<p>几十年来，客户服务一直被定义为令人沮丧的延迟。客户遇到问题，浏览复杂的支持页面，最终往往出于绝望才联系人工代表。这种反应式模型将服务视为一个专注于追赶的成本中心，终于面临着主动性的彻底改革。Salesforce 的新主动服务旨在从根本上重新定义这一范式，利用统一的数据和智能自动化在客户甚至尚未提出投诉之前解决问题。这一转变至关重要，尤其是人工智能已经解决了 30% 的服务案例，预计到 2027 年这一数字将达到 50%。</p>
<p>主动客户服务 AI 的核心创新在于将预测智能与即时、可操作的解决工具相结合。历史上，数据孤岛阻碍了公司将不同信号（如最近的网站点击流、未完成的订单和社交媒体投诉）连接成一个单一、连贯的服务事件。由 Data 360 驱动的主动服务打破了这些障碍，在整个数据宇宙中建立了持续的警觉。这种持续监控使系统能够 24/7 检测即将发生的服务事件，将原始数据转化为预测触发器。</p>
<p>这种预测能力超越了简单的异常检测；它使得上下文预判成为可能。考虑一个电信提供商使用该系统监控外部天气模式和网络健康数据。如果检测到重大风暴，系统会主动通知受影响区域的客户潜在的服务中断，提供指导性故障排除步骤或预计恢复时间，甚至在第一通投诉电话尚未接入之前。这种预防性沟通大幅减少了来电量，维护了客户信任，并将服务互动从对抗式转变为礼宾式支持。</p>
<h2>主动解决的操作现实</h2>
<p>一旦检测到潜在问题，系统立即进入解决阶段，利用 Agentforce 启动自助解决。这不仅仅是发送一封通用电子邮件；它涉及直接向客户的首选渠道提供超个性化、可操作的指导。目标是通过提供预构建的自动化或复杂的指导流程，使用户能够独立解决问题，从而转移案例，而不是客户。这种主动的方法确保了解决方案及时、相关，并最小化通常会使客户远离自助服务选项的摩擦。</p>
<p>主动服务模型中最后也是最关键的一步是优化。系统并不因为立即问题得到解决就认为互动完成；它通过强大的反馈循环收集每个接触点的详细见解。这种持续测量确保主动策略随着时间的推移变得越来越有效，优化检测算法并提高自动解决路径的有效性。这种严格的三步工作流程——检测、解决、优化——是同时推动更高客户满意度评分和更低运营成本的引擎。</p>
<p>转向主动客户服务 AI 的商业影响深远，直接影响长期困扰服务组织的关键绩效指标。通过预防问题并引导客户进行有效的自助服务，公司可以显著减少来电支持请求，这在 65% 的服务领导者预计案件数量增加的情况下尤为重要。此外，能够及早捕捉和处理负面情绪，防止其升级为公共品牌损害，提供了显著的竞争优势。在内部，Salesforce 已经报告使用 Agentforce 的平均解决率飙升至 70% 或更高，证明了当预判取代反应时的变革潜力。</p>
<p>除了降低成本和提高满意度，主动服务也是一个强大的收入杠杆。通过监控合同到期日期或使用模式，系统可以主动通知客户即将到期的订阅或最大化其利益的机会，从而推动追加销售和升级转化。这一能力将服务组织从必要的开支转变为战略增长伙伴。根据公告，近 75% 的公司已经在投资或计划投资于主动服务，确认这一模型正在迅速成为行业标准，受到消费者对预判支持需求的推动。未来的客户服务不再是由你响应的速度决定，而是由你预判的有效性决定。</p>

赛富时

<p>Salesforce 推出了主动客户服务人工智能，旨在通过在问题出现之前解决问题来重新定义客户服务。这个创新系统将预测智能与可操作的解决工具相结合，允许持续监控数据以预测服务事件。通过主动通知客户潜在的干扰并提供个性化指导，该模型减少了来电量并提升了客户满意度。三步工作流程——检测、解决、优化——提高了解决率和运营效率。随着 75% 的公司投资于主动服务，这种方法正成为行业标准，强调在客户服务中以预见为主，而非反应</p>

<p>For decades, customer service has been defined by frustrating latency. Customers encounter a problem, navigate labyrinthine support pages, and only then, often out of desperation, reach a human representative. This reactive model, which treats service as a cost center dedicated to catching up, is finally facing an agentic overhaul. Salesforce’s new Proactive Service aims to fundamentally redefine this paradigm, leveraging unified data and intelligent automation to resolve issues before the customer even registers a complaint. This shift is critical, especially as AI is already resolving 30% of service cases, a figure projected to hit 50% by 2027.</p>
<div class="lb-trans"><p>几十年来，客户服务一直被定义为令人沮丧的延迟。客户遇到问题，浏览复杂的支持页面，最终往往出于绝望才联系人工代表。这种反应式模型将服务视为一个专注于追赶的成本中心，终于面临着主动性的彻底改革。Salesforce 的新主动服务旨在从根本上重新定义这一范式，利用统一的数据和智能自动化在客户甚至尚未提出投诉之前解决问题。这一转变至关重要，尤其是人工智能已经解决了 30% 的服务案例，预计到 2027 年这一数字将达到 50%。</p>
</div><p>The core innovation behind Proactive customer service AI is the integration of predictive intelligence with immediate, actionable resolution tools. Historically, data silos prevented companies from connecting disparate signals—like a recent website clickstream, an incomplete order, and a social media complaint—into a single, coherent service event. Proactive Service, powered by Data 360, breaks down these barriers, establishing constant vigilance across the entire data universe. This continuous monitoring allows the system to detect upcoming service events 24/7, transforming raw data into predictive triggers.</p>
<div class="lb-trans"><p>主动客户服务 AI 的核心创新在于将预测智能与即时、可操作的解决工具相结合。历史上，数据孤岛阻碍了公司将不同信号（如最近的网站点击流、未完成的订单和社交媒体投诉）连接成一个单一、连贯的服务事件。由 Data 360 驱动的主动服务打破了这些障碍，在整个数据宇宙中建立了持续的警觉。这种持续监控使系统能够 24/7 检测即将发生的服务事件，将原始数据转化为预测触发器。</p>
</div><p>This predictive capability moves beyond simple anomaly detection; it enables contextualized anticipation. Consider a telecommunications provider using the system to monitor external weather patterns alongside network health data. If a major storm is detected, the system proactively notifies customers in the affected zone about potential service disruptions, offering guided troubleshooting steps or estimated restoration times before the first complaint call even registers. This preemptive communication drastically reduces inbound call volume, preserves customer trust, and shifts the service interaction from adversarial to concierge-style support.</p>
<div class="lb-trans"><p>这种预测能力超越了简单的异常检测；它使得上下文预判成为可能。考虑一个电信提供商使用该系统监控外部天气模式和网络健康数据。如果检测到重大风暴，系统会主动通知受影响区域的客户潜在的服务中断，提供指导性故障排除步骤或预计恢复时间，甚至在第一通投诉电话尚未接入之前。这种预防性沟通大幅减少了来电量，维护了客户信任，并将服务互动从对抗式转变为礼宾式支持。</p>
</div><h2>The Operational Reality of Agentic Resolution</h2>
<div class="lb-trans"><h2>主动解决的操作现实</h2>
</div><p>Once a potential issue is detected, the system immediately moves into the Resolve phase, utilizing Agentforce to initiate self-service resolution. This is not merely sending a generic email; it involves delivering hyper-personalized, actionable guidance directly to the customer’s preferred channel. The goal is to deflect cases, not customers, by providing pre-built automations or complex, guided walkthroughs that allow the user to resolve the issue independently. This agentic approach ensures that the resolution is timely, relevant, and minimizes the friction that typically drives customers away from self-service options.</p>
<div class="lb-trans"><p>一旦检测到潜在问题，系统立即进入解决阶段，利用 Agentforce 启动自助解决。这不仅仅是发送一封通用电子邮件；它涉及直接向客户的首选渠道提供超个性化、可操作的指导。目标是通过提供预构建的自动化或复杂的指导流程，使用户能够独立解决问题，从而转移案例，而不是客户。这种主动的方法确保了解决方案及时、相关，并最小化通常会使客户远离自助服务选项的摩擦。</p>
</div><p>The final, and perhaps most crucial, step in the Proactive Service model is Optimize. The system does not consider the interaction complete simply because the immediate issue is resolved; it gathers granular insights on every touchpoint through a powerful feedback loop. This continuous measurement ensures that the proactive strategy becomes exponentially more effective over time, refining detection algorithms and improving the efficacy of the automated resolution paths. This disciplined, three-step workflow—Detect, Resolve, Optimize—is the engine that drives both higher CSAT scores and lower operational costs simultaneously.</p>
<div class="lb-trans"><p>主动服务模型中最后也是最关键的一步是优化。系统并不因为立即问题得到解决就认为互动完成；它通过强大的反馈循环收集每个接触点的详细见解。这种持续测量确保主动策略随着时间的推移变得越来越有效，优化检测算法并提高自动解决路径的有效性。这种严格的三步工作流程——检测、解决、优化——是同时推动更高客户满意度评分和更低运营成本的引擎。</p>
</div><p>The business implications of moving to Proactive customer service AI are profound, directly impacting critical KPIs that have long plagued service organizations. By preventing issues and guiding customers to effective self-service, companies can dramatically reduce inbound support requests, a vital necessity given that 65% of service leaders anticipate increased caseloads. Furthermore, the ability to catch and address negative sentiment early, before it escalates into public brand damage, provides a significant competitive advantage. Internally, Salesforce has already reported average resolution rates soaring to 70% or more using Agentforce, demonstrating the transformative potential when anticipation replaces reaction.</p>
<div class="lb-trans"><p>转向主动客户服务 AI 的商业影响深远，直接影响长期困扰服务组织的关键绩效指标。通过预防问题并引导客户进行有效的自助服务，公司可以显著减少来电支持请求，这在 65% 的服务领导者预计案件数量增加的情况下尤为重要。此外，能够及早捕捉和处理负面情绪，防止其升级为公共品牌损害，提供了显著的竞争优势。在内部，Salesforce 已经报告使用 Agentforce 的平均解决率飙升至 70% 或更高，证明了当预判取代反应时的变革潜力。</p>
</div><p>Beyond cost reduction and satisfaction, proactive service is also a powerful revenue lever. By monitoring contract expiration dates or usage patterns, the system can proactively inform customers about expiring subscriptions or opportunities to maximize their benefits, driving upsell and upgrade conversions. This capability transforms the service organization from a necessary expense into a strategic growth partner. According to the announcement, nearly 75% of companies are already investing or planning to invest in proactive services, confirming that this model is rapidly becoming the industry standard, driven by consumer demand for anticipatory support. The future of customer service is defined not by how quickly you respond, but by how effectively you anticipate.</p>
<div class="lb-trans"><p>除了降低成本和提高满意度，主动服务也是一个强大的收入杠杆。通过监控合同到期日期或使用模式，系统可以主动通知客户即将到期的订阅或最大化其利益的机会，从而推动追加销售和升级转化。这一能力将服务组织从必要的开支转变为战略增长伙伴。根据公告，近 75% 的公司已经在投资或计划投资于主动服务，确认这一模型正在迅速成为行业标准，受到消费者对预判支持需求的推动。未来的客户服务不再是由你响应的速度决定，而是由你预判的有效性决定。</p>
</div>

赛富时推出了主动客户服务人工智能