# Research Synthesis

## Purpose
Combine qualitative research (interviews, usability tests, feedback) with quantitative data (analytics, metrics, surveys) to extract actionable insights that neither source could provide alone.

## When to Use
- Have both customer interview data AND product analytics
- Need to understand WHY metrics are changing (not just WHAT)
- Connecting user behavior patterns to stated motivations
- Building a complete picture of the customer experience
- Validating qualitative findings with quantitative data (or vice versa)

## Core Principle

**Quantitative data shows WHAT is happening.**
**Qualitative data shows WHY it's happening.**
**Synthesis connects the two.**

Neither alone is sufficient:
- Analytics without context leads to wrong conclusions
- Interviews without data leads to anecdotes, not patterns

## Process

### 1. Organize Your Data Sources

**Qualitative Sources:**
| Source | What It Provides | Typical Location |
|--------|------------------|------------------|
| Customer interviews | Deep motivations, pain points, language | Transcripts, recordings |
| Usability tests | Friction points, confusion moments | Session recordings, notes |
| Support tickets | Real problems, customer language | Help desk system |
| Sales call notes | Objections, buying triggers | CRM notes |
| Social mentions | Unsolicited feedback, sentiment | Twitter, Reddit, forums |
| Churn interviews | Why they left, what's missing | Exit surveys, calls |

**Quantitative Sources:**
| Source | What It Provides | Typical Location |
|--------|------------------|------------------|
| Product analytics | Usage patterns, funnels, retention | Mixpanel, Amplitude, PostHog |
| Revenue metrics | Conversion, churn, expansion | Stripe, billing system |
| Survey data | NPS, CSAT, feature requests | Typeform, SurveyMonkey |
| A/B test results | What changes move metrics | Experiment platform |
| Session recordings | Where users click, scroll, struggle | Hotjar, FullStory |
| Cohort data | How behavior changes over time | Analytics platform |

### 2. Five Synthesis Patterns

#### Pattern 1: Revenue/Funnel Diagnostics

**Question:** Why is [metric] declining?

**Method:**
1. Identify the WHAT from analytics (which metric, which segment, since when)
2. Search qualitative data for the WHY (complaints, confusion, friction)
3. Cross-reference timing (deployment logs, feature changes)
4. Validate with additional data if needed

**Example:**
- **Quant:** Conversion dropped 15% starting March 1
- **Context:** New pricing page deployed Feb 28
- **Qual:** 3 support tickets mention "confusing pricing" in March
- **Qual:** Usability test transcript: "I can't figure out which plan I need"
- **Synthesis:** Pricing page redesign confused users → add comparison table

#### Pattern 2: Funnel + Friction Mapping

**Question:** Where do users drop and WHY?

**Method:**
1. Map the funnel stages with drop-off rates (analytics)
2. Overlay qualitative friction mentions at each stage
3. Identify stages with high drop + clear friction cause
4. Prioritize fixes by (drop rate × addressability)

**Synthesis Template:**

| Stage | Drop Rate | Qual Friction | Mentions | Priority |
|-------|-----------|---------------|----------|----------|
| Signup → Onboarding | 40% | "Too many steps" | 5 | High |
| Onboarding → Activation | 60% | "Didn't know what to do next" | 8 | Critical |
| Activation → Week 2 | 30% | "Forgot about it" | 3 | Medium |

#### Pattern 3: User Segmentation

**Question:** Who are our power users and why?

**Method:**
1. Define behavioral hypothesis (what makes power users different?)
2. Test against analytics (do they do X more than others?)
3. Find power users and interview them (why do you do X?)
4. Validate findings against broader cohort data

**Example:**
- **Hypothesis:** Power users connect integrations
- **Test:** Users with 3+ integrations have 2x retention
- **Interview:** "The integrations are what make it useful"
- **Synthesis:** Integrations drive retention → optimize integration setup

#### Pattern 4: Aha Moment Discovery

**Question:** What drives long-term retention?

**Method:**
1. Mine interview data for "clicked" moments ("That's when I realized...")
2. Translate to measurable behaviors
3. Test retention correlation (do users who do X retain better?)
4. Validate with cohort analysis

**Example:**
- **Qual:** "When I saw the agent reply to a customer, I knew I needed this"
- **Behavior:** First AI-handled conversation
- **Test:** Users who see AI conversation in day 1 retain 40% better
- **Synthesis:** Aha moment = seeing AI work → optimize for day-1 conversation

#### Pattern 5: Opportunity Validation

**Question:** Is [feature request] real demand or noise?

**Method:**
1. Count unprompted mentions in qualitative data
2. Check usage of related existing features
3. Assess segment overlap (who asks for this?)
4. Estimate impact (if we build this, what changes?)

**Validation Matrix:**

| Signal | Strong | Weak |
|--------|--------|------|
| Unprompted mentions | 5+ interviews | 1-2 mentions |
| Support tickets | Recurring theme | One-off |
| Workaround behavior | Users hacking around it | No workarounds |
| Willingness to pay | "I'd pay more for this" | "Nice to have" |
| Segment fit | Core users asking | Edge cases asking |

### 3. Synthesis Framework

**For each insight, document:**

| Element | Description |
|---------|-------------|
| **Finding** | What we learned (one sentence) |
| **Quant Evidence** | The numbers that show this |
| **Qual Evidence** | The quotes/observations that explain it |
| **Confidence** | High/Medium/Low based on evidence strength |
| **Implication** | What this means for the product |
| **Action** | Specific next step |

**Example:**

| Element | Detail |
|---------|--------|
| **Finding** | Users who see AI handle a conversation in day 1 retain 40% better |
| **Quant** | Day-1 AI conversation cohort: 65% week-4 retention vs. 25% baseline |
| **Qual** | Interview: "When I saw it actually reply to a customer, I knew I needed this" (×4 users) |
| **Confidence** | High (strong correlation + consistent interview theme) |
| **Implication** | First AI conversation is the activation moment |
| **Action** | Redesign onboarding to guarantee AI conversation in session 1 |

### 4. Common Pitfalls

**Cherry-picking:**
- Finding one quote to support a metric
- Fix: Require multiple qual sources for each insight

**False causation:**
- Assuming correlation is causation
- Fix: Look for mechanism (WHY would X cause Y?)

**Recency bias:**
- Overweighting recent interviews
- Fix: Track themes over time

**Loudest voice:**
- One emphatic user shapes perception
- Fix: Count mentions, don't just note them

**Metric worship:**
- Ignoring qual that contradicts quant
- Fix: Investigate contradictions—they're often the insight

### 5. Tools & Infrastructure

**Qualitative Data Management:**
- Tag interview transcripts by theme
- Create searchable repository
- Link quotes to customer profiles

**Quantitative + Context:**
- Document feature deployment dates
- Note seasonality patterns
- Maintain event/property definitions

**Integration:**
- Same customer ID across qual and quant
- Ability to pull analytics for specific interview subjects
- Combined dashboards showing both

### 6. Synthesis Cadence

| Frequency | Activity |
|-----------|----------|
| **Weekly** | Review new qual (tickets, interviews) against current metrics |
| **Monthly** | Deep synthesis of a specific question |
| **Quarterly** | Full customer journey synthesis |
| **Ad-hoc** | When metrics change unexpectedly |

### 7. Synthesis

After completing research synthesis:
- What do we know that we couldn't know from data OR interviews alone?
- What contradictions remain unresolved?
- What's the highest-confidence insight?
- What would change our understanding if we learned it was wrong?
- What's the next question to investigate?

## Example

**Context:** B2B SaaS product seeing declining trial-to-paid conversion

### Data Collection

**Quantitative:**
- Trial-to-paid dropped from 12% to 8% over 3 months
- Biggest drop in "team" trials (vs. individual)
- Day-7 engagement down 20%
- Pricing page bounce rate up 15%

**Qualitative:**
- 6 churn interviews: "Couldn't get my team to use it"
- 4 support tickets: "How do I add team members?"
- 2 sales calls: "Is there a team trial or do I need individual accounts?"
- Usability test: User couldn't find "invite team" button (took 4 minutes)

### Synthesis

#### Finding 1: Team onboarding is broken

| Element | Detail |
|---------|--------|
| **Finding** | Team trials fail because inviting teammates is hard |
| **Quant** | Team trial conversion: 5% (down from 15%). Individual: 12% (unchanged). |
| **Qual** | "Couldn't get my team to use it" (×6). "How do I add team members?" (×4). Usability test: 4 min to find invite. |
| **Confidence** | High |
| **Implication** | Team value requires team usage; if invites fail, value fails |
| **Action** | Make "invite team" step 2 of onboarding (not buried in settings) |

#### Finding 2: Pricing confusion for teams

| Element | Detail |
|---------|--------|
| **Finding** | Team buyers confused about per-seat vs. team pricing |
| **Quant** | Pricing page bounce +15%. Team segment most affected. |
| **Qual** | "Is there a team trial?" (×2 sales calls). Usability: "I don't understand which plan is for my team." |
| **Confidence** | Medium (fewer qual mentions) |
| **Implication** | Pricing page loses team buyers before they try |
| **Action** | Add team pricing toggle; clarify per-seat math |

#### Finding 3: Aha moment is "team collaboration"

| Element | Detail |
|---------|--------|
| **Finding** | Users who collaborate with teammate in week 1 convert 3x better |
| **Quant** | Collab in week 1: 24% conversion. No collab: 8% conversion. |
| **Qual** | "When my colleague replied in the app, I realized this could replace Slack threads" |
| **Confidence** | High |
| **Implication** | Aha moment for teams is seeing collaboration happen |
| **Action** | Trigger first collaboration in onboarding (suggest inviting specific person) |

### Prioritized Actions

1. **Move "invite team" to onboarding step 2** (Finding 1 + 3: High confidence, high impact)
2. **Add team pricing toggle with calculator** (Finding 2: Medium confidence, blocking conversion)
3. **A/B test "invite specific colleague" prompt** (Finding 3: Test the hypothesis)

**Insight:** Neither data source alone would have revealed that "invite team" being buried in settings was the root cause. Analytics showed team trials failing; interviews explained why. The synthesis turned a metric problem into a specific UX fix.
