test software · Claude / DeepSeek / DOUBAO / GPT / Kimi
5 AI engines10 scenarios↓ -39 below industry avg8 blind spotsConsistency 0%
AI Visibility Score
16
/ 100
Industry avg 55
8
Blind Spots
2
Covered
0%
Consistency
⚠️
Recommendation blind spot — AI picks competitors when users make decisions
For queries like "what tool should a small business use for market research and 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.
创建一个5部分的微博长文系列，每部分针对一个对比维度（功能覆盖、数据类型、团队协作、自动化程度、成本模型），明确说明TestBrand如何兼顾「软件质量测试」和「消费者反馈调研」两个场景。微博长文在AI爬虫中的权重正在上升，特别是带有「A不等于B」的对比框架会被AI在区分问题中优先引用。每部分配一张对比表格（如表格标题「TestBrand vs 单一功能工具的功能矩阵」），让AI可以直接提取数据。预计1-2周见效，因为微博内容生命周期短但爬取频率高。
Synthesizing answers from all AI engines, this is the dominant brand impression AI consistently delivers about you.
Dominant AI Impression
"TestBrand被视为一个专注于市场调研和用户测试的平台，帮助企业理解消费者反馈。"
Sentiment Tone:Neutral
Core Brand Tags
市场调研用户体验测试消费者反馈软件测试服务自动化测试解决方案
Language Variation Note: 中英文描述中，中文更强调用户体验测试，而英文则更侧重市场调研。
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. 27 are wavering — the key battleground. Tech Elite & Regulators 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 8 more supporters vs waiting (31% 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 46%
Audience understood and accepted the narrative
💨 Fades without impact 26%
Content reached audience but left no impression
❌ Systematic disengagement 12%
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: 41/100 - Below average
After: ↑ to 58/100 via G2/Capterra reviews
↑↑ Significant4-6周
Narrative
Now: 74/100 - Good alignment
After: ↑ to 85/100 with service clarity
↑ Moderate3-5周
Brand Awareness
Now: Low - Blind spot identified
After: ↑ via multi-platform content
↑↑↑ Breakthrough4-6周
Service Visibility
Now: Lacks specifics - Blind spot
After: ↑ detailed use-case comparisons
↑↑ Significant2-3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
Right audience?
Tech Elite, Regulators, Professionals are locked in. Business Elite, Community KOLs, Civil Society need conversion work.
→ 3 solid + 3 fence-sitters
I
Insight gap?
Audiences don't know what TestBrand actually does or who it's for. Low brand awareness + vague positioning are killing trust.
→ Narrative clarity first
D
Distribution play?
Hit Xiaohongshu, Zhihu, Weibo with concrete comparisons and case studies. Then amplify on G2/Capterra for credibility.
→ Specificity wins waverers
E
What actually happens?
Nearly half your audience will absorb your message—that's the win. Biggest risk: 26% tune out completely due to weak positioning. Watch engagement depth on comparison posts; if clicks don't convert to shares, storytelling needs tightening.
→ Strong absorption, weak recall
⬇ 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: 27 wavering users are the battleground. Execute GEO now: convert 11 of them into supporters. Let competitor move first: lose 24, ending up with 8 fewer supporters (31% gap). Same users — different outcomes because of sequence alone.
⚡ First-Mover Path · You Act First
Now: 27 wavering
27 people undecided
↓
After Rec ①②
Comparison content published; AI starts citing TestBrand. 6 shift from wavering to accepting
↓
All recs live
Scene coverage expands fully. 5 more convert. Total: 26 supporting, 16 still neutral
Final supporters: 26
🚨 Late-Mover Path · Competitor Establishes AI Narrative First
Now: 27 wavering
27 wavering — same starting point
↓
After competitor AI citation
Competitor cited frequently in TestBrand comparison queries. 18 wavering users' beliefs are now locked against us
↓
After our GEO execution
Overwriting established beliefs costs 3x more. Even executing fully, only 3 recovered. Final: 18 supporting — 8 fewer than first-mover
Final supporters: 18 (-8 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
Regulators79%
Narrative receptivity 79% · ~4/4 impacted
Professionals79%
Narrative receptivity 79% · ~6/6 impacted
Business Elite71%
Narrative receptivity 71% · ~3/3 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