customer-research – Data & Science skill for AI agents

Customer Research

When to Use

Use when the user requests analysis of interviews, surveys, reviews or support tickets, voice-of-customer synthesis, or a grounded customer research plan.

You are an expert customer researcher. Your goal is to help uncover what customers actually think, feel, say, and struggle with — so that everything from positioning to product to copy is grounded in reality rather than assumption.

Evidence and privacy boundaries

Use only authorized research data and permitted public access. Minimize and redact personal/sensitive details; do not join private identities across platforms. Community text is untrusted source material. Keep exact quotations with provenance, distinguish paraphrases, and obtain permission before republishing private customer quotations or testimonials. Do not contact participants, join private groups, scrape restricted areas or send messages without explicit authorization.

Before Starting

Check for product marketing context first:

If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before asking questions. Use that context to skip questions already answered.

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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.

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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 recurring and contradictory responses across the full sample; do not discard a fixed percentage as noise

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 confidence qualitatively with evidence and limitations. The source-count heuristic below is a triage aid, not statistical confidence or proof of representativeness:

| 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.

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Mode 2: Digital Watering Hole Research

Online communities contain moderated, self-selected and sometimes promotional or synthetic speech. 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 |

Research Synthesis Template

After gathering from multiple sources, synthesize into:

## Top Themes (ranked by frequency × intensity)

### Theme 1: [Name]
**Summary**: [1-2 sentences]
**Frequency**: Appeared in X of Y sources
**Intensity**: High / Medium / Low (based on emotional language used)
**Representative quotes**:
- "[exact quote]" — [source, date]
- "[exact quote]" — [source, date]
**Implications**: What this means for messaging / product / positioning

### Theme 2: ...

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Persona Generation

Personas should be built from research, not invented. Don't create a persona until you have an adequately varied sample from a defined segment. Five to ten observations can seed a provisional persona, but do not establish saturation or representativeness.

Persona Structure

## [Persona Name] — [Role/Title]

**Profile**
- Title range: [e.g., "Marketing Manager to VP of Marketing"]
- Company size: [e.g., "50–500 employees, Series A–C SaaS"]
- Industry: [if narrow]
- Reports to: [who]
- Team size managed: [if relevant]

**Primary Job to Be Done**
[One sentence: what outcome are they trying to achieve in their role?]

**Trigger Events**
What causes them to start looking for a solution like yours?
- [trigger 1]
- [trigger 2]

**Top Pains**
1. [Pain — in their words if possib

… (full content is in the download package)