customer-research

Files (3)

• SKILL.md
• evals.json · evals/
• source-guides.md · references/

SKILL.md (first lines)

## Mode 1: Analyzing Existing Research Assets

### Asset Types

**Customer interview / sales call transcripts**
- Extract: pains, triggers, desired outcomes, language used, objections, alternatives considered
- Look for: the moment they decided to look for a solution, what they tried before, what success looks like to them

**Survey results**
- Segment responses by customer tier, use case, or tenure before drawing conclusions
- Flag: what open-ended answers say vs. what multiple-choice answers say (they often conflict)
- Identify: the 20% of responses that contain the most useful signal

**Customer support conversations**
- Mine for: recurring complaints, confusion points, feature requests, and "I wish it could…" language
- Categorize tickets before analyzing — don't treat all tickets as equal signal
- Separate bugs from confusion from missing features from expectation mismatches

**Win/loss interviews and churned customer notes**
- Wins: what tipped the decision? What almost made them choose a competitor?
- Losses and churn: was it price, features, fit, timing, or something else?
- Segment by reason — don't average across different churn causes

**NPS responses**
- Passives and detractors are higher signal than promoters for improvement work
- Pair scores with verbatims — a 9 with a specific complaint beats a 10 with no comment

### Extraction Framework

For each asset, extract:

1. **Jobs to Be Done** — what outcome is the customer trying to achieve?
   - Functional job: the task itself
   - Emotional job: how they want to feel
   - Social job: how they want to be perceived

2. **Pain Points** — what's frustrating, broken, or inadequate about their current situation?
   - Prioritize pains mentioned unprompted and with emotional language

3. **Trigger Events** — what changed that made them seek a solution?
   - Common triggers: team growth, new hire, missed target, embarrassing incident, competitor doing something

4. **Desired Outcomes** — what does success look like in their words?
   - Capture exact quotes, not paraphrases

5. **Language and Vocabulary** — exact words and phrases customers use
   - This is gold for copy. "We were drowning in spreadsheets" > "manual process inefficiency"

6. **Alternatives Considered** — what else did they look at or try?
   - Includes doing nothing, hiring someone, or building internally

### Synthesis Steps

After extracting from individual assets:

1. **Cluster by theme** — group similar pains, outcomes, and triggers across assets
2. **Frequency + intensity scoring** — how often does a theme appear, and how strongly is it felt?
3. **Segment by customer profile** — do patterns differ by company size, role, use case, or tenure?
4. **Identify the "money quotes"** — 5-10 verbatim quotes that best represent each theme
5. **Flag contradictions** — where do customers say one thing but do another?

### Research Quality Guardrails

Label every insight with a confidence level before presenting it:

| Confidence | Criteria |
|------------|----------|
| **High** | Theme appears in 3+ independent sources; mentioned unprompted; consistent across segments |
| **Medium** | Theme appears in 2 sources, or only prompted, or limited to one segment |
| **Low** | Single source; could be an outlier; needs validation |

**Recency window**: Weight sources from the last 12 months more heavily. Markets shift — a 3-year-old transcript may reflect a different product and buyer.

**Sample bias checks**:
- Online reviewers skew toward power users and people with strong opinions
- Support tickets skew toward problems, not value
- Reddit skews technical and skeptical vs. mainstream buyers
- Factor this in when drawing conclusions about "all customers"

**Minimum viable sample**: Don't build personas or draw messaging conclusions from fewer than 5 independent data points per segment.

---

## Mode 2: Digital Watering Hole Research

Online communities are where customers speak without a filter. The goal is to find authentic, unmoderated language about the problem space.

### Where to Look

Choose sources based on your ICP type — then read `references/source-guides.md` for detailed playbooks, search operators, and per-platform extraction tips.

| ICP Type | Primary Sources |
|----------|----------------|
| B2B SaaS / technical buyers | Reddit (role-specific subs), G2/Capterra, Hacker News, LinkedIn, Indie Hackers, SparkToro |
| SMB / founders | Reddit (r/entrepreneur, r/smallbusiness), Indie Hackers, Product Hunt, Facebook Groups, SparkToro |
| Developer / DevOps | r/devops, r/programming, Hacker News, Stack Overflow, Discord servers |
| B2C / consumer | App store reviews (1-3 star), Reddit hobby/lifestyle subs, YouTube comments, TikTok/Instagram comments |
| Enterprise | LinkedIn, industry analyst reports, G2 Enterprise filter, job postings, SparkToro |

**Quick decision guide:**
- Have a product category? → Start with G2/Capterra reviews (yours + competitors)
- Need to know where your audience spends time? → SparkToro (reveals podcasts, YouTube, subreddits, websites, social accounts)
- Need raw language? → Reddit and YouTube comments
- Need trigger events? → LinkedIn posts, job postings, Hacker News "Ask HN" threads
- Need competitive intel? → Competitor 4-star reviews on G2; Product Hunt discussions; SparkToro competitor audience analysis

### What to Extract from Each Source

For every piece of content you find:

| Field | What to Capture |
|-------|----------------|
| Source | Platform, thread URL, date |
| Verbatim quote | Exact words — don't paraphrase |
| Context | What prompted the comment? |
| Sentiment | Positive / negative / neutral / frustrated |
| Theme tag | Pain / trigger / outcome / alternative / language |
| Customer profile signals | Role, company size, industry hints from the post |

From mkurman/zorai, MIT.

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