对外，智能客服，提升消费者的服务体验； 对内，专业助手，用于员工培训、知识查询，让效率倍增。 对话式AI，是生成式AI产业落地的最典型应用场景之一： 又一个好消息。 最近，IDC发布《2025年亚太区AI赋能前台对话式AI软件厂商评估》报告。 腾讯云超越众多全球头部厂商，荣获最高评级"第一象限领导者"，也是唯一入选该象限的中国企业。✌️ 而亚太，是对话式AI落地最具挑战的地区，这里语言多样、文化多元、监管复杂，对AI产品的本地化支持与合规能力要求极高。 这次评选，历时半年，门槛极高，不仅要求AI产品技术实力够硬，更考察AI够不够"好用"：有没有真正帮助到企业。 基于我们的智能体开发平台（ADP）、数智人及智能客服产品，腾讯云对话式AI能更精准理解意图、交互更亲和拟人，已经帮众多海内外客户，解决了 问题，提了效： "发国外，这个物品能寄吗？" 作为位列全球品牌价值 500 强的国际物流巨头，DHL（中外运敦豪）联合腾讯云打造客服机器人，转人工客服的绝对数减少了200人次/天，机器人解决率 从69%提升至74%，客户服务效率大大提升。 "给我房间拿杯水。" 作为业务覆盖 19 个国家、运营超 1.2 万家酒店、坐 ...

Core Transformation: AI Reshaping Brand Customer Acquisition Logic - Traditional marketing relied on the AIDA model and a "traffic thinking" approach, focusing on broad coverage and high exposure, but AI enables a shift to "value-driven, precise reach, and deep interaction" [2] - The transition involves four dimensions: from "traffic thinking" to "user value thinking," from "mass communication" to "hyper-personalized communication," from "post-analysis" to "predictive analysis," and from "labor-intensive" to "technology-driven" [2][3][4][5] Five Core Trends Driven by AI in Brand Customer Acquisition - Trend One: Intelligent orchestration of the customer journey through data integration and automation tools, creating a seamless user experience [6][8] - Trend Two: Generative AI enhances content marketing efficiency by enabling scalable production and dynamic optimization of marketing materials [9][10] - Trend Three: Conversational AI upgrades interactive customer acquisition by transforming passive inquiries into proactive need identification [11] - Trend Four: Predictive analytics allows brands to transition from blind outreach to precise targeting by identifying high-value users and conversion opportunities [12] - Trend Five: AI-driven search engine customer acquisition requires brands to adapt SEO strategies to leverage AI tools for capturing search traffic [13] Practical Framework: Building an AI-Driven Intelligent Customer Acquisition System - Step One: Establish a solid data foundation by integrating diverse data sources into a Customer Data Platform (CDP) to ensure high-quality data [14] - Step Two: Select and integrate technology that matches business needs, ensuring compatibility among tools to prevent data silos [15] - Step Three: Focus on strategy and creativity by defining the division of labor between AI execution and human strategic input [16] - Step Four: Create a testing and iteration loop to continuously optimize strategies based on user data and feedback [17][18][19] - Step Five: Evolve measurement metrics to focus on long-term value indicators, such as customer lifetime value (LTV) and marketing contribution revenue [20][21] Current Challenges and Future Outlook - Brands face challenges in data privacy compliance, algorithm bias, and the need for skill transformation within teams to effectively utilize AI tools [22][24] - Future developments may see AI evolve from a tool to an autonomous decision-making entity, capable of setting acquisition goals and executing strategies in real-time [25]