product-discovery

产品探索

核心原则

• 持续探索 — 每周与用户交流，而非偶尔开展研究
• 以成果为导向 — 从要实现的成果出发，而非从要构建的解决方案出发
• 验证假设 — 在投入资源前验证高风险假设
• 共同创造 — 与客户共同构建，而不只是为客户构建
• 数据驱动 — 依据证据，而非直觉和利益相关方的意见
• 问题优先 — 在构思解决方案之前，深入了解问题领域

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硬性规则（必须遵守）

以下规则是强制要求。违反这些规则意味着此技能未能正常工作。

不要先入为主地考虑解决方案

绝不要从解决方案入手。始终先定义问题和成果。

❌ FORBIDDEN:
"We should build a search bar for the product page"
"Let's add AI recommendations"
"Users need a mobile app"

✅ REQUIRED:
"Problem: Users can't find products (40% exit rate on catalog)
Outcome: Reduce exit rate to 20%
Possible solutions:
1. Search bar with filters
2. AI-powered recommendations
3. Better category navigation
4. Visual product browsing"

基于证据做决策

绝不要在没有真实用户研究证据的情况下假设用户需求。

❌ FORBIDDEN:
- "Users probably want X" (assumption without data)
- "Our competitor has X, so we need it too" (copycat without validation)
- "The CEO thinks we should build X" (HiPPO without evidence)
- "It's obvious users need X" (intuition without validation)

✅ REQUIRED:
- "5 out of 8 interviewed users mentioned X as a pain point"
- "Analytics show 60% of users abandon at step 3"
- "Prototype test: 7/10 users completed task successfully"
- "Survey (n=500): 45% rated feature as 'must have'"

最低访谈门槛

每个细分群体的用户访谈不得少于 5 次，才能验证问题。

❌ FORBIDDEN:
- "We talked to 2 users and they loved the idea"
- "One customer requested this feature"
- "Based on a quick chat with sales..."

✅ REQUIRED:
| Segment | Interviews | Key Finding |
|---------|------------|-------------|
| Power Users | 6 | 5/6 struggle with X |
| New Users | 5 | 4/5 drop off at onboarding |
| Churned | 5 | 3/5 cited missing feature Y |

Minimum per segment: 5 interviews
Confidence increases with more interviews

可证伪的假设

每项假设都必须可测试、可证伪，并有明确的成功标准。

❌ FORBIDDEN:
- "Users will like the new design" (not falsifiable)
- "This will improve engagement" (no success criteria)
- "The feature will be useful" (vague)

✅ REQUIRED:
| Assumption | Test | Success Criteria | Result |
|------------|------|------------------|--------|
| Users will complete onboarding in new flow | Prototype test with 10 users | >70% completion | TBD |
| Users prefer visual search | A/B test | >10% lift in conversions | TBD |
| Price point is acceptable | Landing page test | >3% conversion | TBD |

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快速参考

如何选择

场景	框架/工具	输出
验证产品创意	产品机会评估	是否继续的决策
评估市场机会规模	TAM/SAM/SOM	市场规模估算
了解用户需求	用户研究（访谈、调查）	用户洞察、痛点
分析竞争情况	竞争分析	竞争格局图
发现用户动机	Jobs-to-be-Done (JTBD)	工作故事、成果
确定功能优先级	Kano 模型	功能分类
定义价值主张	价值主张画布	价值主张陈述
测试产品概念	Lean Startup / MVP	经验证的学习成果
梳理机会	机会解决方案树	排定优先级的机会

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持续探索习惯

产品三人组

探索工作由三个角色每周协作开展：

Product Manager → Defines outcomes, owns roadmap
Designer        → Explores solutions, tests usability
Engineer        → Assesses feasibility, proposes technical solutions

每周活动

## 1. Customer Interviews (Weekly)
- Schedule 3-5 interviews per week minimum
- Mix of current users, churned users, prospects
- Focus on understanding problems, not pitching solutions
- Record and share insights with team

## 2. Assumption Testing (Weekly)
- Identify riskiest assumptions about solutions
- Design quick tests (prototypes, landing pages, fake doors)
- Run experiments with real users
- Measure results against success criteria

## 3. Opportunity Mapping (Ongoing)
- Build opportunity solution tree
- Map customer needs to potential solutions
- Prioritize based on impact and feasibility
- Update as you learn

探索与交付

Discovery (What to Build)          Delivery (How to Build It)
├─ Customer interviews             ├─ Sprint planning
├─ Prototype testing               ├─ Development
├─ Assumption validation           ├─ QA testing
├─ Market research                 ├─ Deployment
└─ Opportunity assessment          └─ Post-launch monitoring

Key difference: Discovery reduces risk BEFORE committing to build

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产品机会评估

Marty Cagan 的 10 个问题

开始任何产品计划之前，请回答以下问题：

## 1. Problem Definition
**What problem are we solving?**
- Be specific and measurable
- Validate it's a real problem (not assumed)

## 2. Target Market
**For whom are we solving this problem?**
- Define specific user segments
- Size the addressable market (TAM/SAM/SOM)

## 3. Opportunity Size
**How big is the opportunity?**
- Revenue potential
- User growth potential
- Strategic value

## 4. Success Metrics
**How will we measure success?**
- Leading indicators (usage, engagement)
- Lagging indicators (revenue, retention)
- Define targets upfront

## 5. Alternative Solutions
**What alternatives exist today?**
- Direct competitors
- Indirect solutions
- Current user workarounds

## 6. Our Advantage
**Why are we best suited to solve this?**
- Unique capabilities
- Market position
- Technical advantages

## 7. Strategic Fit
**Why now? Why us?**
- Market timing
- Strategic alignment
- Resource availability

## 8. Dependencies
**What do we need to succeed?**
- Technical dependencies
- Partnership requirements
- Regulatory considerations

## 9. Risks
**What could go wrong?**
- Market risk (will anyone want it?)
- Execution risk (can we build it?)
- Monetization risk (will they pay?)

## 10. Cost of Delay
**What happens if we don't build this?**
- Competitive disadvantage
- Lost revenue
- Market opportunity window

价值与投入框架

快速确定机会的优先级：

High Value, Low Effort  → Do First (Quick Wins)
High Value, High Effort → Plan Strategically (Big Bets)
Low Value, Low Effort   → Do Later (Fill Gaps)
Low Value, High Effort  → Don't Do (Money Pit)

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探索方法

如何选择方法

## Generative Research (What problems exist?)
Use when: Starting new product area, exploring unknown space
Methods:
- Ethnographic field studies
- Contextual inquiry
- Diary studies
- Open-ended interviews

## Evaluative Research (Does our solution work?)
Use when: Testing specific solutions, validating designs
Methods:
- Usability testing
- Prototype testing
- A/B testing
- Concept testing

## Quantitative Research (How much? How many?)
Use when: Need statistical validation, measuring impact
Methods:
- Surveys
- Analytics analysis
- A/B experiments
- Market sizing

## Qualitative Research (Why? How?)
Use when: Understanding motivations, uncovering insights
Methods:
- User interviews
- Focus groups
- Customer advisory boards
- User observation

访谈最佳实践

## Preparation
- Define research goals and hypotheses
- Create interview guide (but stay flexible)
- Recruit right participants (6-8 per segment)
- Schedule 45-60 min sessions

## During Interview
✓ Ask open-ended questions ("Tell me about...")
✓ Follow up with "Why?" 5 times to get to root cause
✓ Listen more than talk (80/20 rule)
✓ Ask about past behavior, not future hypotheticals
✓ Look for workarounds and pain points
✓ Record and take notes

✗ Don't ask leading questions
✗ Don't pitch your solution
✗ Don't ask "Would you use X?" (people lie)
✗ Don't multi-task while interviewing

## Example Questions
- "Walk me through the last time you [did task]"
- "What's most frustrating about [current solution]?"
- "How are you solving this problem today?"
- "What would make [task] easier for you?"
- "Tell me more about that..."

调查最佳实践

## When to Survey
✓ Validate findings from qualitative research
✓ Measure satisfaction or sentiment at scale
✓ Prioritize features (Kano surveys)
✓ Segment users by behavior/needs

## Survey Design
- Keep it short (<10 min to complete)
- One question per screen on mobile
- Mix question types (multiple choice, scale, open-ended)
- Avoid leading or biased questions
- Test survey with 5 people before sending

## Question Types
- Multiple choice → Segmentation, categorization
- Likert scale (1-5) → Satisfaction, importance
- Open-ended → Qualitative insights
- Ranking → Prioritization
- NPS (0-10) → Loyalty measurement

## Distribution
- In-app surveys (high response, biased to engaged users)
- Email surveys (broader reach, lower response)
- Incentivize thoughtful responses ($10 gift card, early access)
- Follow up with interviews for interesting responses

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2025 年产品探索趋势

AI 驱动的研究

## AI Tools for Discovery
- **Insight synthesis** — AI analyzes interview transcripts, identifies patterns
- **Synthetic personas** — AI-generated user proxies for rapid testing
- **Market intelligence** — AI tracks competitor moves, pricing changes
- **Survey analysis** — Automated sentiment analysis, theme extraction
- **Trend detection** — AI identifies emerging market trends early

## Examples
- Crayon → Competitive intelligence automation
- Glimpse → Trend detection from web data
- Delve AI → Automated persona creation
- Attest → AI-powered survey insights
- Quantilope → Machine learning research automation

## Best Practices
✓ Use AI to scale research, not replace human insight
✓ Validate AI findings with real user conversations
✓ Combine AI analysis with qualitative depth
✗ Don't rely solely on synthetic users
✗ Don't skip talking to real customers

大规模持续探索

## Modern Approach
- Discovery is embedded in every sprint, not a phase
- Weekly user touchpoints (interviews, tests, feedback)
- Rapid experimentation (dozens of tests running)
- Fast pivots based on evidence (days, not months)

## Team Structure
- Product trios own discovery for their area
- Centralized research team supports (tools, methods)
- Customer success shares feedback loop
- Data analysts provide quantitative insights

## Cadence
- Weekly: Customer interviews, prototype tests
- Bi-weekly: Opportunity review, assumption validation
- Monthly: Market analysis, competitive review
- Quarterly: Strategic discovery (new markets, big bets)

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机会解决方案树

定义

用于梳理从成果到解决方案路径的可视化框架：

        OUTCOME (Business goal)
             |
    ┌────────┴────────┐
    │                 │
OPPORTUNITY 1    OPPORTUNITY 2
    │                 │
    ├─ Solution A     ├─ Solution C
    ├─ Solution B     └─ Solution D
    └─ Solution C

构建方法

## Step 1: Define Outcome
Start with measurable business outcome
Example: "Increase Day 30 retention from 20% to 30%"

## Step 2: Map Opportunities
Discover customer needs/pain points through research
Example: "Users don't understand core features"

## Step 3: Generate Solutions
For each opportunity, brainstorm multiple solutions
Example:
- Better onboarding tutorial
- In-app tooltips
- Interactive product tour

## Step 4: Test Assumptions
For each solution, identify riskiest assumption and test
Example: "Users will complete a 5-step tutorial"
Test: Build simple prototype, test with 10 users

## Step 5: Compare Solutions
Use evidence to choose best path forward
Build what tests validate, discard what fails

优势

✓ Visualizes multiple paths to outcome
✓ Prevents jumping to first solution
✓ Encourages broad exploration before narrowing
✓ Documents why decisions were made
✓ Keeps team aligned on priorities

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扩展参考

从 ## Integrating Discovery with Delivery 开始的详细材料已移至 reference/extended.md，以保持此技能简洁。如果任务需要其中已迁移的示例、命令目录、检查清单、平台详情或实现模板，请加载该参考文件。