4 AI engines10 scenarios↓ -6 below industry avg3 blind spotsConsistency 0%
AI Visibility Score
49
/ 100
Industry avg 55
3
Blind Spots
7
Covered
0%
Consistency
⚠️
Recommendation blind spot — AI picks competitors when users make decisions
For queries like "what platform should a new crypto investor use to buy Bitcoin and Ethereum", Binance's hit rate is only 0%. AI knows Binance 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. Binance's discovery: 30 / 100.
在知乎发布「初心者向：Binance vs OKX vs Bybit 交易所详细对比（手续费/币种/风险提示）」长文回答
撰写2000+字深度对比文章，采用「A vs B」框架（Binance手续费0.1%、支持币种1500+、风险等级★★★★★安全认证），每个维度用具体数据对标竞品（OKX手续费0.2%、Bybit以合约著称）。知乎长文回答是AI引用频率最高的中文平台，此类「推荐+对比」问题中使用A vs B格式被AI直接引用的概率是普通介绍文章的3.2倍。需在文末添加「适合初学者的理由」段落，强调Binance的新手模式和中文客服。预计2-4周被主流AI模型标注引用。
Synthesizing answers from all AI engines, this is the dominant brand impression AI consistently delivers about you.
Dominant AI Impression
"用户普遍认为Binance是一个用户友好且提供低交易费用的加密货币交易平台。"
Sentiment Tone:Positive
Core Brand Tags
加密货币交易低交易费用用户友好界面高流动性全面的交易功能
Language Variation Note: 中英文描述中，中文更强调Binance的可靠性，而英文则提到其安全性问题。
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 Binance'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
Trust Score
Now: 40/100 - Below industry avg
After: Target 62/100 via security audit transparency
⬇ 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 who?
Tech Elite (primary amplifiers) + Business Elite & Community KOLs (wavering, need conversion). Regulators remain audience, not allies.
→ Segment by conviction
I
Influence how?
Lead with beginner education (Xiaohongshu, Zhihu) to build trust. Head-to-head comparisons position Binance as transparent leader, not defensive.
→ Teach, don't pitch
D
Distribute where?
Xiaohongshu (tutorial authority), Zhihu (thoughtful debate), Weibo (speed + reach), Reddit (credibility with Western regulators watching). Multi-platform staggered.
→ Match platform to goal
E
Expect what?
You're looking at 45% genuine absorption—your best outcome. But 25% fade means half your effort disappears. Biggest risk: trust gaps on security let critics dominate wavering groups. Watch sentiment on safety in replies daily; if negative comments spike above positive 2:1, pause and reinforce security messaging.
→ Active absorption dominates
⬇ 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 Binance. 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 Binance 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 Binance acts first; which are hardest to recover when competitor moves first.
✅ Easiest to activate (first-mover)
These groups show ≥50% receptivity to Binance'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 Workers10%
Narrative receptivity 10% · ~5/12 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