1. 系统定位
JefurryAxis 用于分析品牌在大语言模型回答中的可见度、推荐位置、竞品压力和外部内容基础，并把这些诊断结果转化为内容、渠道与品牌认知优化的依据。
传统品牌分析常关注官网流量、广告投放、搜索排名、媒体声量、销售转化等指标。JefurryAxis 关注的是另一类问题：当目标受众向大语言模型提出真实决策问题时，模型是否会把某个品牌纳入回答、是否优先推荐、是否给予足够解释、是否更倾向提到竞品，以及这种表现背后的内容来源是什么。
因此，JefurryAxis 衡量的不是品牌“客观上是否优秀”，而是品牌在 AI 搜索和 LLM 推荐环境中的可见性。一个品牌可以在传统市场中很强，但在 LLM 回答中表现一般；这通常说明品牌的公开内容、第三方引用、结构化资料或目标语境覆盖尚未充分转化为 AI 可见度。
从完整业务价值看，系统形成“测量、诊断、优化、持续监测”的闭环。Detection 负责构建测量空间并识别薄弱人群、场景、模型和内容来源；后续优化与持续监测负责把诊断转化为品牌经营信号。报告不是业务终点，而是连接品牌认知现状与后续优化方向的诊断层。
2. 核心业务链路
先把几个核心概念分清：
| 概念 | 含义 |
|---|---|
| 公开品牌 | 市场中真实存在的品牌、公司、机构或产品品牌 |
| 业务空间 | 公开品牌下面被单独拿出来测量的一块产品、业务或服务范围 |
| Brand 工作空间 | 系统中承载某个业务空间的 Brand 记录；它不只是品牌名，而是由 Brand Name、Core Keywords 和业务描述共同表达 |
| Detection Run | 在某个既定 Brand 工作空间内进行的一次测量 |
业务空间不是一个随意的关键词集合，也不是整个公司所有业务的总和。它的核心是“这次到底测品牌的哪块业务”。判断业务空间时，主要看产品或服务范围、目标客户、用户决策链和竞品环境是否一致。若这些因素明显不同，就说明可能存在不同业务空间。
Brand 工作空间是系统里的承载对象。Brand Name 说明测量哪个品牌，Core Keywords 说明围绕这个品牌的哪块业务测，Business Description 帮助解释这块业务到底是什么。三者合在一起，才让系统知道当前 Brand 工作空间代表的业务空间。
Detection Run 是在这个既定 Brand 工作空间内进行的一次完整测量。它基于当时的 Brand Name、Core Keywords、业务知识、受众语境、生成样本、Run Settings 和 Tested LLMs，形成一批模型回答并聚合为报告。Detection Run 不负责临时重新划分业务线；业务空间应在 Brand 工作空间层面先定义清楚。
同一个公开品牌可以围绕不同业务空间建立不同 Brand 工作空间；同一 Brand 工作空间也可以持续产生多次 Detection Run。只有当业务空间和主要测量条件一致时，多次 Detection Run 才适合做趋势比较。
JefurryAxis 的分析链路可以概括为：
品牌业务信息
-> 客群标签空间
-> 目标受众画像
-> 决策场景
-> 自然语言问题
-> 多模型回答
-> 品牌与竞品识别
-> 可见度、排名、内容占比、来源与竞品压力分析这条链路的关键特征是上游决定下游。品牌信息决定标签空间，标签空间影响 persona，persona 影响 scenario，scenario 影响 query，query 决定模型回答的语境，模型回答最终决定报告指标。
因此，JefurryAxis 的报告不是孤立的分数输出，而是一套由输入变量、生成样本和模型回答共同形成的测量实验。
整条链路由两条相互配合的业务主线构成：
| 主线 | 核心问题 | 主要对象 |
|---|---|---|
| 测量对象与业务空间 | 测量谁，以及围绕哪一类产品、业务或服务进行测量 | Brand Name、Core Keywords |
| 用户视角与决策空间 | 从谁的视角、在什么情境下、通过什么问题进行测量 | Tags、Personas、Scenarios、Queries |
Brand Name 与 Core Keywords 共同确定测量实验的中心。更口语地说，前者告诉系统“测谁”，后者告诉系统“围绕这个品牌的哪块产品、业务或服务测”。其余输入负责补充业务知识、受众结构、地区语境、竞争参照与采样方式。
Core Keywords 定义测量范围，但系统并不把业务简化为关键词出现监测。它会把该范围转化为客群、决策场景和自然问题，再观察目标品牌是否进入模型回答。因此，JefurryAxis 的测量单位本质上是“某个品牌在某块业务空间内，面对特定客群和特定决策场景时的可见度”。
3. 对象类型
系统中的对象可分为三类。
| 类别 | 代表对象 | 业务角色 |
|---|---|---|
| 业务输入变量 | Brand Name、Business Description、Core Keywords、Regions、Competitors、Persona Traits、Files、Run Settings、Tested LLMs | 定义品牌、市场、受众、竞品、采样方式和测量范围 |
| 生成样本 | Tags、Personas、Scenarios、Queries | 把业务输入转化为可测试的用户问题集合 |
| 报告输出 | 综合可见度 VS、提及覆盖 MR、出现频率 OF、平均排名 AR、品牌内容占比 BCR、Source/Citation、Persona/Scenario/Model 明细 | 衡量品牌在模型回答中的表现和原因 |
业务输入变量是因，报告输出是果。生成样本位于中间，承担把业务事实转化为可复测问题空间的作用。
生成样本也是可审查、可治理的中间测量资产，而不是不可见的黑箱结果。其业务价值在于暴露系统如何把输入理解为客群、场景和问题；当中间样本发生变化时，下游回答与报告的实验条件也随之变化。因此，对生成样本的治理本质上是在治理测量问题，而不是直接修改报告分数。
4. 变量重要性层级
不同变量的重要性并不相同。越靠近上游，越容易影响整份报告的方向。
| 层级 | 变量 | 重要性 |
|---|---|---|
| 基础锚点，最高 | Brand Name、Core Keywords | 定义被测实体与被测业务空间，贯穿样本生成和结果归属 |
| 业务知识层，高 | Business Description、Additional Business Files | 消除业务歧义，补充产品、服务、能力与市场知识 |
| 受众建模层，高 | Audience Geographic Regions、Audience Preferred Regions、Custom Persona Traits、Sample Personas Files | 定义用户是谁、身处何处、关心何处以及如何决策 |
| 比较参照层 | Competitors | 定义报告中的竞争对象与相对解释空间 |
| 采样控制层 | Tags Count、Personas Count、Persona Similarity、Scenarios Count、Queries Count、Regenerate Degree | 控制覆盖面、差异性、稳定性、成本和噪声 |
| 模型环境层 | Tested LLMs、由客群语境形成的 Query 语言 | 决定测量覆盖的模型生态和语言环境 |
| 输出层 | VS、MR、OF、AR、BCR、Source/Citation | 反映结果，不直接定义业务事实 |
Brand Name 与 Core Keywords 的错误会改变“测量谁”和“测量什么”，属于方向性偏差。这里的测量中心不是 Brand Name 单独决定的，而是由 Brand Name 与 Core Keywords 共同确定，并由 Business Description 帮助解释。业务知识层与受众建模层的偏差会改变系统对目标业务和目标客群的理解。采样控制层主要改变覆盖、稳定性与成本，无法纠正基础锚点的方向错误。
5. Brand Name
Brand Name 回答的是“这次测谁”。它是系统识别目标品牌的入口，也是报告归因的基础。
这里最重要的是指向稳定。现实里，一个品牌可能同时有中文名、英文名、缩写、公司法定名称、母公司名、子品牌名或产品名；用户、媒体、官网和模型也可能使用不同叫法。Brand Name 不一定要追求最正式的公司名称，而要尽量指向市场中真正被用户和模型识别的那个品牌实体。
Brand Name 的独特重要性在于，它同时连接“系统理解的是哪个实体”和“报告统计的是哪个实体”。名称指向发生偏差时，上游业务理解和下游指标归属可能同时发生偏差。
报告分析中的品牌识别不是简单的主名称字符串计数，而是对目标品牌实体及其可能别名进行语义识别。系统当前以 Brand Name 作为主要实体锚点，并没有独立的 aliases 业务输入字段。因此，名称歧义、别名过多、母子品牌关系复杂或产品名强于品牌名时，仍可能出现漏识别或误归因。Brand Name 的稳定性既影响业务理解，也影响 MR、OF 和 AR 的统计可信度。
6. Business Description
Business Description 回答的是“这块业务具体是什么”。它不是固定模板，也不是要求把所有背景都塞进去；它的作用是让系统读懂 Brand Name 与 Core Keywords 背后的真实业务含义。
好的 Business Description 应该帮助系统理解品牌在当前业务空间里做什么、主要服务谁、解决什么问题，以及这块业务和其他业务线有什么差别。它不需要按固定维度逐项展开，也不需要写成完整公司介绍。只要能让系统清楚理解当前被测业务，就可以保持简洁。
过短或只有口号的描述会让系统缺少判断依据；信息很多但主题混乱的描述，会让系统把多个业务空间混在一起。Business Description 的价值主要体现在澄清和补充，而不是替代 Brand Name 与 Core Keywords 重新定义测量中心。
当品牌同时存在面向企业或机构客户的业务，也存在面向个人用户或消费者的业务，或存在面向不同客户的多条产品线时，Business Description 需要明确指出这些业务线的差异。这里的重点不是给系统增加复杂负担，而是避免系统把不同业务混成一个泛化空间。若两类业务在服务对象、使用场景、决策标准、交付方式和竞品环境上明显不同，通常应视为不同业务空间，而不是放在同一次 Detection 里混合测量。
7. Core Keywords
Core Keywords 回答的是“围绕这个品牌的哪块业务测”。它们不是普通 SEO 关键词，也不是品牌的全部业务介绍。
可以把 Brand Name 和 Core Keywords 的关系理解得更直白一点：Brand Name 定人，Core Keywords 定范围。Brand Name 让系统知道目标品牌是谁；Core Keywords 让系统知道这次围绕该品牌的哪类产品、业务或服务来生成 Tags、Personas、Scenarios 和 Queries。
Core Keywords 需要共同描述一块连贯业务空间：
| 维度 | 含义 |
|---|---|
| 业务范围 | 描述被测产品、业务或服务属于什么空间 |
| 对象聚焦 | 明确本次测量关注品牌的哪一部分能力 |
| 语义一致性 | 多个关键词共同构成连贯、可解释的业务空间 |
| 问题相关性 | 能够自然连接真实客群的决策问题 |
Core Keywords 不是普通的补充信息，而是贯穿生成链路的范围定义。过泛会扩大问题空间，使下游样本远离品牌真实能力；过窄会让报告只覆盖局部业务；彼此不连贯会把多个不同业务空间混合进同一次测量，使结果难以解释。
实际判断时，要避免两个极端：
- 不能完全不区分业务线。若同一品牌下既有服务企业、机构或团队的业务，也有服务个人用户或消费者的业务，并且它们对应不同产品能力、应用场景、决策标准、交付方式或竞品环境，应考虑拆成不同业务空间或不同 Detection Run。判断重点不是给业务贴标签，而是看它们是否真的是两类不同服务对象下的核心业务；如果是，通常应拆开测量。
- 不能拆得过细。若几个关键词本质上只是同一块业务的不同说法，不需要拆成多个空间。
Core Keywords 通常更适合用少量清楚短语共同定义业务空间。一般以 3–4 个彼此相关的词或短语为参考；重点不是填满数量，而是让系统知道本次测量到底围绕品牌的哪块业务。
对同一个公开品牌，业务空间也不宜拆得过多。多数情况下，先控制在 2–3 个清晰业务空间内更容易保持报告可解释；只有当更多业务线确实拥有不同目标客户、不同决策链和不同竞争环境时，才继续拆分。该拆分的业务不拆，会让 Detection 过于泛化，测出来的不是用户真正关心的业务；不该拆的业务拆太细，则会让每个报告失去足够业务宽度。
8. Audience Geographic Regions
Audience Geographic Regions 表示目标受众实际所在地。它描述的是用户或买方的位置，不是品牌总部位置。
业务意义：
- 影响 persona 的地域身份。
- 影响 query 的语言和地区语境。
- 影响模型生态选择。
- 影响价格、法规、可获得性和文化背景。
这个字段的本质问题是“谁在发问、谁在决策、谁在购买”。当品牌所在地、用户所在地和用户关注地区不同，Audience Geographic Regions 仍然围绕用户所在地，而不是品牌所在地。
9. Audience Preferred Regions
Audience Preferred Regions 表示目标受众关心、比较、消费、申请、投资或进入的地区。
业务意义：
- 补充跨区域业务语境。
- 影响 scenarios 中的地区目标。
- 影响 queries 是否自然体现跨境比较、申请、采购或选择。
- 帮助区分“用户在哪里”和“用户关心哪里”。
Geographic 和 Preferred 的关系可以理解为：
| 问题 | 对应字段 |
|---|---|
| 用户身处哪里 | Audience Geographic Regions |
| 用户关心哪里 | Audience Preferred Regions |
纯本地业务中，Preferred 可能与 Geographic 一致，也可能没有明显独立意义。关键不在于字段是否重复，而在于地区是否实质影响用户比较和模型回答。如果地区会影响供应、法规、价格、语言或服务范围，它就是重要语境；如果地区对问题没有实际影响，它就只是背景。
10. Competitors
Competitors 定义报告中的竞争语境。它们不是为了美化或压低目标品牌而存在，而是为了让系统识别真实比较对象。
业务意义：
- 支持竞品雷达和相对可见度解读。
- 帮助判断目标品牌是否被竞品压制。
- 提供相对可见度参照。
- 揭示模型更熟悉或更信任哪些竞品。
竞品集合决定目标品牌与哪些实体共享同一套指标解析与比较空间。过弱竞品会让报告显得乐观但缺乏现实意义；过宽竞品会把目标品牌放进不公平或不相关的竞争环境；泛类别无法形成品牌级实体比较。
Competitors 主要属于报告分析与相对解释层，而不是 Tags、Personas、Scenarios 和 Queries 的核心生成锚点。它更像同一测量实验中的竞争参照系。
这种分离使生成问题保持相对中性，而报告分析仍会针对已定义的竞品逐一识别和比较。竞争集合影响的是“与谁比较”，不直接规定用户问题必须提到谁。
11. Custom Persona Traits
Custom Persona Traits 用于表达关键目标人群特征。它们补充系统对真实用户的理解，使 Personas 更像真实决策者，而不是抽象模板。
业务意义：
- 强化 persona 的真实性。
- 减少生成泛化人群。
- 保护品牌已经明确掌握的战略客群，使其不被通用推断稀释。
- 引导 scenarios 更贴近真实决策。
- 使 queries 更符合目标受众的信息需求。
Persona trait 可以承载多类常见客群信息：
| 维度 | 业务意义 |
|---|---|
| 地区 | 决定语言、文化和可获得性语境 |
| 身份 | 决定用户角色、预算和责任 |
| 动机 | 决定为什么会搜索或购买 |
| 决策标准 | 决定比较品牌时看什么 |
| 信息行为 | 决定会如何向 LLM 提问 |
| 风险或限制 | 决定真实顾虑和阻碍 |
Trait 的业务价值来自客群事实的真实性和具体性，而不是维度数量。失真的客群信息会把下游场景和问题带向错误方向。
Tags 与 Custom Persona Traits 都描述客群，但业务角色不同。Tags 是系统形成的通用客群维度空间，用于发现和组合现有或潜在人群；Custom Persona Traits 是品牌已经掌握的特定客群事实，用于保护关键人群信号。前者偏向系统探索，后者偏向业务先验，二者共同影响 Persona。
12. Additional Business Files
Additional Business Files 补充品牌、产品、市场和差异化事实。它们用于增强系统对业务上下文的理解。
业务意义：
- 补强短描述无法覆盖的产品细节。
- 提供功能、定价、案例、FAQ、定位和行业背景。
- 增强上游业务知识与标签空间。
- 帮助区分品牌真实能力和营销口号。
这类文件主要回答“业务是什么、能力边界是什么、市场事实是什么”。信息密度、相关性和时效性决定其业务价值；低相关、重复、过时或过度宣传的资料会稀释业务信号。
13. Sample Personas Files
Sample Personas Files 补充真实用户研究和人群材料。它们影响 Personas 和 Scenarios 的真实度。
业务意义：
- 让 persona 更接近真实客户。
- 提供购买动机、比较标准和阻碍因素。
- 补充信息来源、搜索行为和地区偏好。
- 减少模板化画像。
这类文件主要回答“真实客户是谁、为何决策、如何比较、存在哪些阻碍”。它们与 Additional Business Files 的业务作用不同：前者补充客群事实，后者补充业务事实。
14. 语言环境
语言环境不是把一种语言机械翻译成另一种语言。不同市场中的品牌描述、关键词、人群特征和 query 语言，其真实性取决于是否反映目标受众自然表达问题的方式。
业务意义：
- 影响目标市场模型的回答质量。
- 影响用户问题是否真实。
- 影响品牌与竞品是否进入正确语境。
- 影响跨境业务中的地区和语言判断。
跨境业务不等于全英文，也不等于只用单一语言。专有名词、问题结构、动机表达和比较方式共同构成目标受众的真实语言习惯。语言环境应服从目标受众和目标市场，而不是服从品牌内部使用的表达。
15. Tags 与 Tags Count
Tag 是描述品牌现有或潜在客群的结构化分群维度。它把分散的客户理解转化为可组合的人群特征空间，主要覆盖人口、地域、心理和行为等客户细分维度。Tag 不是品牌标签，也不是产品功能标签。
业务意义：
- Tags 是 personas、scenarios 和 queries 的上游客群语义空间。
- Tags 决定下游样本会围绕哪些客户差异展开。
- Tags 过少会造成覆盖不足。
- Tags 过多会增加边缘、重复或偏题信号。
Tags Count 控制标签池规模。它不是“越大越好”的参数，而是在覆盖范围和噪声之间取平衡。
判断 Tags Count 时，核心不是行业名称，而是用户决策链有多长。决策链短、压力小、角色少、比较标准简单的业务，通常不需要大量 tags；决策链长、风险大、参与角色多或比较标准复杂的业务，才需要更多 tags 承载差异。服务对象、使用者、决策者和评价标准的差异不是普通标签差异，而是强烈提示业务可能存在不同决策链；当这些差异已经指向不同产品线或不同应用场景时，应优先拆业务空间，再分别判断各自需要多少 tags。
| 情况 | 业务影响 |
|---|---|
| Tags Count 过低 | 业务维度不足，persona 和 scenario 单薄 |
| Tags Count 适中 | 覆盖核心业务维度，同时保持聚焦 |
| Tags Count 过高 | 下游承接过多分散信号，persona 变粗或偏题 |
Tags Count 与 Personas Count 存在配比关系。少量 personas 承接过多 tags 时，画像容易变得分散。复杂业务可以承受更高 Tags Count，但前提是 tags 与业务高度相关，且下游 persona 数量足以承接。
16. Personas 与 Personas Count
Persona 是基于标签空间构建的虚构客群原型，代表品牌所在品类或行业中的现有或潜在客户。它不是单个真实用户，也不是静态营销画像模板，而是连接客群理解、决策场景和真实提问的分析对象。
业务意义：
- 把品牌目标市场拆成不同用户类型。
- 让系统从多个受众角度测试品牌可见度。
- 支撑后续 scenario 和 query 的差异化。
- 帮助报告定位品牌在哪些人群中更强或更弱。
Personas Count 控制模拟人群数量。数量越少，报告越聚焦但覆盖更薄；数量越多，覆盖更广但成本上升，也更依赖上游 tags 和 persona traits 的质量。
Personas Count 同样应看决策链。若购买者、使用者、影响者、审批者或比较者明显不同，需要更多 persona 才能覆盖真实角色；若业务本身决策很直接，强行制造大量 persona 反而会让画像牵强。
| Personas Count 状态 | 业务影响 |
|---|---|
| 偏低 | 更适合粗略探索，细分人群覆盖不足 |
| 适中 | 覆盖主要人群，同时保持报告可解释性 |
| 偏高 | 适合复杂市场，但更容易出现重复或牵强画像 |
Personas 的质量不只由数量决定。真实业务中，更关键的是 persona 是否代表真实购买者、决策者、搜索者或影响者。
17. Persona Similarity
Persona Similarity 控制 personas 之间的差异程度。它不是质量高低，而是人群之间相似度的业务设定。
| 值 | 业务含义 | 典型影响 |
|---|---|---|
| Low | Personas 之间差异更大，去重更严格 | 更适合探索多类用户，画像更分散 |
| Medium | 差异和稳定性平衡 | 更适合常规分析 |
| High | 允许 personas 更接近 | 更适合窄客群或单一 ICP |
Low 与少量 personas 组合时，会形成少数但差异较大的人群视角。这有利于快速发现不同用户类型，但不利于深入刻画某个窄客群。High 与高 Regenerate Degree 组合时，容易形成相近画像，适合非常集中的市场，但会降低多样性。
18. Scenarios 与 Scenarios Count
Scenario 是触发 persona 向 AI 发起搜索或咨询的特定情境与痛点。它连接“用户是谁”和“用户为什么在此刻发问”，通常同时受到 persona 自身因素与外部环境因素影响。
业务意义：
- 把 persona 转化为真实决策情境。
- 体现购买、比较、评估、风险、地区、预算和使用场景。
- 帮助报告定位品牌在哪些决策背景下表现弱。
- 决定 query 是否有真实上下文。
Scenarios Count 控制每个 persona 的场景数量。场景越多，越容易暴露不同决策路径下的弱点；场景过少时，报告更容易只反映单一问题背景。
场景数量应随决策过程变化。决策链越长，用户通常会经历更多信息收集、比较、风险判断、预算权衡或内部说服场景；决策链越短，过多 scenario 可能只是重复描述同一类需求。
Scenarios 与 Queries 的区别在于：Scenario 是决策背景，Query 是在该背景下提出的自然语言问题。Scenario 扩展的是“问题发生在哪些情境”，Query 扩展的是“同一情境下有哪些问法”。
19. Queries 与 Queries Count
Query 是真正提交给 Tested LLMs 的自然语言问题。它是系统测量品牌 AI 可见度的直接触发器。
业务意义：
- 决定模型回答的语境。
- 决定品牌是否有合理出现机会。
- 决定报告是否接近真实用户搜索行为。
- 决定结果是否可复测、可解释。
Queries Count 控制每个 scenario 下的问题数量。同一场景下多个 query 可以降低单个问法带来的偶然性，但也会增加回答规模和成本。
当前业务中的 Query 主要承载品类或行业中的推荐、排名、比较和选择入口。它并非围绕目标品牌发起直接询问，而是在相对中性的用户问题中观察品牌是否自然进入模型回答。
Query 质量主要由上游决定：Brand Name 与 Core Keywords 确定测量对象和业务范围，Business Description 补充业务含义，Regions 决定地区语境，Personas 决定用户身份，Scenarios 决定决策背景。Query 本身是整条上游链路最终转化出的测量入口。
诱导型 query 会改变测量性质。它会让模型围绕目标品牌回答，从而提高部分指标，但这种结果更像品牌问答测试，而不是真实 AI 搜索可见度测试。
中性问题也带来一种重要的测量结果：当新品牌、小众品牌或品类认知尚弱的品牌在相关问题中长期不出现时，可能形成结构性低可见度。这并不自动代表测量失败，而是说明品牌尚未进入该业务空间中的自然推荐集合。Core Keywords 决定品牌获得可见机会的合法业务范围；范围过宽会制造无关的零可见度，范围足够聚焦仍持续为零，则更接近真实的品牌认知缺口。
20. Regenerate Degree
Regenerate Degree 表示 Persona 去重后数量不足时，系统补齐 Personas 的积极程度。
业务意义：
- 控制补齐数量与质量之间的平衡。
- 影响 Persona 补齐过程对“足量覆盖”的追求强度。
- 影响噪声和牵强样本出现概率。
| 值 | 业务含义 | 典型影响 |
|---|---|---|
| min | 保守补齐 | 质量更稳，数量不足时不强行扩展 |
| medium | 平衡补齐 | 兼顾覆盖和质量 |
| full | 激进补齐 | 更追求补足数量，但噪声风险更高 |
Regenerate Degree 与 Persona Similarity 存在张力。Similarity 控制差异方向，Regenerate Degree 控制补齐强度。当差异要求很高、补齐强度也很高时，系统更容易在“数量”和“质量”之间拉扯。
21. Tested LLMs 与模型生态
Tested LLMs 定义报告测量的模型生态。不同模型代表不同语言环境、训练内容、用户市场和回答风格。
业务意义：
- 决定报告覆盖哪些 AI 搜索渠道。
- 影响品牌和竞品出现概率。
- 影响中文、英文和跨境语境的真实性。
- 帮助比较品牌在不同模型生态中的强弱。
Query 语言与 Tested LLMs 强相关。Query 语言不是与 Core Keywords 同级的独立业务锚点，而是由 Persona、地区与场景语境共同形成的测量特征。中文受众、中文问题和中文生态模型构成一个测量语境；英文受众、英文问题和全球模型构成另一个测量语境。语言错配会让报告测到错误市场。
跨境业务的复杂性在于，用户可能身处一个地区，关注另一个地区，并用第三种语言搜索。此时语言、Geographic、Preferred 和 Tested LLMs 共同决定测量语境。
22. Run Settings 与回答规模
Run Settings 是测量采样结构的集合，主要承载 Tags、Personas、Scenarios、Queries 的数量，Persona Similarity、Regenerate Degree 和 Tested LLMs。它不定义品牌业务事实，而是决定系统以多大覆盖面、多少差异性和哪些模型渠道观察同一业务空间。
回答规模近似等于：
Personas 数 × Scenarios 数 × Queries 数 × Tested LLMs 数回答规模的业务意义：
- 决定报告覆盖面。
- 决定成本和耗时。
- 决定单次波动对结果的影响。
- 决定结果是否适合轻量探索、标准 benchmark 或深度研究。
| 回答规模 | 业务含义 |
|---|---|
| 较小 | 适合方向验证，稳定性较低 |
| 适中 | 适合常规 benchmark，覆盖和成本平衡 |
| 较大 | 适合深度研究，但成本、耗时和解释复杂度上升 |
回答规模扩大并不等于报告更好。它只有在上游输入准确、样本质量可靠、任务确实需要更高覆盖时才带来价值。
Run Settings 的数量判断应从业务空间和决策链出发，而不是从“参数越大越全面”出发。简单业务保持聚焦更重要；复杂业务可以提高覆盖，但前提是这些新增样本对应真实存在的人群、角色、场景或问法。若品牌同时存在服务对象、使用场景或决策标准明显不同的核心业务线，优先判断是否应拆成不同 Detection Run，而不是在同一个 Run 里用更大的 Count 强行覆盖所有业务。
回答规模是成本和耗时的主要代理变量，但不是完整成本公式。实际资源消耗还受到模型类型、回答长度、联网检索、失败重试和报告分析调用影响。因此，相同回答数量可能具有不同的耗时与资源成本。
23. 报告指标总览
JefurryAxis 的报告指标不是单一分数体系，而是一组互相补充的观察维度。
指标计算的原子单位是“一个 Query 向一个 Tested LLM 发起一次测量后得到的一条回答”。同一个 Query 被发送给多个 Tested LLMs 时，会形成多条彼此独立的回答；每条回答分别进行品牌、竞品、排名和内容分析，再向上聚合为模型、Persona、Scenario 和整体报告指标。
| 指标 | 全称 | 核心口径 | 单位与范围 |
|---|---|---|---|
| VS | Visibility Score | 综合多个可见度信号形成的总体分数 | 0–100，越高通常越好 |
| MR | Mention Rate | 提及目标品牌的回答数占全部回答数的比例 | 0–100% |
| OF | Occurrence Frequency | 经回答长度校正后，目标品牌在全部回答中的平均出现频率 | >= 0，无固定上限 |
| AR | Average Rank | 目标品牌在有效排序回答中的标准化相对位置均值 | > 0 且 <= 10，越小越好；无有效排名时为空 |
| BCR | Brand Content Ratio | 提及品牌的回答中，含品牌句子占全文句子的平均比例 | 0–1 |
| Source/Citation | 来源与引用 | 回答中能够观察到的外部来源证据 | 非单一数值指标 |
这些指标主要用于相对比较：同一报告内目标品牌与竞品对比、同一 Brand 在相同配置下的历史趋势、不同 persona/scenario/model 之间的差异。它们不适合脱离任务和配置被解释为固定行业阈值。
24. VS: Visibility Score
VS 是范围为 0–100 的综合可见度分数，反映品牌在多个维度上的整体表现。
业务意义：
- 提供报告总体判断入口。
- 汇总提及、频率、排名和内容占比等信号。
- 适合用于趋势观察和竞品对比。
- 不能单独解释原因。
VS 表达的是综合可见度质量，不是对单一曝光数量的追求。它综合 MR、OF、AR 等信号，但单个总分不会说明具体原因。VS 高通常说明品牌在相关回答中表现较好。VS 低可能来自多种原因：品牌未被提及、被提及但排名靠后、竞品更强、某些 persona 或 scenario 中明显弱势，或外部内容基础不足。
25. MR: Mention Rate
MR 表示品牌在多少比例的回答中被提及，范围为 0–100%。
MR = 提及目标品牌的回答数 ÷ 全部回答数业务意义：
- 衡量品牌是否进入模型推荐或比较范围。
- 反映品牌在问题空间中的基础可见度。
- 是判断“模型是否想到这个品牌”的关键指标。
MR 低通常说明品牌没有进入回答候选范围。常见业务原因包括品牌名称不稳定、业务描述不清、关键词偏离强势品类、query 语境不匹配、公开内容不足，或竞品在相关语境中更常被模型学习和引用。
26. OF: Occurrence Frequency
OF 表示目标品牌在全部回答中的长度校正平均出现频率，范围从 0 开始，没有固定上限。它并不是只统计已经提及品牌的回答。
每条回答中的品牌提及次数会结合回答长度进行校正，使较长回答不会仅因篇幅更长而天然获得更高 OF；校正后的结果再对全部回答取平均。因此，未提及品牌的回答会以零值进入 OF。
业务意义：
- 衡量品牌在整体回答空间中的重复出现强度。
- 区分“只是被列出”和“被充分解释”。
- 与 MR 一起判断品牌是否只是被偶然列出，还是在回答中具有更稳定的存在感。
MR 高但 OF 低时，品牌可能只是被频繁列入列表，但缺少展开。OF 高但 MR 低时，品牌可能只在少数回答中被集中讨论，整体覆盖仍然有限。OF 异常高时，需要结合 query 语境判断回答是否被过度诱导。
27. AR: Average Rank
AR 表示品牌在有效排序回答中的标准化相对位置均值。它是方向特殊的指标：数值越小，代表相对排名越靠前。
单条回答中的排名由分析模型根据回答语义识别为“品牌名次与榜单品牌总数”。只有品牌确实上榜、名次与榜单总数都有效的回答才进入 AR；未上榜、没有明确排序或无法识别有效榜单的回答不进入 AR 平均值。有效排名会根据榜单长度转换为可比较的相对位置，因此不同长度的榜单可以共同聚合。
业务意义：
- 衡量品牌是否被优先推荐。
- 区分“被提到”和“被排在前面”。
- 暴露品牌在比较场景中的竞争位置。
MR 高但 AR 差，说明品牌经常出现，但并不一定是优先推荐对象。这类状态通常意味着品牌有基础认知度，但在模型心中的比较优势不够强，或竞品拥有更清晰的优势证据。
AR 只描述“上榜后的相对位置”，不描述“上榜概率”。因此，品牌很少上榜但上榜时靠前，可能同时表现为低 MR 与较好的 AR。
28. BCR: Brand Content Ratio
BCR 在单条回答中表示含目标品牌名称或别名的句子数，占该回答全部句子数的比例。句子边界不仅包含句号，也包括问号、感叹号、段落和结构化列表等自然语句边界；同一句中多次出现品牌仍只计为一个含品牌句子。
报告层 BCR 是所有正值单条 BCR 的平均值。未提及品牌、单条 BCR 为零的回答不进入该平均。因此，BCR 主要描述“品牌出现后获得了多少内容空间”，而不是品牌在全部回答中的覆盖率。
业务意义：
- 观察品牌被提到以后，是否获得足够解释空间。
- 区分“只是被列名”和“被实际展开说明”。
- 与 MR、OF 和 AR 一起判断品牌可见度质量。
- 帮助识别 query 是否过度诱导品牌。
BCR 不是单纯越高越好。过低说明品牌内容不足；过高可能说明问题过度诱导品牌，或回答结构不自然。BCR 的业务意义是“品牌内容占比是否合理”，而不是“品牌内容越多越好”。
28.1 指标组合诊断矩阵
| 指标组合 | 业务诊断 |
|---|---|
| MR 高、AR 好 | 品牌经常进入回答，并且通常处于较优推荐位置 |
| MR 高、AR 差 | 品牌具有基础认知，但相对推荐优势不足 |
| MR 低、AR 好 | 品牌很少进入回答，但进入后具有较强排序位置 |
| MR 低、AR 差或为空 | 品牌既缺少基础可见度，也缺少稳定排序信号 |
| MR 高、OF 低 | 品牌经常被列出，但重复强调和展开程度有限 |
| MR 低、OF 高 | 品牌只在少量回答中出现，但出现时被集中讨论 |
| OF 高、BCR 低 | 品牌名称重复出现较多，但内容解释可能分散或简短 |
| OF 低、BCR 高 | 品牌出现次数不多，但出现后获得较集中说明 |
| 目标品牌 MR 低、BCR 低 | 品牌既不容易进入回答，出现后也没有获得足够内容空间 |
| 指标整体稳定、局部 Persona 或 Scenario 明显偏弱 | 问题更可能集中在特定客群或决策情境，而不是全局品牌认知 |
29. Source/Citation
Source/Citation 表示模型回答中可观察到的信息来源、引用来源或内容依据。
业务意义：
- 提供模型回答所关联外部来源的可观察证据。
- 揭示竞品的外部内容优势。
- 帮助定位目标品牌的公开内容缺口。
- 支持外部内容优化方向判断。
Source/Citation 维度关注的是回答中能够被观察到的外部信息基础。它可以支持来源结构分析，但不等同于模型全部知识来源，也不能单独证明品牌被提及或未被提及的完整因果关系。官网结构化内容、FAQ、产品对比页、案例、第三方报道、榜单、评测和行业资料，都可能影响模型回答中的品牌可见度。
Source/Citation 的可观察覆盖取决于模型渠道、联网检索能力、搜索结果和回答是否返回来源信息。部分回答可能没有可用引用数据；“没有观察到来源”不等同于“品牌没有外部内容基础”。不同模型之间的来源数量也不适合在缺少覆盖条件说明时直接比较。
30. 强品牌但 VS 不达预期
强品牌低 VS 是 JefurryAxis 最重要的业务场景之一。它不一定代表系统错误，也不一定代表品牌真实变弱，而是需要区分两类问题。
第一类是测量配置偏差。品牌名称、别名、关键词、地区、语言、tested models、personas、scenarios 或 queries 与真实业务不一致时，报告可能低估品牌表现。
第二类是真实 AI 可见度差距。品牌传统知名度、广告投放和线下影响力没有充分转化为 LLM 可见内容时，模型回答仍可能偏向公开内容更充分、第三方引用更多、结构化资料更强的竞品。
这个场景的业务价值在于，它能帮助区分“品牌本身强”与“品牌在 AI 搜索中可见”。两者相关，但不是同一件事。
31. 长期趋势与测量不确定性
长期趋势的价值来自可复测性。同一 Brand 在相同输入、相同 query 空间和相同 Tested LLMs 组合下，才适合进行趋势比较。
业务意义：
- 观察品牌 AI 可见度是否改善。
- 判断外部内容优化是否产生效果。
- 识别竞品是否在某些场景中增强。
- 发现模型生态变化带来的影响。
LLM 回答具有非确定性。即使输入与模型名称保持一致，回答措辞、品牌选择、排名结构和引用来源仍可能变化；联网搜索结果、可用来源、模型服务更新与分析模型判断也会带来波动。因此，单次运行是对当时 AI 可见度环境的一次抽样，而不是绝对不变的事实。
模型名称一致也不必然代表底层模型完全一致。外部模型服务可能更新权重、检索策略、安全规则或回答风格，使长期基线产生漂移。趋势变化既可能来自品牌真实变化，也可能来自模型生态变化。
配置变化、query 变化、模型组合变化、目标市场变化和模型服务变化，都会改变实验条件。趋势分析的核心是保持尽可能一致的测量基线，并结合多次结果、变化幅度、多个指标以及 Persona、Scenario、Model 和 Source 明细共同判断。小幅单次变化更接近噪声信号，持续且跨维度一致的变化更具有业务解释价值。
32. 总结
JefurryAxis 的核心业务逻辑可以浓缩为三句话：
- Brand Name 与 Core Keywords 定义测量谁、围绕什么业务空间测量。
- 客群与场景链路把业务空间转化为可测试的用户问题空间。
- 报告指标反映品牌在 LLM 回答中的可见度、竞争位置和内容基础。
每个变量的重要性取决于它处于链路中的位置和业务角色。Brand Name 与 Core Keywords 是基础锚点；业务知识和受众变量决定系统如何理解这两个锚点；采样参数决定测量覆盖；报告指标用于解释发生了什么。系统的核心价值不是制造更高分，而是解释分数为什么形成，以及品牌在哪些人群、场景、模型和内容来源中存在真实机会或缺口。
1. System Positioning
JefurryAxis analyzes how visible a brand is in large language model answers, where it appears in recommendations, how competitors pressure it, and what external content foundation supports the answers. These diagnostics can then inform content, channel, and brand cognition optimization.
Traditional brand analysis often focuses on website traffic, ads, search ranking, media volume, and sales conversion. JefurryAxis focuses on a different question: when the target audience asks a real decision question to an LLM, does the model include the brand, recommend it early, give it enough explanation, mention competitors instead, and rely on particular sources?
JefurryAxis therefore does not measure whether a brand is objectively excellent. It measures visibility in AI search and LLM recommendation environments. A brand can be strong in the traditional market but weak in LLM answers; this often means its public content, third-party references, structured materials, or target-market context have not yet converted into AI visibility.
In business terms, the system forms a loop of measurement, diagnosis, optimization, and continuous monitoring. Detection builds the measurement space and identifies weak audiences, scenarios, models, and sources. Later optimization and monitoring convert diagnosis into brand management signals. The report is not the business endpoint; it is the diagnostic layer between current brand cognition and future optimization.
2. Core Business Chain
Separate these core concepts first:
| Concept | Meaning |
|---|---|
| Public Brand | A real brand, company, institution, or product brand in the market |
| Business Space | A product, business, or service scope under a public brand that is measured separately |
| Brand Workspace | The system record that carries one business space; it is expressed through Brand Name, Core Keywords, and business description |
| Detection Run | One measurement run inside a defined Brand Workspace |
Business space is not a random keyword set and not the whole company. The core question is: which part of the brand is being measured this time? To judge business space, look at whether product or service scope, target customers, user decision chain, and competitor environment are consistent. If these factors clearly differ, there may be different business spaces.
The Brand Workspace is the system container. Brand Name tells the system which brand is being measured. Core Keywords tell it which part of the brand's business to measure. Business Description explains what that business actually is. Together, they define what the current Brand Workspace represents.
A Detection Run is a complete measurement inside that defined Brand Workspace. It uses the current Brand Name, Core Keywords, business knowledge, audience context, generated samples, Run Settings, and Tested LLMs to produce LLM answers and aggregate them into a report. A Detection Run should not be used to redefine business lines on the fly; the business space should be defined at the Brand Workspace level first.
The same public brand can have different Brand Workspaces for different business spaces. The same Brand Workspace can also produce multiple Detection Runs over time. Trend comparison is meaningful only when the business space and major measurement conditions remain consistent.
The JefurryAxis analysis chain can be summarized as:
Brand business information
-> audience tag space
-> audience personas
-> decision scenarios
-> natural language queries
-> multi-model answers
-> brand and competitor recognition
-> visibility, rank, content share, source, and competitive-pressure analysisThe key feature of this chain is that upstream inputs shape downstream outputs. Brand information shapes tag space; tags affect personas; personas affect scenarios; scenarios affect queries; queries define the context of LLM answers; answers ultimately determine report metrics.
The report is therefore not an isolated score. It is a measurement experiment formed by input variables, generated samples, and LLM answers.
The chain has two cooperating business lines:
| Line | Core Question | Main Objects |
|---|---|---|
| Measurement Object and Business Space | Who is measured, and around what product, business, or service scope? | Brand Name, Core Keywords |
| User View and Decision Space | From whose perspective, in what context, and through what questions? | Tags, Personas, Scenarios, Queries |
Brand Name and Core Keywords jointly define the center of the measurement experiment. In plain language, Brand Name tells the system who is being measured, while Core Keywords tell it which part of the brand's product, business, or service is being measured. Other inputs add business knowledge, audience structure, regional context, competitive references, and sampling controls.
Core Keywords define the measurement scope, but the system does not reduce the business to keyword monitoring. It turns the scope into audiences, decision scenarios, and natural questions, then observes whether the target brand enters model answers. The real measurement unit is visibility of a brand inside one business space, for specific audiences and decision scenarios.
3. Object Types
Objects in the system fall into three categories.
| Category | Representative Objects | Business Role |
|---|---|---|
| Business Input Variables | Brand Name, Business Description, Core Keywords, Regions, Competitors, Persona Traits, Files, Run Settings, Tested LLMs | Define brand, market, audience, competitors, sampling, and measurement scope |
| Generated Samples | Tags, Personas, Scenarios, Queries | Convert business input into testable user-question space |
| Report Outputs | VS, MR, OF, AR, BCR, Source/Citation, Persona/Scenario/Model details | Measure brand performance in model answers and explain why |
Business inputs are causes, and report outputs are effects. Generated samples sit in the middle and convert business facts into a repeatable question space.
Generated samples are also reviewable and governable measurement assets rather than invisible black-box output. Their value is that they reveal how the system understands inputs as audiences, scenarios, and questions. When middle samples change, downstream answers and report conditions change. Governing generated samples is therefore governing the measurement question, not directly editing report scores.
4. Variable Importance Hierarchy
Variables are not equally important. The closer a variable is to the upstream foundation, the more it can redirect the whole report.
| Layer | Variables | Importance |
|---|---|---|
| Foundation anchors, highest | Brand Name, Core Keywords | Define the measured entity and business space, and affect both sample generation and result attribution |
| Business knowledge layer, high | Business Description, Additional Business Files | Remove ambiguity and add product, service, capability, and market knowledge |
| Audience modeling layer, high | Audience Geographic Regions, Audience Preferred Regions, Custom Persona Traits, Sample Personas Files | Define who users are, where they are, what they care about, and how they decide |
| Comparison reference layer | Competitors | Define competitive objects and relative interpretation space |
| Sampling control layer | Tags Count, Personas Count, Persona Similarity, Scenarios Count, Queries Count, Regenerate Degree | Control coverage, difference, stability, cost, and noise |
| Model environment layer | Tested LLMs and query language formed by audience context | Define model ecosystem and language environment |
| Output layer | VS, MR, OF, AR, BCR, Source/Citation | Reflect results; they do not directly define business facts |
Errors in Brand Name and Core Keywords change who and what is being measured. This is directional bias. The measurement center is not Brand Name alone; it is jointly defined by Brand Name and Core Keywords, with Business Description explaining them. Bias in business knowledge or audience modeling changes how the system understands the target business and audience. Sampling controls mainly affect coverage, stability, and cost; they cannot correct a wrong foundation.
5. Brand Name
Brand Name answers: who is being measured this time? It is the entry point for identifying the target brand and the foundation for report attribution.
The most important property is stable reference. In reality, a brand may have a Chinese name, English name, abbreviation, legal company name, parent company name, sub-brand name, or product name. Users, media, websites, and models may use different names. Brand Name does not always have to be the most formal legal name; it should point to the brand entity that users and models actually recognize in the market.
Brand Name is uniquely important because it connects the entity the system understands with the entity the report counts. If name reference drifts, upstream business understanding and downstream metric attribution may both drift.
Brand recognition in report analysis is not simple exact string counting of the main name. It semantically recognizes the target brand entity and possible aliases. The system currently uses Brand Name as the main entity anchor and does not have a separate aliases business input field. Therefore, name ambiguity, too many aliases, complex parent-child brand relationships, or product names stronger than brand names can still cause missed recognition or wrong attribution. Brand Name stability affects business understanding and the credibility of MR, OF, and AR.
6. Business Description
Business Description answers: what exactly is this business? It is not a fixed template and does not require every background detail. Its role is to help the system understand the real business meaning behind Brand Name and Core Keywords.
A good Business Description helps the system understand what the brand does in the current business space, whom it mainly serves, what problem it solves, and how this business differs from other lines. It does not need to follow fixed dimensions or become a full company profile. If it lets the system clearly understand the current measured business, it can stay concise.
A description that is too short or slogan-like leaves the system without enough basis for judgment. A long but unfocused description can cause multiple business spaces to blend together. The value of Business Description is clarification and supplementation, not replacing Brand Name and Core Keywords as the measurement center.
When a brand has both enterprise or institutional customer businesses and individual user or consumer businesses, or multiple product lines for different customers, Business Description should make those differences clear. The goal is not to add complexity, but to prevent different businesses from being mixed into one generic space. If two businesses clearly differ in served audience, use case, decision criteria, delivery model, and competitor environment, they should usually be treated as different business spaces rather than mixed into one Detection.
7. Core Keywords
Core Keywords answer: which part of this brand's business is being measured? They are not ordinary SEO keywords and not a complete business introduction.
Put simply, Brand Name defines the subject, while Core Keywords define the scope. Brand Name tells the system who the target brand is. Core Keywords tell it which type of product, business, or service should drive Tags, Personas, Scenarios, and Queries.
Core Keywords should jointly describe one coherent business space:
| Dimension | Meaning |
|---|---|
| Business scope | What space the measured product, business, or service belongs to |
| Object focus | Which part of the brand capability this measurement focuses on |
| Semantic consistency | Keywords together form a coherent and explainable business space |
| Question relevance | They naturally connect to real customer decision questions |
Core Keywords are not supplementary notes. They are the scope definition running through the generation chain. If too broad, they expand the question space and pull downstream samples away from the brand's real strengths. If too narrow, the report covers only a small part of the business. If incoherent, they mix multiple business spaces in one measurement and make results hard to interpret.
Avoid two extremes:
- Do not ignore business-line differences. If one brand serves enterprise, institutional, or team users in one line and individual users or consumers in another, and these lines involve different product capabilities, use cases, decision criteria, delivery models, or competitor environments, consider separate business spaces or Detection Runs. The point is not to label the business, but to decide whether these are genuinely different core businesses for different audiences. If yes, they should usually be measured separately.
- Do not split too finely. If several keywords are only different phrasings of the same business, they do not need separate spaces.
Core Keywords usually work best as a small set of clear phrases that jointly define the business space. Use 3-4 related words or phrases as a reference. The goal is not to fill a quota; it is to tell the system what part of the brand is being measured.
For one public brand, do not split business spaces too much. In most cases, start with 2-3 clear business spaces so reports remain interpretable. Split further only when more business lines truly have different target customers, decision chains, and competitive environments. Failing to split when needed makes Detection too generic; splitting too finely makes each report lose enough business breadth.
8. Audience Geographic Regions
Audience Geographic Regions describe where the target audience actually is. It describes the user or buyer location, not the brand headquarters.
Business meaning:
- Affects persona regional identity.
- Affects query language and regional context.
- Affects model ecosystem selection.
- Affects price, regulation, availability, and cultural background.
The essential question is: who is asking, deciding, or buying? When brand location, user location, and user target region differ, Audience Geographic Regions still follows the user location, not the brand location.
9. Audience Preferred Regions
Audience Preferred Regions describe the regions the target audience cares about, compares, consumes in, applies to, invests in, or wants to enter.
Business meaning:
- Adds cross-region business context.
- Affects regional targets in scenarios.
- Affects whether queries naturally express cross-border comparison, application, procurement, or selection.
- Separates where users are from where users care about.
Geographic and Preferred can be understood as:
| Question | Field |
|---|---|
| Where is the user located? | Audience Geographic Regions |
| Which region does the user care about? | Audience Preferred Regions |
For local businesses, Preferred may match Geographic or may have no independent meaning. The key is not whether the fields repeat, but whether region materially affects comparison and model answers. If region affects supply, regulation, price, language, or service scope, it is important context. If it does not affect the question, it is only background.
10. Competitors
Competitors define the competitive context in the report. They are not used to make the target brand look better or worse. They let the system identify real comparison objects.
Business meaning:
- Supports competitor radar and relative visibility interpretation.
- Helps judge whether the target brand is suppressed by competitors.
- Provides relative visibility reference.
- Reveals which competitors models know or trust more.
The competitor set determines which entities share the same analysis and comparison space with the target brand. Weak competitors make the report look optimistic but less realistic. Overly broad competitors put the brand into an unfair or irrelevant competitive environment. Generic categories cannot support brand-level entity comparison.
Competitors mainly belong to report analysis and relative interpretation, not the core generation anchor for Tags, Personas, Scenarios, and Queries. They are the competitive reference frame in the same measurement experiment.
This separation keeps generated questions relatively neutral while report analysis still recognizes and compares defined competitors one by one. The competitor set affects who the brand is compared with; it does not require user questions to mention them.
11. Custom Persona Traits
Custom Persona Traits express key target-audience characteristics. They supplement the system's understanding of real users, making Personas closer to real decision makers rather than abstract templates.
Business meaning:
- Improves persona realism.
- Reduces generic audience generation.
- Protects strategic audience signals already known by the brand.
- Guides scenarios toward real decisions.
- Makes queries fit target audience information needs.
Persona traits can carry several types of audience information:
| Dimension | Business Meaning |
|---|---|
| Region | Determines language, culture, and availability context |
| Identity | Determines role, budget, and responsibility |
| Motivation | Determines why the user searches or buys |
| Decision criteria | Determines what matters when comparing brands |
| Information behavior | Determines how the user asks an LLM |
| Risks or limits | Determines real concerns and barriers |
The value of traits comes from truthful and specific audience facts, not from the number of dimensions. Distorted audience information pushes downstream scenarios and questions in the wrong direction.
Tags and Custom Persona Traits both describe audiences, but their roles differ. Tags are system-generated general audience dimensions used to discover and combine current or potential groups. Custom Persona Traits are known audience facts provided by the brand to protect key signals. The former is exploratory; the latter is business prior knowledge. Together they shape Personas.
12. Additional Business Files
Additional Business Files supplement brand, product, market, and differentiation facts. They strengthen the system's business-context understanding.
Business meaning:
- Fill product details that a short description cannot cover.
- Provide features, pricing, cases, FAQ, positioning, and industry background.
- Strengthen upstream business knowledge and tag space.
- Distinguish real capabilities from marketing slogans.
These files mainly answer what the business is, where capability boundaries are, and what market facts matter. Their value depends on information density, relevance, and freshness. Low-relevance, repetitive, outdated, or overly promotional materials dilute business signals.
13. Sample Personas Files
Sample Personas Files add real user research and audience materials. They affect the realism of Personas and Scenarios.
Business meaning:
- Make personas closer to real customers.
- Provide decision motivation, comparison criteria, and barriers.
- Add information sources, search behavior, and regional preferences.
- Reduce template-like personas.
These files mainly answer who real customers are, why they decide, how they compare, and what blocks them. They differ from Additional Business Files: persona files add audience facts, while business files add business facts.
14. Language Environment
Language environment is not mechanical translation from one language to another. In different markets, the realism of brand descriptions, keywords, audience traits, and query language depends on whether they reflect how the target audience naturally expresses questions.
Business meaning:
- Affects answer quality in the target market.
- Affects whether user questions are realistic.
- Affects whether brands and competitors enter the correct context.
- Affects regional and language judgment in cross-border businesses.
Cross-border business does not automatically mean all English or one single language. Proper nouns, question structure, motivation expression, and comparison style together form the target audience's real language habits. Language environment should follow the target audience and market, not internal brand wording.
15. Tags and Tags Count
A Tag is a structured segmentation dimension for a brand's current or potential audiences. It converts dispersed customer understanding into a composable audience-feature space, mainly covering demographic, regional, psychological, and behavioral segmentation. A Tag is not a brand label or a product feature label.
Business meaning:
- Tags are the upstream audience semantic space for personas, scenarios, and queries.
- Tags determine which customer differences downstream samples expand around.
- Too few tags cause insufficient coverage.
- Too many tags add marginal, repetitive, or off-topic signals.
Tags Count controls tag-pool size. It is not the more the better. Since Tags are upstream, cutting them too early can reduce downstream diversity; but excessive low-quality tags can make personas and scenarios unfocused.
Tags Count should match business complexity and downstream capacity. If the business space is simple, too many tags add noise. If the business has multiple audience segments, decision roles, regional contexts, and comparison criteria, more tags may be needed.
Tags Count should be judged by decision-chain length, not industry name. Short decision chains with low pressure, few roles, and simple comparison criteria usually do not need many tags. Long decision chains with high risk, many roles, or complex comparison criteria need more tags to carry differences. Differences in served audience, users, decision makers, and evaluation criteria are not ordinary tag differences; they suggest different decision chains. If those differences point to different product lines or use cases, split business spaces first, then judge tags for each.
16. Personas and Personas Count
A Persona is a fictional audience archetype built from tag space. It represents current or potential customers in the brand's category or industry. It is not one real user and not a static marketing persona template; it connects audience understanding, decision scenarios, and real questions.
Business meaning:
- Splits the target market into user types.
- Tests brand visibility from multiple audience perspectives.
- Supports differentiated scenarios and queries.
- Helps locate which audiences are stronger or weaker for the brand.
Personas Count controls the number of simulated audiences. Fewer personas produce a more focused but thinner report. More personas expand coverage but raise cost and depend more on tag and trait quality.
Personas Count should also follow decision-chain complexity. If buyer, user, influencer, approver, or comparer differ, more personas may be needed. If the business decision is direct, forcing many personas makes profiles artificial.
| Personas Count State | Business Impact |
|---|---|
| Low | Better for rough exploration; insufficient segmented-audience coverage |
| Moderate | Covers main audiences while keeping the report interpretable |
| High | Suitable for complex markets but more likely to create repeated or forced personas |
Persona quality is not determined by quantity alone. The key is whether personas represent real buyers, decision makers, searchers, or influencers.
17. Persona Similarity
Persona Similarity controls how different personas are from one another. It is not a quality score; it is a business setting for audience similarity.
| Value | Business Meaning | Typical Effect |
|---|---|---|
| Low | Personas differ more; deduplication is stricter | Better for exploring multiple user types, but profiles are more spread out |
| Medium | Balances difference and stability | Better for regular analysis |
| High | Allows personas to be closer | Better for narrow audiences or a single ICP |
Low Similarity with few personas produces a small number of highly different audience views. This helps quickly reveal different user types but is less suitable for deeply describing one narrow group. High Similarity with high Regenerate Degree can create similar profiles, which may fit a concentrated market but reduces diversity.
18. Scenarios and Scenarios Count
A Scenario is the specific context and pain point that makes a persona ask or consult an AI. It connects who the user is with why the user asks now, and is usually shaped by both persona factors and external conditions.
Business meaning:
- Converts persona into a real decision context.
- Reflects comparison, evaluation, risk, region, budget, and use context.
- Helps locate weak decision backgrounds.
- Determines whether queries have real context.
Scenarios Count controls how many scenarios each persona has. More scenarios reveal weaknesses across different decision paths. Too few scenarios can make the report reflect only one problem background.
Scenario count should follow the decision process. Longer decision chains usually involve more information gathering, comparison, risk judgment, budget tradeoff, or internal persuasion. Shorter decision chains may make too many scenarios repetitive.
Scenario differs from Query: Scenario is decision background, while Query is the natural-language question asked in that background. Scenario expands where the question occurs; Query expands how the same situation can be asked.
19. Queries and Queries Count
A Query is the natural-language question actually submitted to Tested LLMs. It directly triggers measurement of AI visibility.
Business meaning:
- Determines answer context.
- Determines whether the brand has a reasonable chance to appear.
- Determines whether the report resembles real user search behavior.
- Determines whether results are repeatable and interpretable.
Queries Count controls how many questions exist under each scenario. Multiple queries under one scenario reduce randomness from a single wording but increase answer volume and cost.
In this business, Query mainly carries recommendation, ranking, comparison, and selection entry points within a category or industry. It does not directly ask about the target brand. Instead, it observes whether the brand naturally enters relatively neutral user questions.
Query quality is mostly determined upstream. Brand Name and Core Keywords define measurement object and business scope. Business Description adds business meaning. Regions define regional context. Personas define user identity. Scenarios define decision background. Query is the final measurement entry produced by the whole chain.
Induced queries change the nature of measurement. They make the model answer around the target brand and can raise some metrics, but this is closer to brand Q&A testing than real AI search visibility testing.
Neutral questions can also produce an important result: if a new, niche, or weakly recognized brand does not appear in relevant questions for a long time, that can indicate structural low visibility. This does not automatically mean measurement failure; it may mean the brand has not entered the natural recommendation set for that business space. Core Keywords define the legitimate visibility opportunity. Too broad a scope creates irrelevant zero visibility; focused scope with persistent zero visibility is closer to a real brand cognition gap.
20. Regenerate Degree
Regenerate Degree indicates how aggressively the system fills missing Personas after deduplication.
Business meaning:
- Controls the balance between quantity and quality when filling personas.
- Affects how strongly the system pursues enough coverage.
- Affects noise and forced-sample risk.
| Value | Business Meaning | Typical Effect |
|---|---|---|
| min | Conservative fill | More stable quality; does not force expansion when quantity is insufficient |
| medium | Balanced fill | Balances coverage and quality |
| full | Aggressive fill | More focused on reaching the target count, but with higher noise risk |
Regenerate Degree has tension with Persona Similarity. Similarity controls the direction of difference; Regenerate Degree controls fill intensity. When the required difference is high and fill intensity is high, the system is more likely to trade off quantity against quality.
21. Tested LLMs and Model Ecosystem
Tested LLMs define the model ecosystem covered by the report. Different models represent different language environments, training content, user markets, and answer styles.
Business meaning:
- Determines which AI search channels are covered.
- Affects brand and competitor appearance probability.
- Affects realism in Chinese, English, and cross-border contexts.
- Helps compare brand strength across model ecosystems.
Query language is strongly related to Tested LLMs. Query language is not an independent business anchor like Core Keywords; it is a measurement feature formed by Persona, region, and scenario context. Chinese audiences, Chinese questions, and Chinese-ecosystem models form one measurement context. English audiences, English questions, and global models form another. Language mismatch causes the report to measure the wrong market.
Cross-border business is complex because users may be in one region, care about another, and search in a third language. Language, Geographic, Preferred, and Tested LLMs together define the measurement context.
22. Run Settings and Answer Volume
Run Settings are the measurement sampling structure. They mainly include Tags, Personas, Scenarios, Queries counts, Persona Similarity, Regenerate Degree, and Tested LLMs. They do not define business facts; they define how much coverage, difference, and model-channel observation is applied to the same business space.
Approximate answer volume is:
Personas x Scenarios x Queries x Tested LLMsBusiness meaning of answer volume:
- Determines report coverage.
- Determines cost and time.
- Determines how much single-run fluctuation affects results.
- Determines whether results fit light exploration, standard benchmark, or deep research.
| Answer Volume | Business Meaning |
|---|---|
| Small | Good for direction validation; lower stability |
| Moderate | Good for regular benchmark; balanced coverage and cost |
| Large | Good for deep research, but cost, time, and interpretation complexity rise |
Larger answer volume does not automatically mean a better report. It adds value only when upstream inputs are accurate, sample quality is reliable, and the task truly requires broader coverage.
Run Settings should be judged from business space and decision chain, not from the idea that larger parameters are always more complete. Simple businesses need focus. Complex businesses can increase coverage, but only when added samples correspond to real audiences, roles, scenarios, or question forms. If the brand has core business lines with clearly different served audiences, use cases, or decision criteria, first consider separate Detection Runs instead of forcing all coverage into one run.
Answer volume is the main proxy for cost and time, but it is not the full cost formula. Actual resource use also depends on model type, answer length, web retrieval, failed retries, and report-analysis calls. The same answer count can therefore have different time and resource costs.
23. Report Metrics Overview
JefurryAxis report metrics are not one single score system. They are complementary observation dimensions.
The atomic unit for metric calculation is one answer produced when one Query is measured by one Tested LLM. If the same Query is sent to multiple Tested LLMs, it creates multiple independent answers. Each answer is analyzed for brand, competitors, ranking, and content, then aggregated into model, Persona, Scenario, and overall report metrics.
| Metric | Full Name | Core Definition | Unit and Range |
|---|---|---|---|
| VS | Visibility Score | Overall score formed from multiple visibility signals | 0-100, generally higher is better |
| MR | Mention Rate | Share of all answers that mention the target brand | 0-100% |
| OF | Occurrence Frequency | Average length-normalized occurrence frequency of the target brand across all answers | >= 0, no fixed upper bound |
| AR | Average Rank | Mean standardized relative position of the target brand in valid ranking answers | > 0 and <= 10; lower is better; empty if no valid ranking |
| BCR | Brand Content Ratio | In brand-mentioned answers, average share of sentences containing the brand | 0-1 |
| Source/Citation | Sources and citations | Observable external source evidence in answers | Not a single numeric metric |
These metrics are mainly for relative comparison: target brand versus competitors in the same report, the same Brand's historical trend under the same configuration, and differences across persona, scenario, and model. They should not be interpreted as fixed industry thresholds outside the task and configuration.
24. VS: Visibility Score
VS is a 0-100 composite visibility score reflecting overall brand performance across multiple dimensions.
Business meaning:
- Provides an overall report entry point.
- Aggregates mention, frequency, rank, and content-share signals.
- Supports trend observation and competitor comparison.
- Cannot explain causes by itself.
VS represents overall visibility quality, not a pursuit of one exposure count. It combines signals such as MR, OF, and AR, but the total score alone does not explain why. High VS usually means the brand performs well in relevant answers. Low VS can come from many causes: the brand is not mentioned, is mentioned but ranked lower, competitors are stronger, certain personas or scenarios are weak, or external content foundation is insufficient.
25. MR: Mention Rate
MR is the proportion of answers that mention the brand, from 0-100%.
MR = answers mentioning target brand / all answersBusiness meaning:
- Measures whether the brand enters model recommendation or comparison scope.
- Reflects basic visibility in the question space.
- Is the key signal for whether the model thinks of the brand.
Low MR usually means the brand does not enter the answer candidate set. Common business causes include unstable brand naming, unclear business description, keywords drifting away from the strong category, mismatched query context, insufficient public content, or competitors being learned and cited more often in the relevant context.
26. OF: Occurrence Frequency
OF is the length-normalized average occurrence frequency of the target brand across all answers. It starts from 0 and has no fixed upper bound. It is not calculated only from answers that mention the brand.
Brand mention count in each answer is adjusted by answer length so longer answers do not automatically gain higher OF. The adjusted values are averaged across all answers. Answers without the brand enter OF as zero.
Business meaning:
- Measures repeated presence across the full answer space.
- Distinguishes being merely listed from being sufficiently explained.
- Works with MR to judge whether the brand is only occasionally listed or has stable presence.
If MR is high but OF is low, the brand may often be included in lists but not explained. If OF is high but MR is low, the brand may be discussed deeply in only a few answers while overall coverage remains limited. Abnormally high OF should be checked against query context for over-induction.
27. AR: Average Rank
AR is the mean standardized relative position of the brand in valid ranking answers. Its direction is special: lower values mean better relative ranking.
For each answer, the analysis model identifies brand rank and total list size from answer semantics. Only answers where the brand is actually ranked and both rank and list size are valid enter AR. Answers where the brand is absent, no clear ranking exists, or no valid list can be recognized do not enter the AR average. Valid ranks are converted into comparable relative positions, so lists of different lengths can be aggregated.
Business meaning:
- Measures whether the brand is recommended early.
- Separates being mentioned from being ranked highly.
- Reveals competitive position in comparison scenarios.
High MR but poor AR means the brand appears often but is not necessarily preferred. This usually means the brand has basic awareness but lacks enough comparative advantage in the model's view, or competitors have clearer evidence of strengths.
AR describes position after the brand is ranked; it does not describe the probability of being ranked. A brand can have low MR and good AR if it appears rarely but ranks well when it appears.
28. BCR: Brand Content Ratio
BCR for a single answer is the number of sentences containing the target brand name or alias divided by the total number of sentences in that answer. Sentence boundaries include not only periods, but also question marks, exclamation marks, paragraphs, and structured list boundaries. Multiple mentions in the same sentence still count as one brand-containing sentence.
Report-level BCR is the average of positive single-answer BCR values. Answers that do not mention the brand, with BCR equal to zero, do not enter this average. Therefore, BCR mainly describes how much content space the brand receives after it appears, not brand coverage across all answers.
Business meaning:
- Observes whether the brand receives enough explanation after being mentioned.
- Separates being named from being actually discussed.
- Works with MR, OF, and AR to judge visibility quality.
- Helps identify whether queries over-induce the brand.
BCR is not simply higher is better. Too low means insufficient brand content. Too high can mean the query over-induced the brand or the answer structure is unnatural. Its business meaning is whether brand content share is reasonable, not whether more brand content is always better.
28.1 Metric Combination Diagnostic Matrix
| Metric Combination | Business Diagnosis |
|---|---|
| High MR, good AR | The brand often enters answers and is usually in a favorable recommendation position |
| High MR, poor AR | The brand has basic awareness but insufficient relative recommendation advantage |
| Low MR, good AR | The brand rarely enters answers, but ranks strongly when it does |
| Low MR, poor or empty AR | The brand lacks both basic visibility and stable ranking signal |
| High MR, low OF | The brand is often listed, but repeated emphasis and explanation are limited |
| Low MR, high OF | The brand appears in few answers, but is discussed intensively when it appears |
| High OF, low BCR | Brand name appears repeatedly, but explanation may be scattered or brief |
| Low OF, high BCR | Brand appears less often, but receives concentrated explanation when it does |
| Low target-brand MR and low BCR | The brand is hard to enter answers and receives little content space after appearing |
| Overall metrics stable, but one Persona or Scenario is weak | The issue is more likely concentrated in a specific audience or decision context than in global brand awareness |
29. Source/Citation
Source/Citation describes observable information sources, citations, or content evidence in model answers.
Business meaning:
- Provides observable evidence of external sources linked to answers.
- Reveals competitor advantages in external content.
- Helps locate the target brand's public-content gaps.
- Supports direction for external content optimization.
Source/Citation focuses on external information foundations visible in answers. It can support source-structure analysis, but it is not the model's full knowledge source and cannot alone prove complete causality for brand mention or absence. Structured website content, FAQ, product comparison pages, cases, third-party coverage, rankings, reviews, and industry materials can all affect brand visibility in model answers.
Observable Source/Citation coverage depends on model channel, retrieval capability, search results, and whether the answer returns sources. Some answers may have no available citation data. No observed source does not mean the brand has no external content foundation. Source counts across models should not be directly compared without coverage context.
30. Strong Brand but VS Below Expectation
A strong brand with low VS is one of the most important JefurryAxis business scenarios. It does not automatically mean system error or real brand weakness. It requires separating two issue types.
The first type is measurement-configuration bias. Brand name, aliases, keywords, region, language, tested models, personas, scenarios, or queries may not match the real business, causing the report to underestimate the brand.
The second type is a real AI visibility gap. Traditional awareness, advertising, and offline influence may not have converted into LLM-visible content. Model answers may still favor competitors with richer public content, more third-party references, or stronger structured materials.
The value of this scenario is that it separates a strong brand from a brand visible in AI search. They are related but not the same.
31. Long-Term Trends and Measurement Uncertainty
Long-term trend value comes from repeatability. The same Brand is suitable for trend comparison only under the same input, same query space, and same Tested LLMs combination.
Business meaning:
- Observes whether AI visibility improves.
- Judges whether external content optimization has effect.
- Detects competitor improvement in certain scenarios.
- Identifies impact from model-ecosystem changes.
LLM answers are non-deterministic. Even if inputs and model names remain the same, wording, brand choice, ranking structure, and citation sources can change. Web search results, available sources, model service updates, and analysis-model judgment can also cause fluctuation. A single run is a sample of the AI visibility environment at that time, not an immutable fact.
The same model name does not guarantee the same underlying model. External model services may update weights, retrieval strategy, safety rules, or answer style, causing baseline drift. Trend changes may come from real brand changes or from model-ecosystem changes.
Configuration changes, query changes, model-combination changes, target-market changes, and model-service changes all alter experimental conditions. Trend analysis should keep the measurement baseline as consistent as possible and judge changes using multiple runs, change magnitude, several metrics, and Persona, Scenario, Model, and Source details. Small single-run movement is closer to noise; persistent cross-dimensional movement is more business-explainable.
32. Summary
JefurryAxis core business logic can be summarized in three sentences:
- Brand Name and Core Keywords define who is measured and around which business space.
- Audience and scenario chains convert business space into a testable user-question space.
- Report metrics reflect brand visibility, competitive position, and content foundation in LLM answers.
Each variable's importance depends on its position in the chain and its business role. Brand Name and Core Keywords are foundation anchors. Business knowledge and audience variables determine how the system understands those anchors. Sampling parameters determine measurement coverage. Report metrics explain what happened. The system's core value is not manufacturing higher scores, but explaining why scores form and where the brand has real opportunities or gaps across audiences, scenarios, models, and sources.