representatives potentially increasing their problem-solving capacity from 200 to 300 queries per day due to time savings [6] Group 3: AI Integration and Future Outlook - Gartner predicts a 400% growth in AI-driven customer interactions by 2025, with AI agents improving customer satisfaction by 20% [7] - Digital Mali's AI cloud customer service solution integrates AI as a central intelligence hub, reconstructing service processes to balance efficiency and quality [7] - The company offers a "managed service" model, training cloud customer service representatives using AI, and providing ongoing support to enhance customer engagement and brand loyalty [7][8]

Group 1 - The increasing reliance on AI customer service is leading to consumer frustration, as many find it difficult to reach human representatives when issues arise [1][2] - Companies are using AI to manage customer inquiries, especially during peak times, but this can result in inadequate handling of complex issues and customer emotions [2] - There is a call for businesses to improve "human-machine collaboration" by ensuring clear access to human customer service when AI fails to resolve issues [2] Group 2 - The performance of customer service teams is directly linked to the number of complaints they receive, creating pressure to manage inquiries efficiently [2] - AI customer service is better suited for structured and routine inquiries, but struggles with complex problems and emotional customer responses [2] - Recommendations include establishing a clear "transfer to human" option in AI systems and improving compensation and support for customer service personnel [2]

Group 1 - The establishment of the Zhejiang Foreign Language University (ZFU) cross-border e-commerce industrial park aims to create a comprehensive service platform integrating cross-border e-commerce operations, talent cultivation, and entrepreneurial incubation [3][4] - The park has attracted 24 cross-border e-commerce enterprises since its establishment, with nearly half being related to ZFU, including six alumni companies [3][4] - The return of alumni enterprises to the park fosters industry clustering and enhances talent training and employment opportunities for students [4] Group 2 - The "3+1" innovative experimental class at ZFU implements a school-enterprise joint training model, which is further enhanced by the establishment of the industrial park, improving students' practical skills [5] - As of recent statistics, 103 students have entered the industrial park for internships, practical training, and employment, with 158 students expressing job interest in the park [5] - The industrial park is equipped with shared live streaming rooms and intelligent product selection areas, supporting the development of cross-border live streaming as a new business avenue [6] Group 3 - The park plans to focus on developing overseas live streaming business, leveraging the skills of ZFU's language and international students [6] - The initiative promotes a collaborative development model of "campus + industrial park + incubation park," aiming for deep integration of talent cultivation, industry clustering, and innovation entrepreneurship [6] - Several companies within the park are actively recruiting, offering over 20 job positions, and will continue to promote the integrated development of "live streaming + platform economy + cross-border e-commerce" [6]