5 AI engines10 scenarios↓ -30 below industry avg5 blind spotsConsistency 0%
AI Visibility Score
25
/ 100
Industry avg 55
5
Blind Spots
5
Covered
0%
Consistency
⚠️
Recommendation blind spot — AI picks competitors when users make decisions
For queries like "what tools should a marketing team use to gather consumer feedback", TestBrand's hit rate is only 0%. AI knows TestBrand 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. TestBrand's discovery: 0 / 100.
在官网FAQ页面直接嵌入对比式问答，例如「TestBrand和Qualtrics相比有什么优势？」的回答需包含具体差异点和用户数据（如「被X家财富500企业使用」「反馈周期缩短50%」）。FAQ页面是AI爬虫最高频访问的网站页面类型，被直接引用的概率是普通博客的5倍以上。同时在该页面中使用「consumer feedback tool」「market research platform」等高频搜索词，触发AI的冷启动推荐机制。预计4-6周后用户搜索「好用的反馈收集工具」时被AI作为首选推荐提及。
⏱ 6
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
"用户认为TestBrand是一个专注于消费者洞察和市场调研的平台，提供多种测试方法。"
Sentiment Tone:Positive
Core Brand Tags
消费者洞察市场调研品牌测试定制调查软件测试
Language Variation Note: 中英文描述中，中文强调了数据质量和可靠性，而英文则更侧重于功能和市场调研的多样性。
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. 31 are wavering — the key battleground. Tech Elite & Professionals show the highest receptivity to TestBrand'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 9 more supporters vs waiting (38% 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 13%
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
AI Trust
Now: 37/100
After: Boost to 52-58/100 via case studies & data
↑↑ Significant4-6周
Brand Awareness
Now: Low recognition
After: Multi-platform presence across Xiaohongshu
↑↑↑ Breakthrough3-5周
User Reviews
Now: Limited feedback
After: 30+ authentic reviews from Zhihu & tech media
↑↑ Significant4-6周
Narrative Strength
Now: 72/100
After: Reach 85+/100 with comparison content
↑ Moderate2-3周
⬇ Who exactly are these improvements for? → See ② Audience Funnel
⬇ 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
Reach?
Tech Elite + Professionals (amplifiers) will drive adoption; Business Elite, Community KOLs, Regulators wavering. Low brand awareness is the ceiling.
→ Amplifiers exist, reach capped
I
Influence?
37/100 trust is below threshold for persuasion. Lack of user reviews + narrow awareness undermine credibility messaging across all channels.
→ Trust deficit blocks influence
D
Distribution?
Xiaohongshu (lifestyle), Zhihu (expert), 36kr/Huxiu (industry), + FAQ create multi-layer proof. Targets where wavering groups actively listen.
→ Stack proof across 4 layers
E
Expected outcome?
Your realistic win: 45% of exposed audience absorbs narrative and considers you—that's your success floor. The real risk is 25% tune-out. Watch whether wavering groups (Business Elite, Regulators) move from neutral to positive after FAQ/comparisons drop. If they stay silent, distribution failed.
→ Win: 45% adoption; watch: elite silence
⬇ 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: 31 wavering users are the battleground. Execute GEO now: convert 13 of them into supporters. Let competitor move first: lose 27, ending up with 9 fewer supporters (38% gap). Same users — different outcomes because of sequence alone.
⚡ First-Mover Path · You Act First
Now: 31 wavering
31 people undecided
↓
After Rec ①②
Comparison content published; AI starts citing TestBrand. 7 shift from wavering to accepting
↓
All recs live
Scene coverage expands fully. 6 more convert. Total: 24 supporting, 18 still neutral
Final supporters: 24
🚨 Late-Mover Path · Competitor Establishes AI Narrative First
Now: 31 wavering
31 wavering — same starting point
↓
After competitor AI citation
Competitor cited frequently in TestBrand comparison queries. 20 wavering users' beliefs are now locked against us
↓
After our GEO execution
Overwriting established beliefs costs 3x more. Even executing fully, only 4 recovered. Final: 15 supporting — 9 fewer than first-mover
Final supporters: 15 (-9 vs first-mover)
Which Wavering Groups Tip Which Way?
Key group analysis — which groups are easiest to activate when TestBrand acts first; which are hardest to recover when competitor moves first.
✅ Easiest to activate (first-mover)
These groups show ≥50% receptivity to TestBrand's narrative — the right GEO content tips them
Tech Elite79%
Narrative receptivity 79% · ~5/5 impacted
Professionals79%
Narrative receptivity 79% · ~6/6 impacted
Business Elite71%
Narrative receptivity 71% · ~3/3 impacted
Community KOLs70%
Narrative receptivity 70% · ~2/2 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
Service Workers25%
Narrative receptivity 25% · ~4/7 impacted
Young Adults25%
Narrative receptivity 25% · ~6/12 impacted
Small Biz Owners26%
Narrative receptivity 26% · ~5/9 impacted
⬇ The simulation is clear. Here's your prioritized action plan