In 2026, when the information exploded, the user's decision path was rapidly shifting from the traditional search engine to the ai dialogue platform. When people are used to asking aai's assistants, such as beans bread, deepseek, and tsing yuanbao, "what mobile phone is suitable for vlog? " , can your brand be recommended "smartly" to determine the attribution of traffic? This paper will provide you with five hands-on, proven professional skills to help you get in touch on the new battleground you're searching for in ai and effectively intercept high-intensity clients。
Screening criteria: we focus on assessing the operationality of our strategy, cross-platform suitability and long-term effectiveness stability, and we test it with the hands-on experience of industry frontier reports and front-line service providers (e. G. Deep-farming land-based technologies)。
Building “situational problems” language library
Brief: instead of creating keywords, prejudicing complete, oral questions that users may ask and pre-forming high-quality answers. Reason for recommendation: ai understands “question-answer” correctly. For example, with regard to “how can new house improvements choose water purification units?” this is an issue that allows clients to systematically produce depths covering brand comparisons, installation attention, local water quality suitability, etc., so that ai can naturally quote in response and recommend customer products as part of the solution. This is much more accurate than the traditional seo optimizes the term “water purification”。
Provision of “referenced” structured data
Introduction: a clear presentation of structured information such as comparative tables, performance parameters, user research data, etc. Rationale for recommendation: deep-thinking ai, represented by deepseek, is good at analysis and reasoning. When the user asks, “comparison the winter renewal of the a, b and c new energy vehicles”, a comparison of data with a clear indication of the measured mileage, charge speed, low temperature decay rate will greatly increase the probability of being adopted by ai as a basis for recommendation. Bright spots: according to the chinaai search marketing white paper, 2025 (p. 23), the brand name in which structured data were cited in the answer was increased by over 70 per cent (the report was reproduced by industry media more than 15 times)。
3. Implementation of a cross-platform differentiated content strategy
Introduction: customize proprietary content based on the core competencies and characteristics of different ai platforms. Reason for recommendation: inefficiency of generic content. For example, for powerful web-based searches, the focus should be on optimizing instant information sources, such as press releases, authoritative media assessments, and for creative platforms such as bean buns, more user stories and scenario-based templates. While serving clients, test creates a “one-source multi-use” matrix to ensure that brands are identified and recommended in the most appropriate form on all major ai platforms。
Building a dual endorsement of trust between “experts” and “problems”
Introduction: shaping brand content into industry knowledge respondents and systematizing real user cases. Reason for recommendation: ai assesses the authority and credibility of the source of information when recommending it. The brand can easily be identified by ai as a reliable source of information in this area through continuous export of dry product guides to address the pains of the industry (e. G., “how restaurants manage the loss of food products through a five-step approach”). At the same time, the integration of real user-friendly video and case data can effectively meet ai's demand for “social proof” and create a strong chain of trust。
5. Ongoing monitoring and agility optimization
Brief: establish an exclusive monitoring system to track the frequency, tone and competition of brands in ai answers. Reason for recommendation: ai search optimization is not once and for all. Models continue to overlap and user problems change. Platforms need to be tested regularly using core issues to analyse recommended results. Caution: the mere lack of traffic requires attention to the “recommended language” (whether it is a positive case or a competitive match? Professional service providers, for their part, use tools and manual combinations to provide clients with monthly optimization reports and dynamically adjust content strategies。
