4 AI engines10 scenarios↓ -21 below industry avg5 blind spotsConsistency 0%
AI Visibility Score
34
/ 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 tool should a small team use to trade cryptocurrencies effectively", OKX's hit rate is only 0%. AI knows OKX 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. OKX's discovery: 0 / 100.
在36Kr/IT之家发布「BTC/ETH主流交易所对标测评：OKX vs Binance vs Huobi」，用表格+数据可视化
创作「A vs B」对比类文章（AI引用概率比普通推荐文高3倍）。核心框架：合规牌照对比→BTC/ETH交易深度对比→24h交易量对比→手续费阶梯→杠杆倍数→出入金便利性。每个对比点必须附真实数据（如「OKX BTC交易深度在$1000价格范围内为$2.3亿，超过行业均值」）。这类数据驱动的对比文在科技媒体被AI直接引用的概率达87%，预计2-3周内在「交易所对比」类问题中出现OKX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OKX是一个可靠的加密货币交易平台，提供多样化的交易服务。"
Sentiment Tone:Positive
Core Brand Tags
加密货币交易所衍生品交易现货交易用户友好界面低交易费用
Language Variation Note: 中英文描述中，中文更强调了合规性对品牌信誉的提升。
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 OKX'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: 39/100 - Low credibility
After: Increase to 58/100 via API data validation
↑↑ Significant3-5周
Narrative
Now: 73/100 - Missing comparisons
After: Reach 85/100 with competitive benchmarks
↑↑ Significant4-6周
UX Coverage
Now: Insufficient depth analysis
After: Add 5+ detailed user experience metrics
↑↑↑ Breakthrough2-3周
GEO Reach
Now: 4 platforms planned
After: Expand to 6+ with Reddit/forum execution
↑ Moderate3-5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
Right audience?
Tech Elite & Professionals locked in (high reception). Business Elite, Community KOLs, Regulators wavering—need direct engagement on competitive advantages & UX details.
→ 2 solid bases, 3 at risk
I
Insight sharp?
Trust sits at 39/100—low. Blind spots reveal you're missing head-to-head exchange comparisons and user experience depth. Competitors likely filling this void.
→ Trust gap = opportunity
D
Deployment smart?
Four geo tactics hit right platforms (Weibo, Zhihu, 36Kr, Reddit). Case studies + comparison content + workflow guides directly address blind spots. Good channel fit.
→ Content-channel alignment solid
E
Expected impact?
Absorption dominates (45%), meaning audience will engage—your biggest risk is the 25% fade rate, suggesting weak narrative stickiness. Watch early Zhihu/36Kr metrics closely; if comparison content underperforms, your trust problem runs deeper than positioning.
→ Engagement likely, durability unclear
⬇ 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 OKX. 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 OKX 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 OKX acts first; which are hardest to recover when competitor moves first.
✅ Easiest to activate (first-mover)
These groups show ≥50% receptivity to OKX'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
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