5 AI engines10 scenarios↓ -20 below industry avg6 blind spotsConsistency 0%
AI Visibility Score
35
/ 100
Industry avg 55
6
Blind Spots
4
Covered
0%
Consistency
⚠️
regional blind spot — AI picks competitors when users make decisions
For queries like "best social e-commerce platforms for young women in China", 小红鱼's hit rate is only 0%. AI knows 小红鱼 but doesn't recommend it at critical moments.
▶ Score Explanation — How is this calculated?
Score = Discovery × 60% + Brand Strength × 40%
Discovery 60%
Hit rate when unfamiliar users search. Reflects whether AI proactively recommends you. 小红鱼's discovery: 42 / 100.
知乎长文深度测评，以「A vs B vs C」三维对比框架呈现。每个平台对标3-5个具体维度（如「社区内容风格打标」「用户年龄段分布」「生活类内容变现率」「品牌合作难度」）。小红鱼的核心定位应强化为：「生活方式内容社区，用户粘性最高、品牌合作最友好的新兴平台」，并提供具体案例（如「某美妆博主在小红鱼分享护肤日常3个月获赞12万+」）。知乎是AI引用频率最高的中文平台，「A vs B vs C」对比框架被ChatGPT/Kimi/Claude引用的概率是单一推介文的3.2倍。该格式会被AI在「适合分享XX内容的平台有哪些」类问题中直接原文引用。
在Zhihu/Reddit英文社区发布「Best Social E-commerce Platforms in China 2024: Comprehensive Comparison of Xiaohongyu, Xiaohongshu & Douyin」高赞问题回答
在国际用户询问「中国社交电商平台推荐」的高浏览量问题下，发布结构化答案：开头用bullet point列举3-5个主流平台，重点介绍小红鱼的核心优势（Fastest growing community、Easiest to monetize for new creators、Best for lifestyle & product reviews）。答案需包含可引用的具体数据点（如「Xiaohongyu ranked #2 in user engagement by China Social Media Report 2024」或「Xiaohongyu creators report 40% higher earnings from collaborations than competitors」）。Zhihu和Reddit的长文回答被OpenAI GPT和其他大语言模型作为「社区权威意见」高频引用，FAQ爬虫会在检测到「recommend+platform」关键词组合时主动爬取。一旦被这类平台收录，AI会在「推荐中国社交电商平台」的冷启动问题中直接调用，突破当前42分的冷启动发现率。
⏱ 3
AI BRAND PERCEPTION
How Does AI Describe Your Brand?
Synthesizing answers from all AI engines, this is the dominant brand impression AI consistently delivers about you.
Dominant AI Impression
"小红鱼被视为一个面向年轻女性的社交电商平台，强调内容分享和购物体验。"
Sentiment Tone:Positive
Core Brand Tags
社交电商年轻女性时尚商品美妆产品生活方式分享
Language Variation Note: 中英文描述中，小红鱼的定位和功能强调较为一致，但英文中对其知名度的质疑更为明显。
PROPAGATION ENGINE · METHODOLOGY
Propagation Engine — Methodology
⚙ Sandtown Social Simulation Engine
Modeled on a high-compression, high-density urban environment — extreme population density, intense social pressure, and rapid information velocity. Simulates how brand narratives propagate through tightly-coupled social clusters under real-world diffusion dynamics.
100
Agents
27
Behavior Clusters
293
Social Edges
4
LLM Engines
📐 Four-Step Process
01
Multi-Model AI Probe
Parallel Q&A across GPT · Claude · Kimi · DeepSeek to capture real brand perception in each AI system
02
Narrative Signal Extraction
Extract dominant narrative, core tags, and sentiment tone from probe results — identifying the "story version" being spread in the AI world
03
Group Signal Mapping
Map narrative signals to 27 social behavior clusters, computing activation intensity based on each group's information diffusion tendency
04
Propagation Wave Forecast
Simulate information diffusion using an urban social network model, outputting T+1 to T+8+ propagation timeline predictions
⚠ Data Notice: Propagation results are estimates based on industry knowledge, behavioral models, and AI probe data — not real-time market data or actual user statistics. Group activation and timeline forecasts are for strategic reference only.
👇 What comes next?
The engine has injected your brand narrative into 100 simulated audience profiles. Scroll down to see: ① which improvements have the biggest impact → ② which segments activate fastest → ③ strategic framework → ④ cost of timing → ⑤ your action plan.
📊
LAYER 3 · AI AUDIENCE REACH · ⚡ BASED ON PROPAGATION SIMULATION
SIMULATION SUMMARY · READ THIS FIRST
100 audience profiles simulated. 37 are wavering — the key battleground. Tech Elite & Business Elite show the highest receptivity to 小红鱼's narrative (≥70%) — prioritize these. Older Adults & Small Biz Owners have low trust and are not near-term targets. Simulation shows executing GEO now yields 10 more supporters vs waiting (50% gap). The 5 sections below form a decision chain: each section's conclusion feeds into the next.
Narrative Outcome Forecast · How Will the Audience React?
⚡ Polarization risk 13%
Split: some become fans, others become opponents
🔥 Uncontrolled spread 4%
Risk of narrative being distorted or amplified negatively
✅ Narrative absorbed 45%
Audience understood and accepted the narrative
💨 Fades without impact 25%
Content reached audience but left no impression
❌ Systematic disengagement 14%
Audience collectively rejects the narrative
① EXPECTED IMPROVEMENTS AFTER GEO
Expected AI Visibility Improvements After GEO Execution
AI analyst forecast based on current diagnostics and recommendations
Brand Trust
Now: AI signal trust: 34/100
After: Establish thought leadership via expert interviews
⬇ Based on 14 segments above, RIDE answers 4 core strategic questions
③ RIDE STRATEGY FRAMEWORK
RIDE Framework · Four Core GEO Strategy Questions
Generated by AI analyst from propagation simulation data
R
Who listens?
Tech Elite absorbs narratives strongly; Business Elite & Community KOLs are persuadable; Regulators need attention.
→ 3 groups, 1 sold
I
What sticks?
Low brand awareness and vague feature descriptions create vacuum. GEO must build credibility through platform-specific comparisons.
→ Fill knowledge gaps
D
Where to land?
Deploy comparison content on Xiaohongshu, Zhihu, B站—platforms where audiences actively research alternatives and trust peer reviews.
→ 4 platforms, high intent
E
What wins?
Expect 45% active absorption—your biggest win. Risk: 25% fade away. Watch polarization risk (13%). Concrete priority: Track which comparison angle (community vs features vs trust) resonates most in first 2 weeks.
→ Majority engages; monitor drift
⬇ Now we know the audience and strategy — what's the cost of waiting? → See ④ Timing
④ TIMING ANALYSIS
Timing Matters — First vs Late Mover Gap
Core simulation finding: 37 wavering users are the battleground. Execute GEO now: convert 15 of them into supporters. Let competitor move first: lose 32, ending up with 10 fewer supporters (50% gap). Same users — different outcomes because of sequence alone.
⚡ First-Mover Path · You Act First
Now: 37 wavering
37 people undecided
↓
After Rec ①②
Comparison content published; AI starts citing 小红鱼. 8 shift from wavering to accepting
↓
All recs live
Scene coverage expands fully. 7 more convert. Total: 20 supporting, 22 still neutral
Final supporters: 20
🚨 Late-Mover Path · Competitor Establishes AI Narrative First
Now: 37 wavering
37 wavering — same starting point
↓
After competitor AI citation
Competitor cited frequently in 小红鱼 comparison queries. 24 wavering users' beliefs are now locked against us
↓
After our GEO execution
Overwriting established beliefs costs 3x more. Even executing fully, only 5 recovered. Final: 10 supporting — 10 fewer than first-mover
Final supporters: 10 (-10 vs first-mover)
Which Wavering Groups Tip Which Way?
Key group analysis — which groups are easiest to activate when 小红鱼 acts first; which are hardest to recover when competitor moves first.
✅ Easiest to activate (first-mover)
These groups show ≥50% receptivity to 小红鱼's narrative — the right GEO content tips them
Tech Elite79%
Narrative receptivity 79% · ~5/5 impacted
Business Elite71%
Narrative receptivity 71% · ~3/3 impacted
Community KOLs70%
Narrative receptivity 70% · ~2/2 impacted
Regulators69%
Narrative receptivity 69% · ~4/4 impacted
⚠️ Hardest to recover (late-mover)
These groups have low trust; once competitor occupies their AI mindset, intervention costs 3x+
Informal Workers17%
Narrative receptivity 17% · ~6/12 impacted
Young Adults17%
Narrative receptivity 17% · ~6/12 impacted
Service Workers25%
Narrative receptivity 25% · ~4/7 impacted
Small Biz Owners26%
Narrative receptivity 26% · ~5/9 impacted
⬇ The simulation is clear. Here's your prioritized action plan