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CaliberMind&MartechTribe：2026年营销归因状况报告：构建营销控制塔（英文版）（26页）.pdf

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1、April,2026The State of Marketing Attribution 2026A Research Report by Scott Brinker and Frans RiemersmaSPONSORED BY:Building the Marketing Control TowerThe State of Marketing Attribution 2026SponsorsAuthorsThis report is sponsored by CaliberMind.2026 Marketing Technology Media,LLC and MartechTribe B

2、.V.Design and cover art by Angela Ribeiro da Silva at Dattura StudioScott Brinker,dubbed the“godfather of martech”by Ad Age,has been analyzing martech and its impact on marketing organizations for more than 17 years as the editor of the chiefmartec blog.Previously,he served as VP platform ecosystem

3、at HubSpot,and before that he was co-founder and CTO of ion interactive,a pioneering no-code platform for creating interactive content.He authored the best-selling book Hacking Marketing and co-authored the recent best-seller The New Automation Mindset.He holds graduate degrees in computer science a

4、nd management from Harvard and MIT.He jointly produces the MartechMap marketing technology landscape with Frans.Frans Riemersma is a marketing technologist,researcher,and advisor.With over 30 years of consulting experience,he blends qualitative expertise with quantitative Martech data on stacks,vend

5、ors,and requirements,all consolidated in a proprietary Martech Data Warehouse.These insights power industry reports and tailored benchmarks for global enterprises through his company,MartechTribe,which specializes in Martech research and benchmarking.Together with Scott Brinker,he produces the Marte

6、chMap,the definitive overview of the marketing technology landscape.Frans is the author of A Small Book on Customer Technology,co-author of Marketing Tech Monitor and Hello Firstname,and a regular contributor to Martech.org and CMSwire.He frequently lectures at Business Universities,Business Schools

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1. **营销归因未达预期**：传统归因聚焦“转化”，而非“收入”，导致营销在预算削减中首当其冲，无法体现真实价值。 2. **两大核心转变**： - **技术层面**：生成式AI增加推理层，归因从“数据管道”转向“上下文工程”，成为AI驱动的组织连接层。 - **组织层面**：人类角色从“操作者”升级为“价值工程师”，通过反向工程收入（识别高利润客户、关键触点）优化资源。 3. **实践关键**： - **结果导向**：从“谁得功劳”转向“驱动收入”，归因作为决策支持而非评分工具。 - **组织能力**：跨团队协作＞模型复杂度，需数据素养、故事力及明确治理。 - **数据架构**：优先“信号质量”而非“数据量”，归因作为“翻译层”连接系统与决策。 4. **经济逻辑**：80%收入来自20%高价值旅程，归因应聚焦“现金”而非“覆盖”，驱动战略取舍。
归因失效了吗？ 价值如何归因？ AI如何重塑归因？
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