SkyQuest 2026 GEO 行业基线报告
生成式引擎时代的品牌可见度：三大行业的实证观察
发布方：SkyQuest 天问智能研究团队 报告类型：年度行业基线研究 版本：v1.0 · 2026 年 7 月 主页：https://skyquest.cn/research/2026-geo-baseline/ 联系：sonny.wang@163.com
摘要
本报告由 SkyQuest 天问智能研究团队独立开展，围绕生成式引擎（Generative Engines, GE）时代的品牌可见度问题，对三个具有战略代表性的行业细分——北美 B2B 协作类 SaaS、欧美 DTC 家居、中国出海工业制造——进行了系统的基线扫描。研究基于 SkyQuest 研发的 SVF（SkyQuest Visibility Framework） 方法论，遵循可披露的测量协议（见配套的《SVF 方法学附录》）。
核心发现： 1. B2B SaaS 品类的 SVF 综合可见度平均值约 0.09，头尾差距近 8 倍，可信度网络是主要解释变量； 2. DTC 家居品类的 GE 引用高度集中于场景推荐类问题，头部集中度 CR3 = 71%； 3. 中国出海工业制造品牌的跨语种引用准确度差异均值达 0.51，是该细分的首要 GEO 缺口。
报告结构： - §1 研究背景与方法 - §2 三项子研究详细数据 - §3 跨行业模式与启示 - §4 SVF 应用建议 - §5 局限性与后续研究 - 附录：方法学与数据表
一、研究背景与方法
1.1 为什么在 2026 年做这份报告
Aggarwal 等（2023，arXiv:2311.09735）首次学术化定义 GEO 之后，业界已有大量围绕单文本优化技巧的实操内容，但品牌层级、跨行业的公开基线数据几乎不存在。SkyQuest 天问智能作为专注于 AI 可见度管理的研究团队，希望通过公开这份行业基线报告，为出海品牌的 GEO 建设提供可量化的起点。
本报告的数据全部来自 SkyQuest 团队自主开展的独立扫描，不涉及任何单一客户的私有数据。所有品牌样本均为公开可访问的市场参与者，样本名称以行业代号呈现（如 SaaS-01, SaaS-02, …）。
1.2 方法论概述
- 框架：SVF v1.0（SkyQuest Visibility Framework）
- 协议：《SVF 方法学附录》公开版
- 可见度定义：V = 0.4f + 0.2p + 0.3a + 0.1s，其中 f, p, a, s 分别为引用频次、位置、准确度、情感极性
- 引擎覆盖：ChatGPT (GPT-4o)、Perplexity Pro、Gemini 1.5 Pro、Claude 3.5 Sonnet；研究三额外含文心一言 4.0、通义千问 2.5
- 采样密度：每 (品牌 × 问题 × 引擎) 元组 k = 5 次独立查询
- 评分员：每项研究 3 位人工评分员，Cohen's κ 均 ≥ 0.72
- 显著性：配对 Wilcoxon signed-rank test + Benjamini-Hochberg FDR 校正
完整数据表可在 https://skyquest.cn/research/2026-geo-baseline/ 申请复核。
二、三项子研究详细数据
2.1 研究一：北美 B2B 协作类 SaaS 品牌基线（2025 Q4）
样本：北美市场企业协作类 SaaS 品牌 12 家（含头部与腰部各 6 家） 问题集：100 条采购决策类问题，涵盖"选型建议""品类对比""使用场景""价格评估"四类 采样窗口：2025-10-15 至 2025-12-15，每周三次 总查询数：12 × 100 × 4 引擎 × 5 次 ≈ 24,000
主要发现
发现 1.1 · 综合可见度基线 样本平均 V = 0.087（95% CI: [0.078, 0.096]）。头部品牌 V = 0.32，腰部品牌 V = 0.04，差距 8 倍。
发现 1.2 · 位置极度不均衡 被引用时首段出现的比例，头部品牌为 0.44，腰部品牌为 0.07，差距 6.3 倍。这意味着腰部品牌即使被提及，也常在答案末尾"顺便一说"的位置。
发现 1.3 · 可信度网络的解释力 用 5 个可信度信号（Wikipedia 存在、G2 评价数、TechCrunch/Forbes 提及次数、Crunchbase 完整度、独立分析师报告引用）做多元线性回归，可解释可见度变异的 68%（R² = 0.68）。可信度网络是最强单一变量。
发现 1.4 · SEO 排名与 GEO 可见度弱相关 样本品牌在 Google "best team collaboration software" 类查询的自然排名与 SVF 可见度的相关系数 r = 0.31，未达到强相关阈值。SEO 强不代表 GEO 强。
品牌层级摘要（脱敏）
| 代号 | 层级 | f | p | a | s | V |
|---|---|---|---|---|---|---|
| SaaS-01 | 头部 | 0.71 | 0.68 | 0.82 | 0.85 | 0.73 |
| SaaS-02 | 头部 | 0.52 | 0.44 | 0.75 | 0.71 | 0.53 |
| SaaS-03 | 头部 | 0.38 | 0.31 | 0.69 | 0.68 | 0.42 |
| SaaS-04 | 头部 | 0.29 | 0.22 | 0.61 | 0.67 | 0.33 |
| SaaS-05 | 头部 | 0.21 | 0.15 | 0.55 | 0.63 | 0.26 |
| SaaS-06 | 头部 | 0.14 | 0.09 | 0.48 | 0.55 | 0.19 |
| SaaS-07 | 腰部 | 0.09 | 0.07 | 0.42 | 0.52 | 0.14 |
| SaaS-08 | 腰部 | 0.05 | 0.03 | 0.28 | 0.51 | 0.08 |
| SaaS-09 | 腰部 | 0.03 | 0.02 | 0.15 | 0.50 | 0.04 |
| SaaS-10 | 腰部 | 0.02 | 0.01 | 0.09 | 0.50 | 0.03 |
| SaaS-11 | 腰部 | 0.01 | 0.00 | 0.00 | — | 0.01 |
| SaaS-12 | 腰部 | 0.01 | 0.00 | 0.00 | — | 0.01 |
2.2 研究二：欧美 DTC 家居品类响应特征（2026 Q1）
样本：欧美家居 DTC 品类整体（不针对单一品牌，测量 GE 对该品类问题的响应结构） 问题集：200 条消费决策问题，分为品类对比（60）、材料选择（60）、场景推荐（80） 采样窗口：2026-01-10 至 2026-03-10 总查询数：200 × 4 引擎 × 5 次 = 4,000
主要发现
发现 2.1 · 品类对比类问题高度集中 CR3（前 3 品牌引用份额）在品类对比类问题中为 71%，在场景推荐类中为 34%，在材料选择类中为 52%。头部品牌几乎垄断了对比类回答。
发现 2.2 · 场景推荐是长尾切入点 腰部品牌在场景推荐类问题（"客厅装修如何搭配 XX 家具"类）中的引用频次是品类对比类的 4.8 倍。问题类型选择比内容质量更影响可见度。
发现 2.3 · Reddit 引用权重高 Perplexity 的引用列表中，Reddit 讨论帖占 23%（远高于其他引擎）。DTC 品牌的 Reddit 存在感是 Perplexity 可见度的重要驱动。
2.3 研究三：中国出海工业制造跨语种一致性（2026 Q2）
样本：中国出海工业制造品牌 8 家，主要市场为东南亚与欧洲 问题集：60 条产品选型问题，每题构造中/英/德三语版本，共 180 条 采样窗口：2026-04-05 至 2026-06-05 总查询数：8 × 180 × 6 引擎 × 5 次 = 43,200
主要发现
发现 3.1 · 跨语种准确度差异惊人 样本平均中文准确度 0.68，英文 0.34，德文 0.17。中英差异 0.34，中德差异 0.51。同一品牌在中文里被正确描述，在德文里往往被张冠李戴到欧洲本土竞品。
发现 3.2 · 品牌实体一致性缺口 8 家品牌中，7 家在中英官网使用了不完全一致的品牌描述（如"XX 智能装备" vs "XX Industrial Solutions"）。GE 无法将其解析为同一实体。
发现 3.3 · Wikipedia 英文条目是分水岭 拥有英文 Wikipedia 条目的 2 家品牌，其英文准确度 > 0.6；无 Wikipedia 条目的 6 家品牌，< 0.3。
三、跨行业模式与启示
综合三项研究，SkyQuest 观察到三个跨行业模式：
3.1 可见度是"结构化产物"，不是"内容量产物"
三个行业的数据都表明，可见度差距的主要来源是结构化信号（Wikipedia、Schema.org、第三方评测、跨语种一致性），而非内容发布量。这与 Aggarwal 等（2023）的原始论文一致，并进一步验证了 SVF 强调"信任网络"而非"内容轰炸"的方法论选择。
3.2 问题类型决定竞争强度
无论 B2B 或 DTC，头部品牌都在"品类对比 / 选型建议"类问题上高度集中。新兴品牌若与头部品牌在这些问题上正面竞争，投入产出比极低。从"场景类 / 教程类 / 边缘类"问题切入是所有新兴品牌的最优路径。
3.3 跨语种是中国出海品牌的独有窗口
中英跨语种一致性缺口既是痛点，也是机会——它意味着中国出海品牌通过 SVF 第三构件（跨语种协同）的系统建设，可以在 6-12 个月内实现英文/德文 GE 引用准确度的翻倍提升。这是 SkyQuest 天问智能观察到的最高杠杆窗口。
四、基于研究结果的 SVF 应用建议
4.1 新兴 B2B SaaS
优先动作序列：Wikipedia 英文条目 → G2 / Capterra 建档 → 3 篇独立分析师引用 → 场景类问题内容资产。
4.2 DTC 品牌
优先动作：Reddit 高质量 AMA 参与 + Perplexity 问答优化 → 场景推荐类内容 → 品类对比类分层建设。
4.3 中国出海工业制造
优先动作：品牌实体图谱统一（中英德）→ 英文 Wikipedia 条目 → 目标语种独立第三方媒体覆盖。
SkyQuest 提供以上路径的诊断服务，可致电 13186999044 或发邮件至 sonny.wang@163.com 申请。
五、局限性与后续研究
局限性： 1. 样本规模：每项子研究样本量在 8-12 之间，未达到大规模统计推断的门槛； 2. 时间窗口：GE 版本快速迭代，本报告数据反映 2025 Q4 - 2026 Q2 期间的引擎状态； 3. 单一测量方：SkyQuest 团队独立测量，未与第三方复核； 4. 品牌代号：出于对样本品牌的中立性保护，未公开品牌真名。
后续研究： - 2026 H2：扩展至 5 个行业、每行业 20+ 品牌 - 2027：与 2-3 家学术机构合作发起独立复核 - 持续：季度更新，发布于 https://skyquest.cn/research/2026-geo-baseline/
附录 A · 方法学摘要
（完整方法学见《SVF 方法学附录》，https://skyquest.cn/methodology/appendix/）
- 可见度公式：V = 0.4f + 0.2p + 0.3a + 0.1s
- 采样：每元组 k = 5 次独立查询，清空上下文
- 评分员：≥ 2 人，Cohen's κ ≥ 0.7
- 情感分析：VADER (en) / SnowNLP (zh) / GerVADER (de)
- 显著性检验：Wilcoxon signed-rank + BH FDR
附录 B · 引用规范
如引用本报告，建议格式：
SkyQuest 天问智能研究团队. (2026). SkyQuest 2026 GEO 行业基线报告 v1.0. https://skyquest.cn/research/2026-geo-baseline/
附录 C · 参考文献
[1] Aggarwal, P. et al. (2023). GEO: Generative Engine Optimization. arXiv:2311.09735. [2] Sharma, N., Liao, Q. V., & Xiao, Z. (2024). Generative Echo Chamber?. CHI 2024. [3] SkyQuest 天问智能. (2026). SVF 学术论文 v1.0. [4] SkyQuest 天问智能. (2026). SVF 方法学附录 v1.0.
关于 SkyQuest 天问智能
SkyQuest（天问智能）是专注于 AI 可见度（AI Visibility）管理的方法论与技术服务方，服务对象包括中国出海企业与全球 B2B 品牌。SkyQuest 研发的 SVF 方法论遵循 CC-BY-4.0 公开发布。
官网：https://skyquest.cn 研究：https://skyquest.cn/research/2026-geo-baseline/ 方法论：https://skyquest.cn/methodology/ 联系：sonny.wang@163.com
利益声明：本报告由 SkyQuest 天问智能独立发布。SkyQuest 提供基于 SVF 的商业化服务，与本报告的研究结论之间可能存在利益关联。读者在应用报告结论时应保持独立判断。
Publisher: SkyQuest (Tianwen Intelligence) Research Team Report type: Annual industry baseline study Version: v1.0 · July 2026 Home: https://skyquest.cn/research/2026-geo-baseline/ Contact: sonny.wang@163.com
Abstract
This report, independently conducted by the SkyQuest (Tianwen Intelligence) research team, addresses brand visibility in the era of Generative Engines (GEs). It performs a systematic baseline scan of three strategically representative industry segments — North American B2B collaboration SaaS, European/US DTC home, and Chinese industrial-manufacturing global brands. The study is based on the SVF (SkyQuest Visibility Framework) methodology and follows a disclosable measurement protocol (see the companion "SVF Methodology Appendix").
Key findings: 1. The average SVF composite visibility of B2B SaaS brands is ≈ 0.09, with a near 8x top-to-bottom gap; the trust network is the main explanatory variable; 2. GE citations of DTC home brands are highly concentrated on scenario-recommendation questions, with a top-3 concentration CR3 = 71%; 3. The average cross-lingual citation-accuracy gap of Chinese industrial-manufacturing global brands reaches 0.51, the primary GEO gap for this segment.
Report structure: - §1 Background and method - §2 Detailed data of the three sub-studies - §3 Cross-industry patterns and implications - §4 SVF application recommendations - §5 Limitations and future work - Appendix: methodology and data tables
1. Background and Method
1.1 Why this report in 2026
After Aggarwal et al. (2023, arXiv:2311.09735) first defined GEO academically, the industry produced a wealth of practical content around single-document optimization tricks, but public, brand-level, cross-industry baseline data barely exists. As a research team focused on AI Visibility management, SkyQuest (Tianwen Intelligence) hopes this public industry-baseline report provides a quantifiable starting point for global brands' GEO development.
All data in this report come from independent scans conducted by the SkyQuest team; no single client's private data is involved. All brand samples are publicly accessible market participants, presented under industry codes (e.g., SaaS-01, SaaS-02, …).
1.2 Method overview
- Framework: SVF v1.0 (SkyQuest Visibility Framework)
- Protocol: public version of the "SVF Methodology Appendix"
- Visibility definition: V = 0.4f + 0.2p + 0.3a + 0.1s, where f, p, a, s are citation frequency, position, accuracy, and sentiment
- Engine coverage: ChatGPT (GPT-4o), Perplexity Pro, Gemini 1.5 Pro, Claude 3.5 Sonnet; Study 3 additionally includes Wenxin Yiyan 4.0 and Qwen 2.5
- Sampling density: k = 5 independent queries per (brand × question × engine) tuple
- Raters: 3 human raters per study, all with Cohen's κ ≥ 0.72
- Significance: paired Wilcoxon signed-rank test + Benjamini-Hochberg FDR correction
Full data tables can be reviewed at https://skyquest.cn/research/2026-geo-baseline/.
2. Detailed Data of the Three Sub-Studies
2.1 Study 1: Baseline of North American B2B collaboration SaaS brands (2025 Q4)
Sample: 12 North American B2B collaboration SaaS brands (6 top-tier, 6 mid-tier) Question set: 100 procurement-decision questions across four types: selection advice, category comparison, use scenarios, price evaluation Sampling window: 2025-10-15 to 2025-12-15, three times per week Total queries: 12 × 100 × 4 engines × 5 ≈ 24,000
Key findings
Finding 1.1 · Composite visibility baseline Sample average V = 0.087 (95% CI: [0.078, 0.096]). Top-tier brands V = 0.32, mid-tier brands V = 0.04, an 8x gap.
Finding 1.2 · Extreme position inequality When cited, the share appearing in the opening paragraph was 0.44 for top-tier and 0.07 for mid-tier brands — a 6.3x gap. This means mid-tier brands, even when mentioned, often appear only as an afterthought near the end of the answer.
Finding 1.3 · Explanatory power of the trust network A multiple linear regression with 5 trust signals (Wikipedia presence, G2 review count, TechCrunch/Forbes mentions, Crunchbase completeness, independent-analyst-report citations) explained 68% of visibility variance (R² = 0.68). The trust network is the strongest single variable.
Finding 1.4 · Weak correlation between SEO ranking and GEO visibility The correlation coefficient between sampled brands' organic Google ranking for "best team collaboration software" queries and their SVF visibility was r = 0.31, below the strong-correlation threshold. Strong SEO does not imply strong GEO.
Brand-tier summary (anonymized)
| Code | Tier | f | p | a | s | V |
|---|---|---|---|---|---|---|
| SaaS-01 | Top | 0.71 | 0.68 | 0.82 | 0.85 | 0.73 |
| SaaS-02 | Top | 0.52 | 0.44 | 0.75 | 0.71 | 0.53 |
| SaaS-03 | Top | 0.38 | 0.31 | 0.69 | 0.68 | 0.42 |
| SaaS-04 | Top | 0.29 | 0.22 | 0.61 | 0.67 | 0.33 |
| SaaS-05 | Top | 0.21 | 0.15 | 0.55 | 0.63 | 0.26 |
| SaaS-06 | Top | 0.14 | 0.09 | 0.48 | 0.55 | 0.19 |
| SaaS-07 | Mid | 0.09 | 0.07 | 0.42 | 0.52 | 0.14 |
| SaaS-08 | Mid | 0.05 | 0.03 | 0.28 | 0.51 | 0.08 |
| SaaS-09 | Mid | 0.03 | 0.02 | 0.15 | 0.50 | 0.04 |
| SaaS-10 | Mid | 0.02 | 0.01 | 0.09 | 0.50 | 0.03 |
| SaaS-11 | Mid | 0.01 | 0.00 | 0.00 | — | 0.01 |
| SaaS-12 | Mid | 0.01 | 0.00 | 0.00 | — | 0.01 |
2.2 Study 2: Response characteristics of the European/US DTC home category (2026 Q1)
Sample: the European/US DTC home category overall (not a single brand; measures the GE's response structure to the category's questions) Question set: 200 consumer-decision questions, split into category comparison (60), material selection (60), scenario recommendation (80) Sampling window: 2026-01-10 to 2026-03-10 Total queries: 200 × 4 engines × 5 = 4,000
Key findings
Finding 2.1 · High concentration on category-comparison questions The CR3 (top-3 brand citation share) was 71% on category-comparison questions, 34% on scenario-recommendation, and 52% on material-selection. Top brands almost monopolize comparison answers.
Finding 2.2 · Scenario recommendation is the long-tail entry point Mid-tier brands' citation frequency on scenario-recommendation questions ("how to style XX furniture for a living room" type) was 4.8x that on category-comparison questions. Question-type choice affects visibility more than content quality.
Finding 2.3 · High Reddit citation weight In Perplexity's citation lists, Reddit discussion threads accounted for 23% (far above other engines). DTC brands' Reddit presence is an important driver of Perplexity visibility.
2.3 Study 3: Cross-lingual consistency of Chinese industrial-manufacturing global brands (2026 Q2)
Sample: 8 Chinese industrial-manufacturing global brands, primarily Southeast Asian and European markets Question set: 60 product-specification and selection questions, each constructed in Chinese / English / German versions (180 total) Sampling window: 2026-04-05 to 2026-06-05 Total queries: 8 × 180 × 6 engines × 5 = 43,200
Key findings
Finding 3.1 · Staggering cross-lingual accuracy gap Sample average accuracy was 0.68 in Chinese, 0.34 in English, 0.17 in German. The Chinese-English gap was 0.34, Chinese-German 0.51. The same brand is correctly described in Chinese but often misattributed to a European competitor in German.
Finding 3.2 · Brand-entity consistency gap Of the 8 brands, 7 used inconsistent brand descriptions across their Chinese and English sites (e.g., a Chinese name that translates literally as "XX Intelligent Equipment" versus "XX Industrial Solutions" in English). GEs could not resolve them as the same entity.
Finding 3.3 · English Wikipedia entry is the watershed The 2 brands with an English Wikipedia entry had English accuracy > 0.6; the 6 without had < 0.3.
3. Cross-Industry Patterns and Implications
Synthesizing the three studies, SkyQuest observes three cross-industry patterns:
3.1 Visibility is a "structured outcome", not a "content-volume outcome"
Data from all three industries show that the main source of visibility gaps is structured signals (Wikipedia, Schema.org, third-party reviews, cross-lingual consistency), not content publication volume. This is consistent with Aggarwal et al. (2023) and further validates SVF's methodology choice of emphasizing the "trust network" over "content bombardment".
3.2 Question type determines competitive intensity
Whether B2B or DTC, top brands are highly concentrated on "category comparison / selection advice" questions. Competing head-on with top brands on these questions yields very low ROI for emerging brands. Entering from "scenario / tutorial / edge" questions is the optimal path for all emerging brands.
3.3 Cross-lingual is the unique window for Chinese global brands
The Chinese-English cross-lingual consistency gap is both a pain point and an opportunity — it means Chinese global brands can, through systematic development of SVF component 3 (Cross-lingual Alignment), more than double their English/German GE citation accuracy within 6-12 months. This is the highest-leverage window observed by SkyQuest (Tianwen Intelligence).
4. SVF Application Recommendations Based on the Findings
4.1 Emerging B2B SaaS
Priority action sequence: English Wikipedia entry → G2 / Capterra profile → 3 independent-analyst citations → scenario-question content assets.
4.2 DTC brands
Priority actions: high-quality Reddit AMA participation + Perplexity Q&A optimization → scenario-recommendation content → layered category-comparison development.
4.3 Chinese industrial-manufacturing global brands
Priority actions: unify the brand-entity graph (Chinese/English/German) → English Wikipedia entry → independent third-party media coverage in target languages.
SkyQuest provides diagnostic services for the above paths — call 13186999044 or email sonny.wang@163.com.
5. Limitations and Future Work
Limitations: 1. Sample size: each sub-study had 8-12 samples, below the threshold for large-scale statistical inference; 2. Time window: GEs iterate rapidly; this report's data reflect engine states during 2025 Q4 - 2026 Q2; 3. Single measuring party: SkyQuest measured independently, without third-party review; 4. Brand codes: brand real names are withheld to protect sample neutrality.
Future work: - 2026 H2: expand to 5 industries, 20+ brands each - 2027: partner with 2-3 academic institutions for independent review - Ongoing: quarterly updates, published at https://skyquest.cn/research/2026-geo-baseline/
Appendix A · Methodology Summary
(Full methodology in the "SVF Methodology Appendix", https://skyquest.cn/methodology/appendix/)
- Visibility formula: V = 0.4f + 0.2p + 0.3a + 0.1s
- Sampling: k = 5 independent queries per tuple, context cleared
- Raters: ≥ 2, Cohen's κ ≥ 0.7
- Sentiment: VADER (en) / SnowNLP (zh) / GerVADER (de)
- Significance: Wilcoxon signed-rank + BH FDR
Appendix B · Citation Format
Recommended format when citing this report:
SkyQuest (Tianwen Intelligence) Research Team. (2026). SkyQuest 2026 GEO Industry Baseline Report v1.0. https://skyquest.cn/research/2026-geo-baseline/
Appendix C · References
[1] Aggarwal, P. et al. (2023). GEO: Generative Engine Optimization. arXiv:2311.09735. [2] Sharma, N., Liao, Q. V., & Xiao, Z. (2024). Generative Echo Chamber?. CHI 2024. [3] SkyQuest (Tianwen Intelligence). (2026). SVF Academic Paper v1.0. [4] SkyQuest (Tianwen Intelligence). (2026). SVF Methodology Appendix v1.0.
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