Scout Agent: Deep Social Media Observation

One-sentence summary: Observe social media content across 3 phases to build authentic user personas and behavioral patterns for your target audience.

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Core Value

1. Based on Real Content, Not Imagination

vs. Asking AI Directly:

• Direct question: "What kind of coffee do Gen Z like?"
• AI answer: Generic answer based on training data, may be out of touch with reality

Scout Agent:

• Observes real discussions on Xiaohongshu/Bilibili
• Analyzes content users actually publish
• Discovers latest trends and authentic attitudes

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2. Three-Phase Workflow: Observe → Reason → Verify

Phase 1: Observation (5 rounds)

• Analyze social media content
• Extract user behavioral characteristics
• Record key discussion points

Phase 2: Reasoning (Auto-triggered)

• Triggered automatically after 5 observations
• Deep analysis and hypothesis formation
• Generate preliminary user persona

Phase 3: Verification (Continue observing)

• Continue observing with hypotheses in mind
• Refine and perfect understanding
• Output final persona

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3. Output High-Quality AI Personas

Ready for Downstream Research:

• Auto-generate Tier2 high-quality personas
• Include complete persona backgrounds
• Can be used for Interview/Discussion

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Real Case Study: Sparkling Coffee Target User Research

Task

"Observe Xiaohongshu/Weibo discussions about sparkling coffee to understand target audience characteristics"

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Phase 1: Observation (5 rounds)

Observation 1:

• Search "sparkling coffee" on Xiaohongshu
• Findings:
  • Young women posting coffee photos
  • Emphasis on "looks great" and "photogenic"
  • Mentions "not too sweet, healthier than milk tea"

Observation 2:

• Weibo topic #Sparkling Coffee
• Findings:
  • Taste discussions ("refreshing", "bubbly sensation")
  • Price feedback ("¥30 is expensive", "¥20 acceptable")
  • Purchase scenarios ("afternoon tea", "while shopping")

Observation 3-5:

• Continue observing different platforms and topics
• Extract more user characteristics

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Phase 2: Reasoning (Auto-triggered)

AI automatically reasons after 5 observations:

Initial Hypotheses:

1. Core demographic: 25-32 year old first-tier city women
2. Main motivations:
   • Not for energy boost (rarely mention "work", "late nights")
   • Rather for "special experiences" ("want to try something new")
   • Value visual presentation ("looks great" appears frequently)
3. Price sensitivity:
   • ¥20-30 acceptable
   • Over ¥30 feels expensive
4. Purchase occasions:
   • Afternoon tea time
   • Shopping/social gatherings
   • Not daily commute

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Phase 3: Verification (Continue observing)

Continue observing with hypotheses:

Verification Question 1: "Do they really value visual presentation?"

• Observation: 60%+ of content includes product photos
• Result: ✅ Confirmed

Verification Question 2: "Do they really not care about energy boost?"

• Observation: Less than 10% mention energy boost effects
• Result: ✅ Confirmed

Verification Question 3: "Is price sensitivity really ¥20-30?"

• Observation: Extensive discussions of ¥25-28 products
• Result: ✅ Confirmed, even refined to ¥25-28

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Final Output

Target Audience Persona:

Demographics:

• Age: 25-32 years old
• Gender: Primarily female (about 80%)
• Location: First-tier cities
• Income: Upper-middle income (¥8K-20K monthly)

Psychological Traits:

• Willing to try new products
• Value quality of life
• Care about product aesthetics
• Enjoy sharing on social media

Behavioral Patterns:

• Purchase occasions: Afternoon tea, shopping
• Decision factors: Aesthetics > Taste > Health
• Price acceptance: ¥25-28 optimal, hesitates over ¥30
• Repurchase intent: Will repurchase if good taste, but not essential

Pain Points:

• Concern: "What if the bubbles are too strong?"
• Concern: "What if it doesn't taste like coffee?"
• Desire: "Hope the packaging looks good"

Can Directly Be Used For:

• Follow-up Interview deep dives
• Discussion group interactions
• Auto-generated Tier2 AI personas

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4 Use Cases for Scout Agent

Use Case 1: Understanding Users Before Entering New Market

Need:

• Want to enter a new market
• Don't understand target users
• Need to quickly build understanding

Scout Value:

• Quickly understand authentic user characteristics
• Discover market opportunities
• Avoid decision-making based on imagination

Example:

"Want to make camping gear, but don't understand camping enthusiasts"

Scout Agent observes 10 rounds → discovers 3 user groups:

• Premium campers (pursue quality and ambiance)
• Value seekers (focus on practicality and price)
• Family campers (prioritize safety and convenience)

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Use Case 2: Discover Emerging Consumption Trends

Need:

• Want to know latest industry trends
• Unsatisfied with industry reports alone
• Want to hear authentic user voices

Scout Value:

• Based on real social content
• Discover details missing in reports
• Real-time observation of latest changes

Example:

"What are the new trends in healthy food market for 2026?"

Scout Agent observes → discovers:

• "Functional foods" discussion surging (sleep aid, liver support, anti-anxiety)
• "Emotional value" becoming new selling point ("comfort snacks")
• Clear demand for "personalization"

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Use Case 3: Brand Positioning Research

Need:

• Want to reposition brand
• Don't know how users perceive brand
• Need to understand authentic user attitude

Scout Value:

• Observe organic user discussions
• Discover brand perception issues
• Find positioning opportunities

Example:

"Observe how users discuss our brand"

Scout Agent discovers:

• Young moms: perceive as "reliable but boring"
• Gen Z: perceive as "healthy but outdated"
• Positioning opportunity: "Health × Aesthetics"

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Use Case 4: Build High-Quality AI Personas

Need:

• Need to run Interview/Discussion next
• Need high-quality AI personas
• Don't want to use low-quality hastily-generated personas

Scout Value:

• Auto-generate Tier2 personas
• Based on authentic user characteristics
• Can directly use for follow-up research

Example:

Scout observes 10 rounds → auto-generates 3 Tier2 personas → directly use for Discussion to test product concept → saves persona-building time, increases research quality

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Scout Agent vs Other Methods

Key Differences:

• Scout Agent is not just data collection, it's understanding users
• Output is not just statistics, it's insights
• Can be directly used for downstream research, not isolated

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FAQ

Q1: What platforms does Scout Agent observe?

Supported platforms:

• Xiaohongshu (Little Red Book)
• Weibo
• Bilibili
• Douyin (TikTok China)
• X (Twitter)
• Reddit
• Other public social platforms

Selection guidance:

• Young female consumers → Xiaohongshu
• Tech products → Weibo/Bilibili/X
• Gen Z → Douyin/Bilibili
• International market → X/Reddit

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Q2: How many observation rounds are needed?

Minimum 5 rounds:

• First 5 rounds: Initial observation
• After round 5: Auto-trigger reasoning
• Continue: Verification and refinement

Recommended 10-15 rounds:

• More comprehensive understanding
• More accurate persona
• Discover more details

Avoid too many:

• Beyond 20 rounds shows diminishing returns
• Wastes time and cost

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Q3: Can I specify observing specific user groups?

Yes:

• Set filtering conditions
• Example: "25-35 year old women discussing healthy snacks"
• Scout will focus on this group

Recommendation:

• Don't filter too narrowly (insufficient content)
• Don't filter too broadly (too much noise)

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Q4: How good are Scout-generated personas?

Quality Level:

• Auto-generate Tier2 personas
• Consistency ~85%
• Close to human baseline 81%

vs. Hastily-generated:

• Hastily-generated (Tier0): Low consistency, shallow feedback
• Scout-generated (Tier2): High consistency, authentic feedback

Recommendation:

• Use Scout-generated personas for important research
• Can use hastily-generated for quick validation

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Q5: How reliable are Scout's discovered trends?

Reliability:

• ✅ Based on authentic user content
• ✅ Multi-round cross-verification
• ⚠️ Still qualitative research, not statistical significance

Applicable Scenarios:

• ✅ Quickly identify trend direction
• ✅ Understand user attitudes and motivations
• ❌ Cannot replace large-scale quantitative research

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Q6: Can I observe competitor users?

Yes:

• Observe users discussing competitors
• Understand competitor user characteristics
• Discover differentiation opportunities

Example:

"Observe users discussing Starbucks"

Scout discovers:

• Frequent words: "convenient", "stable", "nothing special"
• Opportunity: Users think Starbucks "lacks surprise"
• Positioning direction: "Coffee experience with character"

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Practical Recommendations

1. Scout First, Then Interview

Recommended Process:

1. Scout observes 10 rounds → generate user persona
2. Select AI personas based on persona
3. Interview deep dives for verification

Value:

• Scout ensures right direction
• Interview dives deeper based on authentic users
• Avoid "interviewing thin air"

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2. Don't Over-rely on Single Platform

Recommendation:

• Observe 2-3 platforms
• Cross-verify findings
• Avoid platform bias

Example:

• Xiaohongshu + Weibo + Bilibili
• See consistent trends → high credibility
• See contradictions → needs further verification

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3. Focus on "Why" Not "What"

Common Mistake:

• Only record "users like A"
• Don't dig deeper "why they like A"

Correct Approach:

• Ask "why" when observing
• Analyze underlying motivations
• Output insights not phenomena

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4. Re-observe Periodically

Markets Change:

• Re-run Scout 3-6 months later
• Discover trend changes
• Adjust strategy timely

Value:

• Avoid decisions based on outdated knowledge
• Capture emerging opportunities

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Summary

Scout Agent Core Value:

1. Authenticity: Based on real social content, not imagination
2. Depth: Not just statistics, but understanding users
3. Usability: Auto-generate high-quality AI personas for direct downstream use

Applicable Scenarios:

• Understanding users before entering new market
• Discovering emerging consumption trends
• Brand positioning research
• Building high-quality AI personas

Best Practices:

• Recommended 10-15 observation rounds
• Observe 2-3 platforms for cross-verification
• Scout first, then Interview
• Re-observe periodically (every 3-6 months)

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Document Version: v2.0 | 2026-01-15 | User-Centric Perspective