AI聊天成东南亚网购新起点，零售商面临数据挑战

亿邦动力 2026-10-10 15:16

亿邦动力 2026/10/10 15:16

邦小白快读

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AI正成为购物的新起点，了解这些变化能帮你更好消费。

1. AI聊天大幅改变商品发现方式：代理式搜索作为购物第一步的使用量同比增长200%，AI聊天带来的电商流量最高增长428%，越来越多消费者是通过AI推荐而不是传统搜索发现商品。

2. 用AI购物虽然方便，但要多方核实：AI会根据气候、脚型、价格等需求直接给出产品推荐，可如果品牌库存和价格数据不统一，就可能推荐缺货商品或报错价格，下单前最好在多个渠道确认。

3. 线下购物也越来越数字化：77%的人仍把实体店作为节假日购物首选，79%的人在店内会刷手机，12%会在货架旁咨询AI，如果线上线下的库存或促销信息对不上，体验会大打折扣。

AI重构了品牌被发现的路径，品牌需要主动抢占AI推荐场景。

1. 发现渠道洗牌：品牌自有渠道发现产品比例下滑7%，传统搜索下滑15%，AI助手、社交AI、配送应用等新发现渠道增长38%，首个推荐机会大多脱离零售商管控。

2. 产品内容和数据必须为AI优化：42%从业者已提升产品内容质量，40%优化内容结构，39%提交标准化数据馈送，38%用自然语言重写产品描述；AI在解读潮湿气候用品、扁平足跑鞋、100新元以下礼品等意图，数据不完整就会失去曝光。

3. 统一数据是品牌竞争底座：仅25%企业完全打通客户数据，46%库存不同步，36%定价促销不一致，AI会放大这些问题并导致弃单；数据统一的企业中44%协作效率提升、42%留存增长，且更容易落地AI。

4. 实体门店体验需要线上对齐：86%的B2C从业者反馈消费者期待线下和线上同等个性化服务，79%消费者店内购物时用手机，需确保库存、促销和服务口径一致。

AI聊天是东南亚电商的新流量闸口，也是新风险点。

1. 增长机会明确：代理式搜索作为购物首步使用量同比增200%，AI聊天导流电商流量同比增幅150%-428%，而同期整体电商流量只有个位数至低两位数增长，抓住AI入口就能获得超额流量。

2. 消费需求变化要跟上：AI按意图而非关键词推荐，消费者会直接问适合潮湿气候的用品、适合扁平足的跑鞋、100新元以下的礼品，商品数据若不能被AI识别和理解，会直接失去曝光。

3. 同行已经行动：42%从业者提升产品内容质量，39%向AI搜索平台提交标准化数据馈送，38%用自然语言重写描述，28%已部署代理式AI，另有52%计划六个月内上线，反应慢会落后。

4. 风险提示：数据分散导致36%企业客户响应缓慢、32%无法衡量投入；AI推荐缺货或价格不一致会直接提高购物车放弃率，需要优先打通库存和价格数据。

AI消费场景对产品信息提出新标准，工厂可从数字化和设计上抓住机会。

1. AI按产品属性做推荐，产品参数和描述要结构化：潮湿气候用品、扁平足跑鞋、价格区间礼品等需求都会被AI调用标准化数据，工厂把产品信息整理成机器可读的格式，能提高被下游推荐的概率。

2. 数据价值凸显：40%零售商在优化内容结构，38%用自然语言重写产品描述，工厂可以提供更完整、更一致的属性数据，减少客户的数据维护负担，这本身也是竞争力。

3. 从数据统一需求中找商机：81%商家称供应商数量增加，但仅25%完全打通数据，36%客户响应缓慢、40%承担高维护成本，工厂可借助更规范的产品信息管理帮助客户解决痛点。

4. 实体店正在数字化：12%消费者在货架旁向AI咨询，产品包装和展示信息也需要支持AI查询，工厂可提前布局线下场景的数字内容。

服务商面对一个由AI入口引爆的数据整合与企业服务市场。

1. 核心趋势：代理式搜索和AI聊天成为购物起点，AI聊天导流电商流量增幅150%-428%，但AI无法修复混乱系统，反而暴露和放大原有体系的短板，这正是服务商切入时机。

2. 客户痛点具体可量化：81%商家供应商数量增加，但仅25%完全打通销售、服务、营销、电商数据；46%库存不同步，36%定价促销不一致，36%响应慢，32%无法衡量投入，40%维护成本高。

3. 解决方案方向包括产品内容优化（内容结构、标准化数据馈送、自然语言重写），全渠道库存和价格同步，以及统一数据平台；目前仅28%部署代理式AI，52%计划六个月内上线，部署需求旺盛。

4. 代理式AI已在实际场景产生收益：35%亚太落地企业正在客服、IT、商品运营多部门规模化推广，AI提升了满意度、个性化、运营效率和员工生产力，服务商可以围绕这些收益场景提供方案。

平台商需要适应AI成为购物入口的新生态，并帮助商家避免风险。

1. 流量格局已变：AI聊天导流电商流量增幅150%-428%，新发现渠道包括AI助手、社交AI、配送应用，消费者不再从品牌网站或搜索栏开启购物，平台若不嵌入这些AI场景会被边缘化。

2. 东南亚渠道高度分散：新加坡、雅加达、马尼拉、曼谷消费者在电商平台、TikTok、WhatsApp、配送应用、门店、官网间切换，AI进一步压缩发现路径，首个推荐机会多数脱离平台管控，平台需要为商家提供AI可读的数据接口和标准。

3. 信息错配是平台运营大坑：仅2%多渠道企业无故障，46%库存不同步、36%定价促销不一致，AI推荐缺货或报错价格会直接导致购物车放弃率上升，平台若能帮助商家同步库存和价格，会有效提升粘性。

4. 平台可提供内容优化工具和线下联动能力：42%商家在提升产品内容，39%提交数据馈送，还有12%消费者在货架旁问AI，平台在帮助商家优化内容和连接实体门店方面存在机会。Salesforce高管认为，消费者接触到的第一个品牌门面很可能是AI的回答，平台需要做好这个门面的对接。

东南亚零售正经历以AI为起点的购物链路重构，数据基础设施成为竞争关键。

1. 新动向：代理式搜索作为购物第一步的使用量同比增长200%，AI聊天导流电商流量增幅区间150%-428%，同期电商整体流量仅个位数至低双位数增长；品牌自有渠道发现比例下滑7%，传统搜索下滑15%，新发现渠道增长38%。

2. 新问题：数据分散和全渠道错配严重，81%企业供应商数量增加，仅25%完全打通客户数据；46%库存不同步，36%定价促销不一致，仅2%多渠道企业没有明显故障；AI推荐缺货或价格错误会放大缺陷，导致购物车放弃率上升。

3. 消费者行为变化：实体门店仍是节假日首选（77%），但线下购物高度数字化，79%消费者店内用手机，12%货架旁问AI，消费者期待线上线下同等个性化服务。

4. 商业模式启示：代理式AI在东南亚已进入规模化阶段（35%落地企业在多部门推广），数据统一的企业获得协作效率、留存率和AI落地效果的实际回报，未来商业模式将围绕数据质量和AI可识别性展开。

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AI is becoming the new starting point for shopping, and understanding these changes can help you make better purchasing decisions.

1. AI chats are reshaping product discovery: usage of agentic search as the first step in shopping rose 200% year over year, and e-commerce traffic driven by AI chats grew by as much as 428%. More and more consumers are discovering products through AI recommendations rather than traditional search.

2. Shopping with AI is convenient, but verify through multiple sources: AI can give product recommendations based on needs like climate, foot shape, and price. However, if brand inventory and pricing data are inconsistent, it may recommend out-of-stock items or show incorrect prices. It's best to confirm on multiple channels before ordering.

3. Offline shopping is also becoming more digital: 77% of people still choose physical stores as their first choice for holiday shopping, 79% use their phones in stores, and 12% consult AI while at the shelf. If online and offline inventory or promotional information doesn't match, the experience suffers significantly.

AI is reshaping how brands get discovered, and brands need to proactively capture AI recommendation scenarios.

1. Discovery channels are being reshuffled: brand-owned channels saw a 7% decline in product discovery, traditional search dropped 15%, while new discovery channels such as AI assistants, social AI, and delivery apps grew 38%. Most first recommendation opportunities now sit outside retailers' control.

2. Product content and data must be optimized for AI: 42% of practitioners have improved product content quality, 40% optimized content structure, 39% submitted standardized data feeds, and 38% rewrote product descriptions in natural language. AI is interpreting intents such as products for humid climates, running shoes for flat feet, and gifts under SGD 100. Incomplete data means lost visibility.

3. Unified data is the foundation for brand competitiveness: only 25% of companies have fully connected customer data, 46% have unsynchronized inventory, and 36% have inconsistent pricing and promotions. AI amplifies these issues and leads to abandoned carts. Among companies with unified data, 44% saw improved collaboration efficiency, 42% saw retention growth, and they found it easier to deploy AI.

4. Physical store experience needs to align with online: 86% of B2C practitioners report that consumers expect the same level of personalization offline as online, and 79% of consumers use their phones while shopping in stores. Brands need to ensure consistency across inventory, promotions, and service messaging.

AI chats are a new traffic gateway for Southeast Asian e-commerce, but also a new source of risk.

1. Clear growth opportunity: usage of agentic search as the first shopping step grew 200% year over year, and AI chat-driven e-commerce traffic increased by 150% to 428%, while overall e-commerce traffic grew only by single digits to low double digits over the same period. Capturing the AI entry point can generate outsized traffic.

2. Consumer needs are shifting: AI recommends based on intent, not keywords. Consumers directly ask for products for humid climates, running shoes for flat feet, and gifts under SGD 100. If product data cannot be recognized and understood by AI, sellers will lose visibility.

3. Competitors are already acting: 42% of practitioners have improved product content quality, 39% submit standardized data feeds to AI search platforms, 38% rewrite descriptions in natural language, 28% have deployed agentic AI, and another 52% plan to launch within six months. Being slow means falling behind.

4. Risk warning: fragmented data leads to slow customer response for 36% of companies and an inability to measure investment for 32%. When AI recommends out-of-stock items or shows inconsistent pricing, cart abandonment rates rise. Prioritizing integration of inventory and pricing data is essential.

AI-driven shopping scenarios are setting new standards for product information, giving factories opportunities in digitalization and design.

1. AI recommends products based on attributes, so product parameters and descriptions must be structured: needs like products for humid climates, running shoes for flat feet, and gifts in a specific price range all require standardized data that AI can call upon. Factories that organize product information into machine-readable formats increase their chances of being recommended downstream.

2. Data value is becoming clear: 40% of retailers are optimizing content structure, and 38% are rewriting product descriptions in natural language. Factories that provide more complete and consistent attribute data reduce customers' data maintenance burden. This is itself a competitive advantage.

3. The need for data unification creates business opportunities: 81% of merchants say the number of suppliers is increasing, but only 25% have fully integrated data. 36% have slow customer response, and 40% bear high maintenance costs. Factories can help customers solve these pain points through more standardized product information management.

4. Physical stores are digitalizing: 12% of consumers consult AI while at the shelf. Product packaging and display information must also support AI queries, so factories can prepare digital content for offline scenarios in advance.

Service providers now face a data integration and enterprise services market ignited by AI entry points.

1. Core trend: agentic search and AI chats have become the starting point for shopping, with AI chat-driven e-commerce traffic growing 150% to 428%. But AI cannot fix chaotic systems; instead, it exposes and amplifies the weaknesses of existing infrastructure. This is exactly the moment for service providers to step in.

2. Client pain points are concrete and quantifiable: 81% of merchants have seen an increase in the number of suppliers, but only 25% have fully connected sales, service, marketing, and e-commerce data; 46% have unsynchronized inventory, 36% have inconsistent pricing and promotions, 36% have slow response times, 32% cannot measure investment, and 40% face high maintenance costs.

3. Solution areas include product content optimization (content structure, standardized data feeds, natural language rewriting), omnichannel inventory and price synchronization, and unified data platforms. Currently only 28% have deployed agentic AI, while 52% plan to launch within six months. Deployment demand is strong.

4. Agentic AI is already generating returns in real scenarios: 35% of companies that have deployed it in Asia-Pacific are scaling it across multiple departments such as customer service, IT, and merchandising operations. AI has improved satisfaction, personalization, operational efficiency, and employee productivity. Service providers can build offerings around these value scenarios.

Marketplace platforms need to adapt to a new ecosystem where AI is becoming the shopping entry point, and help merchants avoid risks.

1. The traffic landscape has changed: AI chat-driven e-commerce traffic has grown 150% to 428%, and new discovery channels include AI assistants, social AI, and delivery apps. Consumers no longer start shopping from brand websites or search bars. Platforms that fail to integrate into these AI scenarios risk being marginalized.

2. Southeast Asian channels are highly fragmented: consumers in Singapore, Jakarta, Manila, and Bangkok switch between e-commerce platforms, TikTok, WhatsApp, delivery apps, physical stores, and official websites. AI further compresses the discovery path, and most first recommendation opportunities are beyond platform control. Platforms need to provide AI-readable data interfaces and standards for merchants.

3. Information mismatch is a major pitfall for platform operations: only 2% of multi-channel businesses are fault-free, 46% have unsynchronized inventory, and 36% have inconsistent pricing and promotions. When AI recommends out-of-stock items or incorrect prices, cart abandonment rates rise directly. Platforms that help merchants synchronize inventory and pricing can effectively improve stickiness.

4. Platforms can provide content optimization tools and offline linkage capabilities: 42% of merchants are improving product content, 39% submit data feeds, and 12% of consumers ask AI while at the shelf. Platforms have opportunities to help merchants optimize content and connect physical stores. A Salesforce executive said that the first brand touchpoint a consumer encounters is likely to be an AI answer, and platforms need to support that touchpoint.

Southeast Asian retail is undergoing a reshaping of the shopping journey with AI as the starting point, and data infrastructure has become the key to competition.

1. New trends: usage of agentic search as the first step in shopping grew 200% year over year; AI chat-driven e-commerce traffic grew by 150% to 428%, while overall e-commerce traffic grew only by single digits to low double digits. Brand-owned channels' share of discovery fell 7%, traditional search dropped 15%, and new discovery channels grew 38%.

2. New problems: data fragmentation and omnichannel mismatch are severe. 81% of companies have seen an increase in the number of suppliers, only 25% have fully connected customer data, 46% have unsynchronized inventory, 36% have inconsistent pricing and promotions, and only 2% of multi-channel businesses have no obvious faults. AI recommending out-of-stock items or incorrect prices amplifies these flaws and leads to higher cart abandonment.

3. Consumer behavior changes: physical stores remain the top choice for holidays (77%), but offline shopping is highly digitalized: 79% of consumers use their phones in stores, 12% consult AI at the shelf, and consumers expect the same level of personalized service online and offline.

4. Business model implications: agentic AI has entered a scaling phase in Southeast Asia (35% of deployed companies are rolling it out across departments). Companies with unified data are seeing tangible returns in collaboration efficiency, retention, and AI implementation outcomes. Future business models will revolve around data quality and AI discoverability.

Disclaimer: The "Quick Summary" content is entirely generated by AI. Please exercise discretion when interpreting the information. For issues or corrections, please email run@ebrun.com .

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2026年10月发布的Salesforce第四版《商业状况报告》显示，代理式搜索作为购物旅程首步的使用量同比增长200%。本次调研覆盖20个国家13个行业的3450名商业从业者，含100名新加坡受访者，同时收集了4689名消费者的调研反馈，以及37个国家超15亿购物者的行为数据。

各统计季度内，AI聊天导流的电商流量同比增幅区间为150%到428%，同期电商整体流量仅实现个位数至低双位数增长。2025年8月至2026年5月，消费者通过品牌自有渠道发现产品的比例下滑7%，传统搜索渠道的相关占比下滑15%，包含AI助手、社交平台AI、配送应用在内的新发现渠道占比增长38%。

东南亚电商环境本就高度分散，新加坡、雅加达、马尼拉、曼谷等地的消费者下单前往往会在第三方电商平台、TikTok、WhatsApp、配送应用、实体门店、品牌官网多渠道切换。AI的加入进一步压缩了产品发现路径，单轮对话即可完成初步筛选，这也意味着品牌获得的首个推荐机会大概率脱离零售商的直接管控范围。

82%的新加坡受访商业负责人判断，大语言模型将在未来一年内成为产品发现环节的必备工具。当地零售从业者已开始调整运营策略，42%的从业者着手提升产品内容质量，40%针对对话式查询优化内容结构，39%向AI搜索平台提交标准化数据馈送，38%采用自然语言重写产品描述，核心目标是让机器能够准确识别、理解品牌商品目录。传统搜索仅匹配关键词，AI助手则会解读用户意图，对适配潮湿气候的用品、适合扁平足的跑鞋、100新元以下的礼品这类需求给出直接推荐，产品数据不完整、不一致、结构混乱的品牌，将直接在这类对话场景中失去曝光机会。

目前仅28%的新加坡受访企业已部署代理式AI，未部署的企业中有52%计划在未来六个月内完成上线。86%的商业负责人认为AI正在抬高消费者预期，41%的受访者坦言当前满足这类预期的难度远超以往，落地或拓展AI应用既是他们来年的首要工作方向，也是他们预判的最大挑战。

代理式AI指可围绕预设目标自主采取行动的AI系统，区别于仅能生成文本、回答问题的普通聊天工具。在商业场景中，这类AI可帮助消费者对比产品参数、查询库存状态、推荐组合商品、处理退货申请、转接服务请求。完全自动化采购目前仍在发展早期，但AI在产品发现和消费决策阶段的影响力正在快速攀升。亚太区已落地代理式AI的企业中，仅5%仍处于用例测试阶段，占比最高的35%的企业正在客服、IT、商品运营等多个部门和团队规模化推广相关应用。已落地企业普遍反馈，AI应用帮助他们提升了客户满意度、个性化服务水平、运营效率和员工生产力，这类增益对零售利润偏薄、获客成本持续走高的亚太市场吸引力显著。

AI无法自动修复底层系统的混乱，反而会直接暴露原有体系的短板。新加坡受访商业负责人中，81%称过去两年企业合作的供应商数量持续增加，仅25%的企业实现了销售、服务、营销、电商板块客户数据的完全打通。数据分散的企业中，36%存在客户问题响应缓慢或无效的状况，32%无法准确衡量商业投入的实际效果，40%需要承担高额的分散系统维护成本。

全渠道运营同样存在明显漏洞，仅2%的新加坡多渠道运营企业称没有明显的故障节点。最常见的问题为库存无法实时同步，获46%的受访者提及，其次是定价和促销信息不一致，占比36%。这些问题在传统渠道中已存在，AI普及后会被进一步放大，若AI助手推荐了缺货商品、报错价格，或是给出的建议和门店店员口径不一致，消费体验会直接断裂。在东南亚这类消费者习惯跨渠道比价的市场，这类信息错配会直接导致购物车放弃率上升，消耗用户对品牌的信任。

实体零售同样被纳入这一数字链路。调研数据显示，77%的消费者仍将实体门店作为节假日购物的首选渠道，但线下购物的线上关联度持续提升。79%的消费者在门店购物时会使用手机，12%的消费者会在货架旁向AI助手咨询购买建议，86%的B2C受访从业者反馈，消费者期待在线下获得和线上同等水平的个性化服务。

已在数据统一领域取得进展的企业已经获得实际回报，44%的相关企业称销售、营销、电商团队的协作效率明显提升，42%实现了客户留存率和忠诚度的增长，31%获得了更好的AI和自动化落地效果。数据基础设施已经脱离后台运维范畴，成为直接影响市场竞争力的核心要素。随着AI成为新的购物入口，零售商需要保证产品信息、用户上下文、促销规则、库存状态、服务记录等数据能在所有渠道顺畅流转。

Salesforce亚太区区域副总裁Swatantra Kumar称，消费者不再从品牌网站或搜索栏开启购物流程，他们的起点是AI聊天界面、社交信息流或配送应用。同步布局线上线下业务的企业需要在所有用户可能发现产品的场景完成触达，而这一切的前提是企业数据足够统一，能够支撑AI准确呈现自身产品和品牌信息。对东南亚电商从业者而言，AI是否重要已经不需要讨论，真正的考验是现有运营系统能否适配新的消费环境，在这个环境中，消费者接触到的第一个品牌门面，很可能不是实体店铺或品牌官网，而是AI助手生成的一句回答。

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