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Opportunity Solution Tree (OST)

机会解决方案树（OST）

A visual framework for structuring continuous product discovery. Connects a desired outcome to customer opportunities, possible solutions, and experiments to validate them.
这是一个用于结构化持续产品发现工作的可视化框架。将预期成果与客户机会、可行解决方案，以及用于验证方案的实验关联起来。

Domain Context

领域背景

The Opportunity Solution Tree (Teresa Torres, Continuous Discovery Habits) is the backbone of modern product discovery. It prevents teams from jumping to solutions by forcing them to first map the opportunity space.
Structure (4 levels):
  1. Desired Outcome (top) — The measurable business or product outcome the team is pursuing. Should be a single, clear metric (e.g., "increase 7-day retention to 40%"). This comes from your OKRs or product strategy.
  2. Opportunities (second level) — Customer needs, pain points, or desires discovered through research. These are problems worth solving — not features. Frame them from the customer's perspective: "I struggle to..." or "I wish I could..." Prioritize using Opportunity Score: Importance × (1 − Satisfaction) (Dan Olsen, The Lean Product Playbook). Normalize Importance and Satisfaction to 0–1.
  3. Solutions (third level) — Possible ways to address each opportunity. Generate multiple solutions per opportunity — don't commit to the first idea. The Product Trio (PM + Designer + Engineer) should ideate together. "Best ideas often come from engineers."
  4. Experiments (bottom) — Fast, cheap tests to validate whether a solution actually addresses the opportunity. Use assumption testing (Value, Usability, Viability, Feasibility risks). Prefer experiments with "skin-in-the-game" (Alberto Savoia) over opinion-based validation.
Key principles:
  • One outcome at a time. Don't try to solve everything. Focus the tree on a single desired outcome.
  • Opportunities, not features. "Never allow customers to design solutions. Prioritize opportunities (problems), not features."
  • Compare and contrast. Always generate at least 3 solutions per opportunity before choosing. Avoid the "first idea" trap.
  • Discovery is not linear. Loop back if experiments fail. Kill solutions that don't validate. Explore new branches.
  • Continuous, not periodic. Update the tree weekly as you learn from interviews, analytics, and experiments.
机会解决方案树（由Teresa Torres在《Continuous Discovery Habits》中提出）是现代产品发现工作的核心框架。它通过要求团队先梳理机会空间，避免团队直接跳到解决方案阶段。
结构（4个层级）：
  1. 预期成果（顶层）——团队追求的可衡量业务或产品成果。应为单一、清晰的指标（例如：“将7日留存率提升至40%”）。该指标来源于你的OKRs或产品战略。
  2. 机会（第二层）——通过研究发现的客户需求、痛点或期望。这些是值得解决的问题——而非功能特性。要从客户视角表述：“我难以……”或“我希望能……”。使用机会评分法进行优先级排序：重要性 × (1 − 满意度)（来自Dan Olsen的《The Lean Product Playbook》）。将重要性和满意度标准化为0–1区间的数值。
  3. 解决方案（第三层）——解决每个机会的可行方式。针对每个机会生成多个解决方案——不要局限于第一个想法。Product Trio（产品经理+设计师+工程师）应共同构思方案。“最佳想法往往来自工程师。”
  4. 实验（底层）——快速、低成本的测试，用于验证解决方案是否真的能解决对应的机会。采用假设测试法（覆盖价值、可用性、可行性、生存风险）。优先选择带有“切身投入”的实验（Alberto Savoia提出），而非基于观点的验证。
核心原则：
  • 一次聚焦一个成果：不要试图解决所有问题，让整个树聚焦于单一预期成果。
  • 聚焦机会，而非功能：“永远不要让客户设计解决方案。优先关注机会（问题），而非功能特性。”
  • 多方案对比：在选择方案前，针对每个机会至少生成3个解决方案。避免陷入“第一个想法”的陷阱。
  • 发现工作并非线性：如果实验失败，就回到上一步。放弃无法验证的解决方案。探索新的分支。
  • 持续进行，而非周期性开展：每周根据用户访谈、数据分析和实验结果更新树状图。

Instructions

操作说明

You are helping a product team build an Opportunity Solution Tree for $ARGUMENTS.
你将协助产品团队为**$ARGUMENTS**构建机会解决方案树。

Input Requirements

输入要求

  • A desired outcome or business metric to improve
  • Customer research data (interviews, surveys, analytics, feedback)
  • Optionally: existing opportunities or solution ideas to organize
  • 待提升的预期成果或业务指标
  • 客户研究数据（访谈、调研、分析、反馈）
  • 可选：已有的机会或解决方案构想，用于整理

Process

流程步骤

  1. Define the desired outcome — Confirm or help articulate a single, measurable outcome at the top of the tree.
  2. Map opportunities — From provided research, identify 3-7 customer opportunities (needs/pains). Group related opportunities. Frame each from the customer's perspective.
  3. Prioritize opportunities — Use Opportunity Score or qualitative assessment to rank. Focus on the top 2-3.
  4. Generate solutions — For each prioritized opportunity, brainstorm 3+ solutions from PM, Designer, and Engineer perspectives.
  5. Design experiments — For the most promising solutions, suggest 1-2 fast experiments. Specify: hypothesis, method, metric, success threshold.
  6. Visualize the tree — Present the full OST in a clear hierarchical format.
Think step by step. Save as markdown if substantial.
  1. 定义预期成果——确认或协助明确树状图顶层的单一、可衡量成果。
  2. 梳理机会——从提供的研究数据中，识别3-7个客户机会（需求/痛点）。将相关机会分组。从客户视角表述每个机会。
  3. 排序机会优先级——使用机会评分法或定性评估对机会排序。聚焦前2-3个高优先级机会。
  4. 生成解决方案——针对每个高优先级机会，从产品经理、设计师和工程师的角度头脑风暴3个以上解决方案。
  5. 设计实验——针对最具前景的解决方案，建议1-2个快速实验。明确：假设、方法、指标、成功阈值。
  6. 可视化树状图——以清晰的层级格式呈现完整的OST。
逐步思考。如果内容较多，保存为markdown格式。

Further Reading

延伸阅读