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对话深圳拓扑卢聪:AI与数据驱动,重构跨境出海独立站运营新范式

亿邦智库黄斌 2026-06-22 14:17
亿邦智库黄斌 2026/06/22 14:17

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本文分享了AI与数据驱动下跨境出海独立站运营的新范式,核心趋势是中国品牌出海已经从传统流量驱动转向AI与数据双轮驱动,有不少可落地的实操干货。

1. 核心行业变化:生成式引擎优化GEO正在替代传统SEO,是当前跨境圈最被低估的增长机会,深圳拓扑自身近一半新客户都来自DeepSeek、ChatGPT等AI工具的推荐,已有合作客户实现一年GEO流量增长百倍。

2. 起步实操建议:刚起步做独立站出海,要主动拥抱AI、数据和品牌,不要走传统铺货老路,要先搭建完善的数据指标体系,整合多方数据做前期调研少走弯路。用好AI就可以完成建站、广告素材制作、投放优化等工作,核心是要掌握AI提示词能力,把背景、需求、案例信息给到AI就能获得较好效果。

本文针对出海品牌布局独立站,分享了AI+数据时代品牌营销和运营的新趋势,对品牌建设和增长有较多参考价值。

1. 消费与渠道新趋势:当前跨境出海已经从流量驱动转向AI和数据双轮驱动,流量渠道正在发生变化,GEO(生成式引擎优化)是被低估的新增长渠道,AI推荐已经能贡献一半的新增客户流量,提前布局就能获得新增长。

2. 运营升级方向:品牌做独立站首先要搭建完善的数据指标体系,整合自身数据、二方三方数据以及行业大数据,提前了解行业规模、竞品布局和目标市场情况,避免试错走弯路。AI已经渗透独立站从调研到客服全链路,可实现AI解决80%的客服问题,提供7*24小时服务,大幅降低人力成本。

3. 核心提醒:品牌要放弃传统铺货模式,主动拥抱AI和数据,采用人+AI协同的模式,将依赖个人经验的运营变成可量化可复制的体系,提升出海成功率。

本文针对出海独立站卖家,分析了当前行业的新增长机会,指出了现存运营痛点,并给出了可落地的应对方案,对卖家把握机会提升增长有很大帮助。

1. 新增量机会:GEO生成式引擎优化是当前最被低估的增长机会,替代传统SEO的趋势已经显现,已有成功案例:NihaoJewelry饰品独立站通过相关运营,12个月月销从1.8万美金增长到84万美金,综合ROAS从不足4倍提升到12倍以上,另有扩音器品牌WinBridge3个月广告ROAS从1.5提升到4以上,自然转化率提升85%。

2. 现存核心瓶颈:多数卖家的内部瓶颈是缺乏数据指标体系的相关认知,不具备整合多源数据的能力,外部则面临平台数据壁垒和隐私政策变化的障碍,限制了数据价值释放。

3. 破局方法:卖家可以通过专业培训提升团队数据能力,也可以借力专业代运营服务,AI大幅降低了运营门槛,只要掌握提示词能力,就能借助AI完成原本需要三到五年经验才能做的运营工作,采用人+AI协同就能提升成功率。

本文对工厂转型跨境独立站出海,分析了现存的能力差距,指明了转型方向和新的商业机会,对工厂推进数字化电商转型有较多启示。

1. 转型基础差距:多数外贸OEM工厂没有基础的数据意识,仍然依赖传统展会、B2B平台获客,缺乏独立站运营需要的数据运用能力,和平台卖家、品牌商相比初始数据能力差距较大,这是转型的主要障碍。

2. 转型新机会:当前AI技术大幅降低了独立站运营的门槛,全链路从调研到客服都有AI赋能,工厂不需要培养大量经验丰富的运营人员,就能搭建独立站业务,开辟toC出海的新增长曲线,摆脱OEM的低利润困境。

3. 转型启示:工厂转型要主动拥抱AI和数据,首先要搭建适配独立站的数据指标体系,可以借助第三方服务商的能力,通过多源大数据提前调研行业和竞品,少走试错弯路,AI可解决八成客服问题,还能自动优化广告投放,有效降低运营成本,提升出海成功率。

本文分享了跨境独立站服务行业的最新发展趋势,以及AI时代服务商的升级路径和客户痛点解决方案,对同行有较高的参考价值。

1. 行业发展趋势:中国品牌出海独立站已经从流量驱动转向AI与数据双轮驱动,行业需要把原本依赖个人经验的运营,转化为可量化、可复制的科学体系,帮助客户实现确定性增长,GEO替代传统SEO是明确的新趋势,是当前行业未被充分挖掘的增长机会。

2. 客户核心痛点:绝大多数出海客户缺乏完善的数据指标体系认知,不具备整合多源数据的能力,多数中小客户无法找到自身独立站的问题所在,不同类型客户初始数据能力差异大,工厂、中小卖家能力缺口尤其明显。

3. 行业解决方案:可采用培训加代运营结合的服务模式,帮客户先搭建数据指标体系,整合多方大数据做前期调研,将AI渗透到服务全链路,同时要建立完善的数据安全保障体系,通过明确数据权属、案例脱敏、同区域同行业只服务一家、限制数据导出权限等方式保障客户数据安全,未来还可开发AI自动化诊断优化工具服务更多客户。

本文分析了当前独立站商家的核心需求和现存问题,对独立站平台优化运营、把握行业趋势、规避风险有较多启示。

1. 商家核心需求:当前商家普遍需要平台提供成熟的AI能力,帮助降低独立站运营门槛,完成数据分析、自动化建站、广告优化等工作,目前主流平台Shopify已经自带AI分析和自动化建站工具,符合商家需求,获得了市场认可。

2. 商家现存核心问题:商家除了自身数据能力不足,还面临外部平台数据壁垒和隐私政策变化的障碍,数据安全问题也是商家关注的核心痛点,很多商家对数据共享和使用存在顾虑。

3. 平台运营优化方向:平台可以加大AI工具的研发和开放,适配GEO新趋势,开发帮助商家优化内容体系获取AI推荐流量的工具,完善数据安全合规相关的规则设计,帮助商家保障数据安全,还可以和专业代运营服务商合作,为商家提供全链路服务,吸引更多商家入驻,同时要打破不必要的数据壁垒,在合规范围内开放更多可用数据给商家。

本文展现了当前中国跨境出海独立站领域的最新产业动向,以及AI赋能跨境服务的创新商业模式,为产业研究提供了鲜活的案例和新的方向。

1. 产业新动向:当前中国品牌出海独立站正从传统流量驱动转向AI与数据双轮驱动,AI已经重构了独立站从调研选品到客服全链路的运营逻辑,生成式引擎优化GEO正在替代传统SEO,成为新的流量增长入口,这一趋势目前刚刚起步,是未来产业重要的增长点。

2. 创新商业模式:国内已经出现AI化的独立站全链路代运营新范式,服务商通过自营独立站业务积累完整的运营经验和数据资产,再反哺代运营服务,既做运动员又做教练员,能够精准把握各环节痛点,将依赖个人经验的运营转化为可量化可复制的科学体系,提升了出海成功率。

3. 行业新问题与参考方案:当前行业的核心问题是多数出海企业数据能力不足,外部存在数据壁垒和隐私约束,数据安全是行业共性问题,深圳拓扑提出的明确权属、脱敏处理、同区域同行业单客户、权限分级等数据安全方案,为行业解决相关问题提供了可参考的实践样本。

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Quick Summary

This article shares a new paradigm for cross-border independent DTC site operations driven by AI and data. It notes that Chinese brands going global have shifted from the traditional traffic-driven model to an AI-and-data dual-driven model, and delivers many actionable operational insights.

1. Core industry change: Generative Engine Optimization (GEO) is replacing traditional SEO, and it is currently the most undervalued growth opportunity in the cross-border e-commerce space. Nearly half of Shenzhen Topology's new customers come from recommendations from AI tools such as DeepSeek and ChatGPT, and one of its existing clients has achieved a 100x increase in GEO-driven traffic within a year.

2. Practical startup advice: New entrants to cross-border independent DTC sites should proactively embrace AI, data and brand building instead of sticking to the old mass-listing model. They should first build a complete data indicator system and integrate multi-source data for early-stage research to avoid unnecessary detours. AI can already handle tasks including site building, advertising material creation and campaign optimization; the core skill required is mastering AI prompt engineering — providing clear context, requirements and case information to AI will deliver solid results.

This article shares new trends in AI-and-data era branding and operations for global brands building independent DTC sites, offering valuable insights for brand building and growth.

1. New consumer and channel trends: Cross-border brands have shifted from a traffic-driven model to an AI-and-data dual-driven model. GEO (Generative Engine Optimization) is an underrated new growth channel, and AI recommendations already contribute half of all new customer traffic. Early布局 in this space will unlock new growth.

2. Direction for operational upgrade: To build an independent DTC site, brands should first establish a complete data indicator system, integrate their first-party, second-party, third-party and industry-wide big data to gain early clarity on market size, competitor positioning and target market conditions, reducing costly trial and error. AI has penetrated the entire independent DTC site operation chain from research to customer service: it can handle 80% of customer inquiries and provide 24/7 service, cutting labor costs significantly.

3. Key reminder: Brands should abandon the traditional mass-listing model, proactively embrace AI and data, and adopt a human-AI collaboration model. This transforms experience-dependent operations into a quantifiable, replicable system, improving the odds of successful global expansion.

This article analyzes new growth opportunities for cross-border independent DTC site sellers, points out core operational pain points, and provides actionable solutions, helping sellers capture opportunities and boost growth.

1. New growth opportunity: GEO (Generative Engine Optimization) is the most undervalued growth opportunity today, and the trend of it replacing traditional SEO is already clear. There are proven success cases: NihaoJewelry, an independent jewelry DTC site, grew its monthly sales from $18,000 to $840,000 in 12 months and boosted its overall ROAS from less than 4x to over 12x through GEO-aligned operations. Another brand, speaker manufacturer WinBridge, increased its ad ROAS from 1.5 to over 4 in 3 months and lifted organic conversion rate by 85%.

2. Core bottlenecks: Most sellers face internal bottlenecks from lack of knowledge of data indicator systems and inability to integrate multi-source data. Externally, platform data barriers and changing privacy policies further limit the value they can extract from data.

3. Path to breakthrough: Sellers can upgrade their team's data capabilities through professional training, or leverage professional third-party managed operation services. AI has drastically lowered the operational threshold: as long as sellers master prompt engineering, AI can help them complete operational work that previously required three to five years of experience. A human-AI collaboration model significantly improves the odds of success.

This article analyzes existing capability gaps for OEM factories transitioning to cross-border independent DTC sites, outlines transition directions and new business opportunities, offering key insights for factories pursuing digital e-commerce transformation.

1. Foundational capability gaps for transition: Most foreign trade OEM factories lack basic data awareness, still rely on traditional trade shows and B2B platforms for customer acquisition, and do not have the data utilization capabilities required for independent DTC site operations. Their initial data capability gap compared to platform sellers and DTC brands is the main barrier to successful transition.

2. New transition opportunities: AI technology has drastically lowered the threshold for independent DTC site operations, and empowers the entire operation chain from research to customer service. Factories can build an independent DTC site business without hiring a large team of experienced operators, open up a new toC growth curve for global expansion, and escape the low-margin OEM trap.

3. Key takeaways for transition: Factories should proactively embrace AI and data, and first build a data indicator system adapted for independent DTC sites. They can leverage third-party service provider capabilities to conduct early research on industries and competitors through multi-source big data to avoid trial and error. AI can handle 80% of customer service inquiries and automatically optimize ad campaigns, effectively cutting operational costs and improving the odds of successful global expansion.

This article shares the latest development trends in the cross-border independent DTC site service industry, as well as upgrade paths for service providers in the AI era and solutions to customer pain points, offering high reference value for industry peers.

1. Industry development trends: Chinese brands building independent DTC sites have shifted from a traffic-driven model to an AI-and-data dual-driven model. The industry needs to transform experience-dependent operations into a quantifiable, replicable scientific system to help clients achieve predictable growth. GEO replacing traditional SEO is a clear new trend, and represents an under-explored growth opportunity for the industry today.

2. Core customer pain points: The vast majority of cross-border clients lack understanding of complete data indicator systems and the ability to integrate multi-source data. Most small and medium-sized clients cannot identify the core problems with their independent DTC sites. Initial data capability varies widely across different client types, with factories and small and medium-sized sellers facing particularly large capability gaps.

3. Industry solutions: Service providers can adopt a combined training and managed operation service model to help clients first build a data indicator system and integrate multi-source big data for early-stage research. AI should be embedded across the entire service delivery chain. Service providers also need to build a complete data security guarantee system: clarifying data ownership, anonymizing client case data, serving only one client per region per industry, and restricting data export permissions to protect client data security. In the future, providers can also develop AI-powered automated diagnosis and optimization tools to serve more clients.

This article analyzes the core needs and existing problems of independent DTC site merchants, offering insights for independent DTC platform operators to optimize operations, capture industry trends and mitigate risks.

1. Core merchant needs: Merchants generally require platforms to provide mature AI capabilities to lower the operational threshold for independent DTC sites, supporting tasks including data analysis, automated site building and ad optimization. Leading platform Shopify already offers built-in AI analysis and automated site building tools, which align with merchant needs and have earned market recognition.

2. Core existing problems for merchants: In addition to their own insufficient data capabilities, merchants also face external barriers from platform data barriers and changing privacy policies. Data security is also a core pain point for merchants, and many have concerns about data sharing and usage.

3. Direction for platform operational optimization: Platforms can increase investment in R&D and opening up of AI tools to adapt to the new GEO trend, develop tools that help merchants optimize their content systems to capture AI recommendation traffic, and improve rule design around data security and compliance to help merchants protect data security. They can also partner with professional managed operation service providers to offer end-to-end services for merchants to attract more sellers to onboard. Platforms should also break down unnecessary data barriers and open up more usable data to merchants within compliance limits.

This article presents the latest industry developments in China's cross-border independent DTC site space and innovative business models of AI-enabled cross-border services, providing fresh cases and new research directions for industrial research.

1. New industry developments: Chinese brands building independent DTC sites are shifting from the traditional traffic-driven model to an AI-and-data dual-driven model. AI has already restructured the entire independent DTC operation logic from product research and selection to customer service. Generative Engine Optimization (GEO) is replacing traditional SEO to become a new traffic growth entry point. This trend is still in its early stage and will become an important industrial growth driver in the future.

2. Innovative business models: A new AI-powered end-to-end managed operation model for independent DTC sites has emerged in China. Service providers accumulate complete operational experience and data assets through operating their own independent DTC sites, then apply that experience to their managed operation services — acting as both operator and advisor, allowing them to accurately address pain points across all links, transform experience-dependent operations into a quantifiable, replicable scientific system, and improve the success rate of global expansion.

3. New industry problems and reference solutions: The core problem facing the industry today is that most cross-border enterprises lack sufficient data capabilities, while facing external data barriers and privacy constraints. Data security is a common industry challenge. The data security framework proposed by Shenzhen Topology — including clarifying data ownership, data anonymization, serving only one client per region per industry, and permission-based data access — provides a actionable practical reference for the industry to address these challenges.

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 .

I am a Brand Seller Factory Service Provider Marketplace Seller Researcher Read it again.

【亿邦原创】在全球化与数字化浪潮的交汇处,一批以AI和数据驱动的跨境服务商正在重塑中国品牌出海的底层逻辑。深圳拓扑的故事始于2016年——从Google与Facebook营销服务起步,2020年切入独立站自营业务实现高速增长,如今已成长为服务超300家出海企业的独立站全链路代运营服务商。近日,我们与深圳拓扑创始人卢聪进行了一次深入对话,探讨了AI如何重构独立站运营的竞争力、GEO正在替代传统SEO的行业趋势,以及代运营模式的AI化升级路径。

“让成功出海从偶然变得更必然。”卢聪开门见山。这句口号背后,是深圳拓扑对数据要素和AI技术的深度信仰——将过去依赖个人经验的独立站运营,转变为一套可量化、可复制、高成功率的科学体系。

亿邦智库:从“让成功出海从偶然变得更必然”这一口号出发,深圳拓扑是如何利用“数据要素”和AI技术,将过去依赖个人经验(如选品、广告投放)的独立站运营,转变为一套可量化、可复制、高成功率的科学体系?

卢聪:很多卖家从平台过渡到独立站,是一个巨大跨越。在平台上,大家看的数据指标很少——流量、加购率、CPC、广告费、ROAS,大概十个左右。但换到Facebook上,总共有五百个数据指标。你能看多少指标,决定了你广告能力的高低。我们做了很多客户,他们独立站做不成功,其中一个原因就是连数据指标体系的知识都没有,更别说运用了。

今天的数据来源不只是一方数据——自己网站的销售额、加购、结账、用户注册。还有大量二方、三方数据,甚至行业大数据。比如SimilarWeb、SEMrush,我们做任何一个品类,第一步都是通过这些工具获取行业大数据——了解行业规模、增速、竞品怎么做、流量来源、在什么国家。这为客户提供了非常好的前期指引,不用走弯路。

过去这些区别都要通过大量自身一方数据去洞察和调整。但现在,大量大数据可以在做之前就告诉我们行业标杆怎么做——抄作业就是走捷径。

亿邦智库:您提到公司目前近一半的新客户来自DeepSeek、ChatGPT等AI工具的推荐。这是否可以看作是“GEO(生成式引擎优化)”替代传统SEO的一个标志性案例?

卢聪:我们公司目前的业务,差不多一半的用户来源是通过DeepSeek、豆包、ChatGPT等AI推荐的流量咨询来的。这确实是一个标志性的变化。

GEO是跨境圈目前最被低估的增长机会。我们结合过往SEO的经验(我2012年在新加坡入行SEO)整理出一套GEO方法论,再结合代运营客户落地实践,取得了不错的成效。我们自身也有做过一年GEO流量增长百倍的客户。

亿邦智库:深圳拓扑服务超过300家企业,涵盖工厂、平台卖家、品牌商。这三类客户在数据应用能力上的初始差距在哪里?

卢聪:差距比较大。有一些外贸工厂和OEM企业,可以说没有基本的数据意识,传统展会、B2B平台是他们获取客户的主要方式。亚马逊卖家稍微好一点,但他们的数据视野基本局限在平台内部的十几个指标。品牌商相对好一些,但真正能把数据用好的也不多。

我们帮客户做的第一件事,往往不是投放广告,而是帮他们建立数据指标体系——告诉他们应该看什么、怎么看、怎么用。绝大多数跨境电商卖家不具备这种数据运用能力,不懂得这些工具,没法收集数据,看到了也不懂怎么整合分析。

亿邦智库:从选品调研、建站、引流到客服,AI技术具体渗透到了“七步服务体系”的哪些环节?

卢聪:我们的服务流程一般可以概括为七个环节,即:调研、建站、营销广告、运营、支付、物流、客服。当前的工作中,基本每个环节都有AI参与。譬如,调研环节,我们通过SimilarWeb等工具获取行业大数据,再用AI做数据整合和分析。建站环节,Shopify等主流平台本身就有很好的AI工具帮商户做分析和自动化建站。广告营销环节,谷歌、Meta自身的AI会帮我们做优化,有很多工具可以自动化调整预算和出价。运营环节,AI帮我们做A/B测试、热力图分析、转化率优化。等等。

客服环节变化最大。以前我们经营自主品牌时用菲律宾客服团队,现在AI能解决百分之八十的客服问题,包括售前咨询、售后查询和投诉。我们用AI工具实现7×24小时智能客服。

亿邦智库:公司拥有国内外多元化的团队,在AI辅助内容本地化和用户行为分析方面,这套全球化数据网络是如何协同工作的?

卢聪:我工作一开始就是国际化团队——毕业后在新加坡工作,有很多菲律宾人、印度人、马来西亚人。我们现在做的事情,是利用中国供应链的优势走向海外。未来,国际化企业应该用好全球化的人力优势。

客服团队在菲律宾,大概六个人,成本低,能倒班,语言过关。技术性事务,如建站、速度优化,我们跟印度团队合作了四年多,他们成本低,专业能力强。未来服务美国本土企业,也会找美国人做创意、设计。

内部培养方面,我们每个同事都会先去调研本地节日,看本地社媒,了解他们认同什么样的传播形式和广告表现形式,然后应用到自己的广告上。核心方法论是通用的,但打法要根据不同国家变通。

亿邦智库:作为服务商,深圳拓扑如何在利用跨客户行业数据的同时,保障客户的数据安全呢?

卢聪:这个问题我们非常重视。首先,数据我们只是管理者和使用者,客户是所有者,这个界定非常清楚。我们只是管家,不能把主人的钱和家具搬走。

第二,用于公开课或案例展示时,中间都会脱敏——不会出现品牌信息或数据原始截图,对数据做模糊处理。

第三,我们服务客户时直接承诺:同一个行业、同一个产品、在一个国度里,我们只签一家客户。这样就避免了同业竞争和数据滥用。我们没有动力去把数据用到别的地方。

制度与文化上有讲究,技术上也要有要求,比如网站后台导出用户和数据,我们有规范——很多账户不给导出权限,你可以用但不能导出。

亿邦智库:您提到GEO是跨境圈最被低估的增长机会,能否以具体案例说明GEO策略与传统SEO的本质区别?

卢聪:以NihaoJewelry为例,这个饰品独立站通过我们的代运营服务,12个月月销从1.8万美金增长到84万美金,综合ROAS从不足4倍提升至12倍以上。

传统SEO做的是关键词排名,盯着搜索引擎的搜索结果页。但GEO做的是让AI推荐引擎——比如DeepSeek、ChatGPT、豆包——在回答用户问题时推荐你的品牌。这就完全改变了内容策略:你不再只是优化关键词密度,而是要构建能被AI理解和推荐的内容体系。

这个趋势才刚刚开始。

亿邦智库:案例中提到“3个月广告综合ROAS从1.5提升至4+”,AI技术发挥了多大作用?未来AI是否会完全取代人工优化师?

卢聪:以WinBridge(扩音器,美国市场)为例,3个月广告综合ROAS从1.5提升至4+,自然转化率提升85%。这里面AI的作用非常大——自动竞价、动态创意、受众预测,这些都在Google和Meta的AI系统里内置了。

但AI会不会完全取代人工优化师?我觉得不会。AI极大地降低了门槛,但怎么样用好AI这个能力,还是有一定要求。未来AI一定是提升很多公司数据运用能力的利器,很多公司还没有这种意识,还没用。用的人,可能一两年就赶上别人十年的积累,完全有可能。但这不是说个人多厉害,是“人+AI”协同的厉害。

亿邦智库:公司曾自营toC业务并保持高增长,这段“既当教练员又当运动员”的经历,积累了哪些独家的能力?

卢聪:2020年疫情期间我们启动跨境电商独立站toC业务,在两个产品品类保持着月均20%以上的增长。这段经历让我们积累了完整的独立站综合运营能力——从选品、建站、投放到物流、客服,每个环节我们都亲自跑过。

更重要的是,我们积累了一套完整的数据资产——用户行为模型、选品算法、广告投放模型。这些能力反哺到给客户的服务中,让我们不只是“纸上谈兵”,而是真正知道每个环节的痛点和解决方案。

亿邦智库:当前制约独立站卖家释放数据价值的最大瓶颈是什么?深圳拓扑如何帮助客户克服这些约束呢?

卢聪:内部瓶颈首先是团队认知——正如前文所说,很多卖家连数据指标体系的知识都没有,这是主要的内部瓶颈。外部障碍主要是平台数据壁垒和隐私政策变化。

我们的解决办法有两个。第一种是帮助企业提升内力——开展培训、公开课、直播。我们在全国20多个城市开办超过100期独立站、Facebook等课程,累计培训超过3000名学员。第二种是推荐企业引入外力——代运营,借助外部专业能力。

亿邦智库:面向未来,深圳拓扑在“AI+数据”方面有哪些新的战略规划?

卢聪:这方面是一个新命题,我们现在还在实践,还没有上升到很清晰的战略。但模糊的方向是明确的——未来是AI的,但更重要的是数据。

第一,GEO是我们非常重视的方向。第二,利用好Shopify自身的AI系统,做自动化建站和数据分析。第三,借助国外顶尖的AI模型,帮我们建站、分析、定策略。

我们招了一些应届生,他们大量接触AI后,很快就把三年、五年、十年经验的人的东西学会了。所以AI对就业是有帮助的——它在创造原本人做不了的事情。

我们一直在思考,未来是不是把AI能力设计成一个产品——比如借助AI自动诊断网站、自动化优化。我几乎每周见五到十个客户,发现绝大多数公司不知道问题在哪里。如果能把诊断经验内化成工具,给大量独立站卖家使用,会很有价值。

亿邦智库:如果请您给那些刚起步、渴望构建自身数据竞争力的出海品牌一条最核心的建议,这条建议会是什么?

卢聪:一句话——主动拥抱AI、数据和品牌。不能像过去做跨境电商平台那样铺店铺、铺链接、铺货,这个模式越来越走不通了。一旦重视AI、重视数据、重视品牌,就开启了一个全新的篇章。用AI构建数据处理能力——就像你们提出的数据要素竞争力模型,要从“获、治、用、安”四个维度入手,而AI在各个环节都可以帮到你。

未来做独立站,你只要会问问题、会提需求就可以了。AI可以帮你做广告素材、投放计划、页面建站优化、自动化流程设计、数据获取分析。这些传统上需要三到五年经验的人才能做的事,AI都可以做。

当然,提示词能力很关键。AI提示词是一个对话过程,不是单向提问。善于表达,把背景、需求、案例、关联信息给到AI,它就用得很好。

结语:“让成功出海从偶然变得更必然”

深圳拓扑的实践表明,中国品牌出海正从“流量驱动”转向“数据与AI双轮驱动”。从选品调研到广告投放,从网站运营到客服体系,AI正在重构独立站运营的每一个环节。而GEO这一被低估的增长机会,正在为敢于拥抱变化的企业打开新的增长空间。

“让成功出海从偶然变得更必然”——这不仅是深圳拓扑的使命宣言,更是AI与数据时代给所有出海企业的一份确定性承诺。在技术与全球化深度融合的今天,深圳拓扑的探索,或许为中国品牌独立站出海提供着一种新的范式。亿邦智库将持续关注相关产业与企业数据要素竞争力提升,并报道相关发展的新成果与新案例。

      联系邮箱为:huangbin@ebrun.com



文章来源:亿邦智库

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