suede-customer-research

Files (6)

• CARD.md
• SKILL.md
• openai.yaml · agents/
• evals.json · evals/
• persona-templates.md · references/
• source-guides.md · references/

SKILL.md (first lines)

# Suede AI Customer Research Skills

Use this customer-research playbook to ground positioning, product, and copy in traceable customer evidence rather than assumption.

## Compatibility

An agent able to read supplied research files and save evidence captures. New public-source research needs web access; existing-asset synthesis requires no paid research tool.

## Before Starting

Check for `.agents/product-marketing.md` (or `.claude/product-marketing.md`, or the legacy `product-marketing-context.md`) and read it if present: the ICP, segment definitions, and what research already exists decide where to look and what counts as a representative sample. Ask only what it does not already answer.

---

## Two Modes of Research

### Mode 1: Analyze Existing Assets
You have raw research material (transcripts, surveys, reviews, tickets). Your job is to extract signal.

### Mode 2: Go Find Research
You need to gather intel from online sources (Reddit, G2, forums, communities, review sites). Your job is to know where to look and what to extract.

Most engagements combine both. Establish which mode applies before proceeding.

---

## 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**: 5 independent data points per segment (10 is better), drawn from interviews, reviews, tickets, or community posts, before building a persona or drawing a messaging conclusion for that segment. Below 5, present the material as raw signal, not as a finding.

---

## 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 |
|----------|----------------|

From JasonColapietro/suede-creator-skills, MIT.

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