user-research
Compare original and translation side by side
🇺🇸
Original
English🇨🇳
Translation
ChineseUser Research
用户研究
Research Methodologies
研究方法论
User Interviews
用户访谈
- One-on-One Interviews: Deep, qualitative conversations with individual users
- Semi-Structured: Use a guide but allow flexibility to explore unexpected topics
- Open-Ended Questions: Ask questions that encourage detailed responses
- Active Listening: Listen more than you speak, probe for deeper understanding
- Recording: Record interviews (with permission) for later analysis
- Interview Length: 30-60 minutes is optimal for maintaining engagement
- 一对一访谈：与单个用户进行深入的定性对话
- 半结构化访谈：使用访谈指南，但允许灵活探索意外话题
- 开放式问题：提出能激发详细回答的问题
- 积极倾听：多听少说，深入挖掘以获得更深刻的理解
- 录音：在获得许可的情况下录制访谈，以便后续分析
- 访谈时长：30-60分钟是保持用户参与度的最佳时长
Surveys
问卷调查
- Survey Design: Keep surveys short and focused (5-10 minutes max)
- Question Types: Use a mix of multiple choice, rating scales, and open-ended questions
- Avoid Bias: Use neutral language and avoid leading questions
- Pilot Testing: Test surveys with a small group before full distribution
- Distribution Channels: Email, in-app, social media, or dedicated survey platforms
- Response Rates: Expect 10-20% response rate for email surveys
- 问卷设计：保持问卷简短且聚焦（最多5-10分钟）
- 问题类型：混合使用选择题、评分量表和开放式问题
- 避免偏见：使用中立语言，避免诱导性问题
- 试点测试：在全面分发前，先用小群体测试问卷
- 分发渠道：电子邮件、应用内、社交媒体或专用问卷平台
- 回复率：电子邮件问卷的预期回复率为10-20%
Usability Testing
可用性测试
- Moderated Testing: Researcher guides participants through tasks
- Unmoderated Testing: Participants complete tasks independently
- Think-Aloud Protocol: Ask participants to verbalize their thoughts
- Task Design: Create realistic tasks that represent actual user goals
- Metrics: Track task completion rate, time on task, error rate, and satisfaction
- Sample Size: 5 users reveal 80% of usability issues
- 有主持测试：由研究人员引导参与者完成任务
- 无主持测试：参与者独立完成任务
- 出声思考法：要求参与者说出自己的想法
- 任务设计：创建代表真实用户目标的现实任务
- 指标：跟踪任务完成率、任务耗时、错误率和满意度
- 样本量：5名用户即可发现80%的可用性问题
Card Sorting
卡片分类
- Open Card Sort: Users create their own categories
- Closed Card Sort: Users sort into predefined categories
- Hybrid Approach: Combine both methods for comprehensive insights
- Tools: Use online tools for remote card sorting sessions
- Analysis: Look for patterns and consensus in how users organize information
- Application: Inform information architecture and navigation design
- 开放式卡片分类：用户自行创建分类
- 封闭式卡片分类：用户将卡片归入预定义分类
- 混合方法：结合两种方法以获得全面洞察
- 工具：使用在线工具进行远程卡片分类会话
- 分析：寻找用户组织信息的模式和共识
- 应用：为信息架构和导航设计提供依据
Persona Creation
用户角色创建
Persona Development
用户角色开发
- Research-Based: Personas should be based on real research data
- Demographics: Age, gender, location, education, occupation
- Psychographics: Goals, motivations, frustrations, attitudes
- Behaviors: How they interact with products, technology preferences
- Quotes: Include real quotes from interviews to bring personas to life
- Scenarios: Describe typical use cases and contexts
- 基于研究：用户角色应基于真实研究数据
- 人口统计信息：年龄、性别、地域、教育背景、职业
- 心理统计信息：目标、动机、痛点、态度
- 行为特征：与产品的互动方式、技术偏好
- 引用语录：加入访谈中的真实语录，让用户角色更生动
- 使用场景：描述典型的使用案例和情境
User Journey Mapping
用户旅程地图
- Touchpoints: List all interactions across channels and devices
- Emotions: Map user emotions at each touchpoint
- Pain Points: Identify areas of frustration or difficulty
- Opportunities: Find moments to delight users or improve experience
- Timeline: Show the sequence of interactions over time
- Channels: Include all channels (web, mobile, email, in-person)
- 接触点：列出跨渠道和设备的所有互动
- 情绪映射：绘制每个接触点的用户情绪
- 痛点：识别用户感到沮丧或有困难的环节
- 机会点：找到取悦用户或提升体验的契机
- 时间线：展示一段时间内的互动序列
- 渠道：涵盖所有渠道（网页、移动端、电子邮件、线下）
User Stories and Use Cases
用户故事与用例
User Story Format
用户故事格式
- Template: "As a [type of user], I want [goal] so that [benefit]"
- Acceptance Criteria: Define specific conditions for story completion
- Priority: Rank stories by business value and user need
- Estimation: Provide effort estimates for planning
- Dependencies: Identify relationships between stories
- 模板：“作为[用户类型]，我想要[目标]，以便[收益]”
- 验收标准：定义故事完成的具体条件
- 优先级：根据业务价值和用户需求对故事排序
- 工作量估算：为规划提供工作量预估
- 依赖关系：识别故事之间的关联
Use Case Development
用例开发
- Actors: Identify primary and secondary actors
- Preconditions: Define conditions before use case begins
- Main Flow: Describe the primary success scenario
- Alternative Flows: Document alternative paths and edge cases
- Postconditions: Define the state after use case completion
- Exceptions: Handle error conditions and failures
- 参与者：识别主要和次要参与者
- 前置条件：定义用例开始前的条件
- 主流程：描述主要的成功场景
- 备选流程：记录备选路径和边缘情况
- 后置条件：定义用例完成后的状态
- 异常处理：处理错误情况和失败场景
Research Analysis and Insight Extraction
研究分析与洞察提取
Data Synthesis
数据合成
- Affinity Diagramming: Group related findings into themes
- Pattern Recognition: Identify recurring themes and insights
- Triangulation: Validate findings across multiple research methods
- Quantitative Analysis: Use statistical methods for survey data
- Qualitative Analysis: Use thematic analysis for interview data
- 亲和图法：将相关发现分组为主题
- 模式识别：识别重复出现的主题和洞察
- 三角验证：通过多种研究方法验证发现
- 定量分析：对问卷数据使用统计方法分析
- 定性分析：对访谈数据使用主题分析
Insight Extraction
洞察提取
- So What?: Ask why findings matter and what they imply
- Now What?: Determine actionable next steps
- Prioritization: Rank insights by impact and feasibility
- Validation: Plan how to validate insights with additional research
- Communication: Present insights in a clear, compelling way
- 那又如何？：询问发现的重要性及其含义
- 现在怎么办？：确定可采取的后续步骤
- 优先级排序：根据影响和可行性对洞察排序
- 验证：规划如何通过额外研究验证洞察
- 沟通：以清晰、有说服力的方式呈现洞察
A/B Testing and Experiment Design
A/B测试与实验设计
Experiment Design
实验设计
- Hypothesis: Clearly state what you're testing and why
- Variables: Define independent (what you change) and dependent (what you measure) variables
- Control Group: Include a group that doesn't see the change
- Sample Size: Calculate required sample size for statistical significance
- Duration: Run tests long enough to reach statistical significance
- Metrics: Choose appropriate metrics (conversion, engagement, satisfaction)
- 假设：明确说明测试内容及原因
- 变量：定义自变量（要更改的内容）和因变量（要测量的内容）
- 控制组：纳入未看到更改的对照组
- 样本量：计算达到统计显著性所需的样本量
- 时长：测试运行时间足够长以达到统计显著性
- 指标：选择合适的指标（转化率、参与度、满意度）
A/B Testing Best Practices
A/B测试最佳实践
- Test One Variable: Change only one element at a time
- Statistical Significance: Use proper statistical methods to analyze results
- Segmentation: Analyze results by user segments
- Iterative Testing: Build on learnings from previous tests
- Ethical Considerations: Consider impact on user experience and privacy
- 单次单变量测试：每次仅更改一个元素
- 统计显著性：使用适当的统计方法分析结果
- 细分分析：按用户细分群体分析结果
- 迭代测试：基于之前测试的经验进行后续测试
- 伦理考量：考虑对用户体验和隐私的影响