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product-discovery

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Chinese

Product Discovery

产品探索

Run structured discovery to identify high-value opportunities and de-risk product bets.
开展结构化探索，识别高价值机会并降低产品投入的风险。

When To Use

使用场景

Use this skill for:
  • Opportunity Solution Tree facilitation
  • Assumption mapping and test planning
  • Problem validation interviews and evidence synthesis
  • Solution validation with prototypes/experiments
  • Discovery sprint planning and outputs
本技能适用于：
  • Opportunity Solution Tree 引导
  • 假设梳理与测试规划
  • 问题验证访谈与证据整合
  • 借助原型/实验验证解决方案
  • 探索冲刺规划与产出物整理

Core Discovery Workflow

核心探索流程

  1. Define desired outcome
  • Set one measurable outcome to improve.
  • Establish baseline and target horizon.
  1. Build Opportunity Solution Tree (OST)
  • Outcome -> opportunities -> solution ideas -> experiments
  • Keep opportunities grounded in user evidence, not internal opinions.
  1. Map assumptions
  • Identify desirability, viability, feasibility, and usability assumptions.
  • Score assumptions by risk and certainty.
Use:
bash
python3 scripts/assumption_mapper.py assumptions.csv
  1. Validate the problem
  • Conduct interviews and behavior analysis.
  • Confirm frequency, severity, and willingness to solve.
  • Reject weak opportunities early.
  1. Validate the solution
  • Prototype before building.
  • Run concept, usability, and value tests.
  • Measure behavior, not only stated preference.
  1. Plan discovery sprint
  • 1-2 week cycle with explicit hypotheses
  • Daily evidence reviews
  • End with decision: proceed, pivot, or stop
  1. 定义期望成果
  • 设置一个可衡量的待优化成果指标。
  • 确立基准值与目标周期。
  1. 构建 Opportunity Solution Tree（OST）
  • 成果 → 机会 → 解决方案构想 → 实验
  • 确保机会基于用户证据，而非内部主观意见。
  1. 梳理假设
  • 识别吸引力、可行性、可落地性与易用性假设。
  • 根据风险与确定性对假设打分。
使用：
bash
python3 scripts/assumption_mapper.py assumptions.csv
  1. 验证问题
  • 开展访谈与行为分析。
  • 确认问题发生频率、严重程度及用户解决意愿。
  • 尽早剔除价值较低的机会。
  1. 验证解决方案
  • 先制作原型再开发。
  • 开展概念测试、易用性测试与价值测试。
  • 衡量用户行为，而非仅依赖用户自述偏好。
  1. 规划探索冲刺
  • 1-2周周期，明确假设前提
  • 每日进行证据复盘
  • 周期结束时做出决策：推进、调整或终止

Opportunity Solution Tree (Teresa Torres)

Opportunity Solution Tree（Teresa Torres 提出）

Structure:
  • Outcome: metric you want to move
  • Opportunities: unmet customer needs/pains
  • Solutions: candidate interventions
  • Experiments: fastest learning actions
Quality checks:
  • At least 3 distinct opportunities before converging.
  • At least 2 experiments per top opportunity.
  • Tie every branch to evidence source.
结构：
  • 成果：你希望提升的指标
  • 机会：未被满足的用户需求/痛点
  • 解决方案：候选干预措施
  • 实验：最快获取认知的行动
质量检查：
  • 收敛前至少要有3个不同的机会。
  • 每个核心机会至少对应2个实验。
  • 每个分支都需关联证据来源。

Assumption Mapping

假设梳理

Assumption categories:
  • Desirability: users want this
  • Viability: business value exists
  • Feasibility: team can build/operate it
  • Usability: users can successfully use it
Prioritization rule:
  • High risk + low certainty assumptions are tested first.
假设类别：
  • 吸引力：用户需要该产品/功能
  • 可行性：具备商业价值
  • 可落地性：团队能够开发/运营
  • 易用性：用户可成功使用
优先级规则：
  • 先测试高风险、低确定性的假设。

Problem Validation Techniques

问题验证技巧

  • Problem interviews focused on current behavior
  • Journey friction mapping
  • Support ticket and sales-call synthesis
  • Behavioral analytics triangulation
Evidence threshold examples:
  • Same pain repeated across multiple target users
  • Observable workaround behavior
  • Measurable cost of current pain
  • 聚焦用户当前行为的问题访谈
  • 旅程摩擦梳理
  • 支持工单与销售通话内容整合
  • 行为分析交叉验证
证据阈值示例：
  • 多个目标用户提及相同痛点
  • 可观察到用户的 workaround 行为
  • 当前痛点造成可衡量的成本损失

Solution Validation Techniques

解决方案验证技巧

  • Concept tests (value proposition comprehension)
  • Prototype usability tests (task success/time-to-complete)
  • Fake door or concierge tests (demand signal)
  • Limited beta cohorts (retention/activation signals)
  • 概念测试（价值主张理解度）
  • 原型易用性测试（任务成功率/完成时间）
  • 假门测试或礼宾测试（需求信号）
  • 限定版 beta 群组（留存/激活信号）

Discovery Sprint Planning

探索冲刺规划

Suggested 10-day structure:
  • Day 1-2: Outcome + opportunity framing
  • Day 3-4: Assumption mapping + test design
  • Day 5-7: Problem and solution tests
  • Day 8-9: Evidence synthesis + decision options
  • Day 10: Stakeholder decision review
建议10天结构：
  • 第1-2天：成果与机会框架搭建
  • 第3-4天：假设梳理与测试设计
  • 第5-7天：问题与解决方案测试
  • 第8-9天：证据整合与决策选项制定
  • 第10天：利益相关者决策评审

Tooling

工具

scripts/assumption_mapper.py

scripts/assumption_mapper.py

CLI utility that:
  • reads assumptions from CSV or inline input
  • scores risk/certainty priority
  • emits prioritized test plan with suggested test types
See 
references/discovery-frameworks.md
for framework details.
CLI工具功能：
  • 从CSV或输入中读取假设
  • 按风险/确定性优先级打分
  • 生成包含建议测试类型的优先级测试计划
详见 
references/discovery-frameworks.md
获取框架细节。