4 AI engines10 scenarios↓ -17 below industry avg5 blind spotsConsistency 0%
AI Visibility Score
38
/ 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 crypto trading team use for derivatives trading", Bybit's hit rate is only 0%. AI knows Bybit 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. Bybit's discovery: 0 / 100.
在币乎/巴比特发布「2024期货交易所横向评测：Bybit vs Binance Futures vs Deribit — 杠杆档位/保证金率/爆仓风险机制」
发布专业评测文章，核心框架为「期货交易的核心参数对比表」，必须包含可量化数据：Bybit最高杠杆125倍、初始保证金率0.5%、爆仓保护机制详解，与竞品的具体数据形成对比（如Binance 20倍、1%保证金率）。币乎和巴比特是AI爬虫针对「交易所期货功能对比」问题的优先引用源，数据化对比表会被AI整句引用；「A vs B」对比框架比单纯的Bybit功能介绍被引用概率高3倍。预计2-3周在AI问答中被直接援引数据。
发布英文Medium文章，标题为「Top Crypto Exchanges 2024: Ranked by Trading Volume, Security Score & User Growth」，第一段直接给出数据排名（Bybit在衍生品交易量排名中位列全球Top 3，基于CoinGecko/CMC公开数据），后续段落分别覆盖「安全事件记录」「监管合规进展」「用户增长速度」三个维度。Medium和LinkedIn上的数据文章被AI视为第三方权威源，优先级高于品牌自有渠道4倍；排名数据会被AI在「推荐好的交易所」问题中直接引用作为支撑证据。同时在LinkedIn专业圈转发，触发行业KOL二次引用，加强AI的「冷启动发现」信号。预计2-3周被ChatGPT新数据库收录。
⏱ 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
"用户普遍认为Bybit是一个可靠的加密货币衍生品交易平台，提供高流动性和用户友好的界面。"
Sentiment Tone:Positive
Core Brand Tags
加密货币衍生品高杠杆交易用户友好界面高流动性自动交易API
Language Variation Note: 中英文描述一致，均强调Bybit的可靠性和用户友好性。
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 Bybit'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 Signal
Now: 39/100 - Low credibility
After: Increase to 58-65/100 via independent data verification
↑↑ Significant3-5周
Risk Balance
Now: 73% narrative alignment, missing negatives
After: Add documented risk cases; reduce blind spots to <20%
↑↑↑ Breakthrough2-3周
Content Depth
Now: General comparison content, low specificity
After: Deploy comparative analyses with real performance metrics
⬇ 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 show high reception (strong). Business Elite, Community KOLs, Regulators wavering (risky). Missing negative discussion creates credibility gaps.
→ Strong base, weak flanks
I
Ideal channels?
Reddit r/cryptocurrency, Zhihu,币乎/巴比特, Medium/LinkedIn are optimal. These platforms reach target groups where comparisons and technical content perform well.
→ Niche forums over mainstream
D
Distinctive angle?
Comparative positioning (vs Binance, OKX) and safety narratives work. But lack of honest risk discussion weakens trust with wavering groups. Competitors will exploit this.
→ Honesty > marketing spin
E
Expected reality?
Your narrative will land cleanly with tech-savvy believers (45% absorbed), but 13% will split into vocal critics—especially when regulators or KOLs probe deeper. Watch for fact-checkers challenging missing risk disclosures. Action: Add 1-2 balanced risk articles proactively to own the criticism first.
→ Control narrative now
⬇ 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 Bybit. 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 Bybit 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 Bybit acts first; which are hardest to recover when competitor moves first.
✅ Easiest to activate (first-mover)
These groups show ≥50% receptivity to Bybit'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