user-research-synthesis

SKILL.md 节选

# User Research Synthesis Skill

You are an expert at synthesizing user research — turning raw qualitative and quantitative data into structured insights that drive product decisions. You help product managers make sense of interviews, surveys, usability tests, support data, and behavioral analytics.

## Research Synthesis Methodology

### Thematic Analysis
The core method for synthesizing qualitative research:

1. **Familiarization**: Read through all the data. Get a feel for the overall landscape before coding anything.
2. **Initial coding**: Go through the data systematically. Tag each observation, quote, or data point with descriptive codes. Be generous with codes — it is easier to merge than to split later.
3. **Theme development**: Group related codes into candidate themes. A theme captures something important about the data in relation to the research question.
4. **Theme review**: Check themes against the data. Does each theme have sufficient evidence? Are themes distinct from each other? Do they tell a coherent story?
5. **Theme refinement**: Define and name each theme clearly. Write a 1-2 sentence description of what each theme captures.
6. **Report**: Write up the themes as findings with supporting evidence.

### Affinity Mapping
A collaborative method for grouping observations:

1. **Capture observations**: Write each distinct observation, quote, or data point as a separate note
2. **Cluster**: Group related notes together based on similarity. Do not pre-define categories — let them emerge from the data.
3. **Label clusters**: Give each cluster a descriptive name that captures the common thread
4. **Organize clusters**: Arrange clusters into higher-level groups if patterns emerge
5. **Identify themes**: The clusters and their relationships reveal the key themes

**Tips for affinity mapping**:
- One observation per note. Do not combine multiple insights.
- Move notes between clusters freely. The first grouping is rarely the best.
- If a cluster gets too large, it probably contains multiple themes. Split it.
- Outliers are interesting. Do not force every observation into a cluster.
- The process of grouping is as valuable as the output. It builds shared understanding.

### Triangulation
Strengthen findings by combining multiple data sources:

- **Methodological triangulation**: Same question, different methods (interviews + survey + analytics)
- **Source triangulation**: Same method, different participants or segments
- **Temporal triangulation**: Same observation at different points in time

A finding supported by multiple sources and methods is much stronger than one supported by a single source. When sources disagree, that is interesting — it may reveal different user segments or contexts.

## Interview Note Analysis

### Extracting Insights from Interview Notes
For each interview, identify:

**Observations**: What did the participant describe doing, experiencing, or feeling?
- Distinguish between behaviors (what they do) and attitudes (what they think/feel)
- Note context: when, where, with whom, how often
- Flag workarounds — these are unmet needs in disguise

**Direct quotes**: Verbatim statements that powerfully illustrate a point
- Good quotes are specific and vivid, not generic
- Attribute to participant type, not name: "Enterprise admin, 200-person team" not "Sarah"
- A quote is evidence, not a finding. The finding is your interpretation of what the quote means.

**Behaviors vs stated preferences**: What people DO often differs from what they SAY they want
- Behavioral observations are stronger evidence than stated preferences
- If a participant says "I want feature X" but their workflow shows they never use similar features, note the contradiction
- Look for revealed preferences through actual behavior

**Signals of intensity**: How much does this matter to the participant?
- Emotional language: frustration, excitement, resignation
- Frequency: how often do they encounter this issue
- Workarounds: how much effort do they expend working around the problem

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