Decoration Enterprise GEO Case | Practical Review: Brand Mention Rate from 0 to 60% on AI Platforms
Challenge
The client is a leading local decoration and renovation enterprise in China, deeply engaged in the local home decoration and commercial construction track, with a mature construction system and real project cases. However, as local users' information access has shifted from traditional search engines and local life platforms to six major AI platforms including Doubao, DeepSeek, Kimi, Tongyi Qianwen, Wenxin Yiyan and Tencent Yuanbao, the brand mention rate of the enterprise in AI Q&A scenarios has remained at 0% for a long time, leaving the AI natural traffic channel completely vacant. Core Challenges: 1.Complete silence in local recommendation scenarios: For core questions such as "local decoration company recommendation" and "best local old house renovation service", the enterprise has a 0% mention rate in mainstream large models like Doubao and DeepSeek. It has never appeared on the platform recommendation list, resulting in a complete break in AI natural customer acquisition channels. 2.Insufficient structured content supply: There is a lack of structured information across the network, including real cases, construction techniques, service scope and customer reviews. Large models have no valid materials to invoke, and the brand is directly overlooked when users compare local decoration enterprises horizontally.
Solution
1. Build a local demand keyword matrix to adapt to AI local search logic Focusing on high-frequency demands of local users such as new house decoration, commercial construction and old house renovation, we build a three-tier local keyword matrix of "region + category + demand", align with the semantic logic of large models for local search, supplement the lexical source coverage of the brand in AI search scenarios, and broaden the access path for local users. 2. Output structured Q&A content to supplement invocable materials for large models Targeting the content capture preferences of large models, we systematically output structured Q&A content including construction technique interpretation, real project cases and service advantage descriptions, transforming scattered enterprise information into semantic fragments that can be directly extracted and cited by large models, solving the core problem that AI has no valid content to recommend.