pm-research-patterns

PM Research Patterns

Overview

Expert knowledge for synthesizing user research, competitive intelligence, and customer feedback into structured insights. Turns raw data into actionable product decisions.

The Iron Law

NO SYNTHESIS WITHOUT EVIDENCE GRADING AND SOURCE TRIANGULATION

Every finding must state its evidence strength (Strong/Medium/Weak). Every theme must be checked against multiple sources. Research without grading is storytelling. Findings without triangulation are anecdotes.

When to Use

• Synthesizing user interview notes
• Analyzing survey results
• Conducting competitive analysis
• Processing customer feedback at scale
• Building evidence-based personas
• Sizing opportunities from research data

Thematic Analysis Methodology

The core method for synthesizing qualitative research:

1. Familiarize: Read through all data. Get a feel for the landscape before coding.
2. Initial Coding: Tag each observation, quote, or data point with descriptive codes. Be generous -- easier to merge than split.
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 data. Does each theme have sufficient evidence? Are themes distinct? Do they tell a coherent story?
5. Theme Refinement: Define and name each theme clearly. 1-2 sentence description of what each captures.
6. Report: Write up themes as findings with supporting evidence.

Affinity Mapping

A collaborative method for grouping observations:

1. Capture: Write each distinct observation as a separate note
2. Cluster: Group related notes by similarity. Don't pre-define categories -- let them emerge.
3. Label: Give each cluster a descriptive name
4. Organize: Arrange clusters into higher-level groups if patterns emerge
5. Identify themes: Clusters and relationships reveal key themes

Tips:

• One observation per note
• Move notes between clusters freely
• Large clusters probably contain multiple themes -- split them
• Outliers are interesting -- don't force-fit

Triangulation

Strengthen findings by combining sources:

Type	Definition	Example
Methodological	Same question, different methods	Interviews + survey + analytics
Source	Same method, different participants	Multiple user segments
Temporal	Same observation, different times	Q1 vs Q3 comparison

Findings supported by multiple sources and methods are much stronger than single-source. When sources disagree: that is a signal, not an error. May reveal segments or contexts.

Evidence Strength Grading

Grade	Definition	Criteria
Strong	High confidence	3+ independent sources, multiple methods, behavioral data
Medium	Moderate confidence	1-2 sources, single method, or stated preferences
Weak	Low confidence	Assumption, single anecdote, or extrapolation

Rule: Every finding must state its evidence strength. Weak evidence must be flagged explicitly.

Interview Note Processing

Extracting Insights

For each interview, identify:

Observations: What did the participant describe doing, experiencing, feeling?

• Distinguish behaviors (what they do) from 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 illustrate a point

• Good quotes are specific and vivid, not generic
• Attribute to participant type, not name: "Enterprise admin, 200-person team"
• A quote is evidence, not a finding

Behaviors vs Stated Preferences: What people DO differs from what they SAY

• Behavioral observations are stronger evidence
• Note contradictions between stated and revealed preferences

Signals of Intensity: How much does this matter?

• Emotional language, frequency, workaround effort, impact of failure

Cross-Interview Analysis

• Patterns: which observations appear across participants?
• Frequency: how many mentioned each theme?
• Segments: do different user types show different patterns?
• Contradictions: where do participants disagree?
• Surprises: what challenged assumptions?

Competitive Analysis

Identifying Competitors

Level	Definition
Direct	Same problem, same approach, same users
Indirect	Same problem, different approach
Adjacent	Could expand into your space
Substitute	Entirely different solution to same need

Feature Comparison Matrix

| Capability | Us | Competitor A | Competitor B |
|-----------|-----|-------------|-------------|
| [Feature] | Strong | Adequate | Absent |

Rating scale:

• Strong: Market-leading, deep, well-executed
• Adequate: Functional, gets job done, not differentiated
• Weak: Exists but limited, significant gaps
• Absent: Not available

Win/Loss Patterns

Track reasons for wins and losses:

• Feature gaps, integration advantages, pricing structure
• Incumbent advantage, sales execution, brand/trust
• Segment by deal type, size, industry

Opportunity Sizing

For each finding, estimate:

• Addressable users: How many could benefit?
• Frequency: How often encountered? (daily/weekly/monthly)
• Severity: Impact when it occurs? (blocker/friction/annoyance)
• Willingness to pay: Would it drive upgrades/retention?

Scoring: Impact = Users x Frequency x Severity

Present with transparency:

• Show assumptions and confidence levels
• Use ranges, not false precision
• Compare opportunities against each other (relative ranking)

Persona Development

Build evidence-based personas from research data:

1. Identify behavioral clusters across participants
2. Define distinguishing variables (company size, skill level, use case)
3. Create profiles (3-5 personas max):

[Persona Name] -- [One-line description]

Who: Role, company, experience
Goals: Primary jobs to be done
Pain points: Top 3 frustrations
Behaviors: Usage patterns, tools, frequency
Values: What matters most in a solution
Quote: Representative verbatim quote

Avoid: Demographic-only personas, too many personas (>5), fictional data, never updating.

Survey Design Principles

Question Types and When to Use

Type	Best For	Watch Out For
Multiple choice	Frequency, demographics, simple preferences	Forcing choices that don't reflect reality
Likert scale	Attitudes, satisfaction, agreement	Acquiescence bias (people tend to agree)
Open-ended	Exploring unknowns, getting language	Hard to analyze at scale
Ranking	Relative priorities	Cognitive load above 5-7 items
Matrix	Multiple items on same scale	Straight-lining (same answer for all)

Writing Good Questions

• One concept per question -- never double-barreled
• Avoid leading language ("Don't you agree that...")
• Offer "Not applicable" or "I don't know" options
• Randomize answer order where possible
• Test with 3-5 people before sending

Sample Size Guidelines

Confidence Level	Margin of Error	Sample Needed
95%	+/- 5%	~385
95%	+/- 10%	~97
90%	+/- 5%	~271

For qualitative themes: 12-15 interviews typically reach saturation for a well-defined user segment.

Jobs To Be Done (JTBD) Framework

Core Structure

When [situation], I want to [motivation], so I can [expected outcome].

Uncovering JTBD

Interview questions that reveal jobs:

• "Walk me through the last time you [did this task]."
• "What were you trying to accomplish?"
• "What did you try before this? Why did you switch?"
• "What would make you stop using this?"

Job Map

Break a main job into process steps:

1. Define: How does the customer define what they need?
2. Locate: How do they find inputs?
3. Prepare: How do they set up?
4. Confirm: How do they verify readiness?
5. Execute: How do they do the core task?
6. Monitor: How do they track progress?
7. Modify: How do they adjust?
8. Conclude: How do they finish?

Each step is an opportunity for innovation.

Research Synthesis Template

Save to: docs/research/YYYY-MM-DD-<topic>-synthesis.md

# Research Synthesis: [Topic]

## Methodology
- Sources: [list with type]
- Methods: [interviews/survey/analytics/competitive]
- Sample: [size and composition]
- Period: [when conducted]

## Key Themes

### Theme 1: [Name]
**Evidence Strength:** Strong/Medium/Weak
**Sources:** [which sources]
**Finding:** [description]
**Key Quotes:** [1-2 representative]
**Implication:** [what this means for product]

## Opportunities
| Opportunity | Users | Frequency | Severity | Evidence | Score |
|------------|-------|-----------|----------|----------|-------|

## Personas
[If applicable]

## Recommendations
1. [Recommendation] -- [Evidence basis]

## Open Questions
- [Remaining unknowns]

Red Flags — STOP

• Presenting findings without evidence strength grades → STOP. Grade every finding Strong/Medium/Weak.
• Single source supporting a "key theme" → STOP. One anecdote is not a theme. Need 3+ for Strong.
• Skipping triangulation → STOP. Combine methods and sources before claiming confidence.
• Synthesizing without methodology stated → STOP. Document how you collected and analyzed.
• Cherry-picking quotes that confirm hypothesis → STOP. Present all themes including contradictory.
• Research report with no recommendations → STOP. Research without action is waste.

Rationalization Prevention

Excuse	Reality
"We only have one data source"	Then grade it Medium/Weak and say so. Don't present assumptions as findings.
"The quotes speak for themselves"	Quotes are evidence, not findings. Themes require interpretation and grading.
"We don't have time to triangulate"	Untriangulated findings lead to wrong decisions. Triangulation saves time long-term.
"Everyone agrees, so it must be true"	Unanimous agreement may mean you asked leading questions. Check methodology.
"The data is clear"	If clear, grading takes 30 seconds. If ambiguous, grading prevents overconfidence.
"We'll validate later"	Later means after you've built the wrong thing. Validate strength now.
"This user is very representative"	One user is N=1. Representative requires multiple participants showing same pattern.
"Outliers aren't important"	Outliers may represent edge cases, future segments, or broken assumptions. Document them.

Anti-Patterns to Avoid

Anti-Pattern	Problem	Instead
Cherry-picking quotes	Confirmation bias	Present all themes, including contradictory
N=1 findings	Single anecdote masquerading as insight	Require 3+ sources for "strong" evidence
Asking "would you use X?"	Stated preference is unreliable	Ask about past behavior and current workarounds
Ignoring outliers	Missing edge cases and segments	Document outliers explicitly, investigate
Research without action	Waste of effort	Every synthesis must end with recommendations
Skipping triangulation	Fragile findings	Combine methods and sources