{"slug":"suede-customer-research","title":"suede-customer-research","summary":"Suede-owned customer-research discipline for interview design, transcript and ticket synthesis, review and forum mining, quote banks, jobs, and evidence-backed personas. Use when discovering or synthesizing what a defined customer segment actually says, does, needs, and resists. ","platform":"Claude","tags":[],"authorName":"LLM Mart","authorSlug":"llm-mart","score":0,"source":"github","price":null,"verified":false,"createdAt":"2026-08-23T08:58:01.823533Z","repo":{"url":"https://github.com/JasonColapietro/suede-creator-skills","stars":125,"forks":12,"license":"MIT","updatedAt":"2026-09-24T09:44:24Z"},"bodyHtml":"
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name: suede-customer-research\ndescription: \"Suede-owned customer-research discipline for interview design, transcript and ticket synthesis, review and forum mining, quote banks, jobs, and evidence-backed personas. Use when discovering or synthesizing what a defined customer segment actually says, does, needs, and resists. NOT FOR: competitor-only profiling (use suede-competitor-profiling), writing final marketing copy (use suede-copy), or deciding product priorities without product evidence (use suede-product-marketing).\"\nmetadata:\nversion: 2.0.1

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Suede Customer Research

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

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Before Starting

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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.

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Two Modes of Research

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Mode 1: Analyze Existing Assets

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You have raw research material (transcripts, surveys, reviews, tickets). Your job is to extract signal.

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Mode 2: Go Find Research

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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.

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

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Mode 1: Analyzing Existing Research Assets

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Asset Types

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Customer interview / sales call transcripts

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Survey results

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Customer support conversations

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Win/loss interviews and churned customer notes

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NPS responses

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Extraction Framework

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For each asset, extract:

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    \n

  1. Jobs to Be Done — what outcome is the customer trying to achieve?

    \n\n
    2. \n

  2. Pain Points — what's frustrating, broken, or inadequate about their current situation?

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    3. \n

  3. Trigger Events — what changed that made them seek a solution?

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    4. \n

  4. Desired Outcomes — what does success look like in their words?

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    5. \n

  5. Language and Vocabulary — exact words and phrases customers use

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    6. \n

  6. Alternatives Considered — what else did they look at or try?

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    7. \n
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Synthesis Steps

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After extracting from individual assets:

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

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Label every insight with a confidence level before presenting it:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
ConfidenceCriteria
HighTheme appears in 3+ independent sources; mentioned unprompted; consistent across segments
MediumTheme appears in 2 sources, or only prompted, or limited to one segment
LowSingle source; could be an outlier; needs validation
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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.

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Sample bias checks:

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Minimum viable sample: 5 independent data points per segment — 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.

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Mode 2: Digital Watering Hole Research

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

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Where to Look

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

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ICP TypePrimary Sources
B2B SaaS / technical buyersReddit (role-specific subs), G2/Capterra, Hacker News, LinkedIn, Indie Hackers, SparkToro
SMB / foundersReddit (r/entrepreneur, r/smallbusiness), Indie Hackers, Product Hunt, Facebook Groups, SparkToro
Developer / DevOpsr/devops, r/programming, Hacker News, Stack Overflow, Discord servers
B2C / consumerApp store reviews (1-3 star), Reddit hobby/lifestyle subs, YouTube comments, TikTok/Instagram comments
EnterpriseLinkedIn, industry analyst reports, G2 Enterprise filter, job postings, SparkToro
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Quick decision guide:

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What to Extract from Each Source

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For every piece of content you find:

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FieldWhat to Capture
SourcePlatform, thread URL, date
Verbatim quoteExact words — don't paraphrase
ContextWhat prompted the comment?
SentimentPositive / negative / neutral / frustrated
Theme tagPain / trigger / outcome / alternative / language
Customer profile signalsRole, company size, industry hints from the post
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Persist Captures Before Synthesizing

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Save what you gathered before extracting themes from it — otherwise the\nprovenance gate below is unenforceable and a re-run repeats the entire\ncollection. Mirror the raw-evidence convention suede-competitor-profiling\nuses: one dated folder per run at customer-research/raw/<YYYY-MM-DD>/, one\nfile per source inside it (reddit.md, g2-<competitor>.md, app-store.md),\nplus a captures.csv whose columns are the capture table above. Create the date\nfolder fresh each run and never overwrite a prior date's — that is how you diff\nwhat moved in the market. Mode 1 assets (transcripts, tickets, win/loss notes,\nNPS verbatims) usually already live somewhere: don't copy them, record each in\ncaptures.csv by file path or system identifier plus date and segment, so every\nquote resolves to a named record either way.

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Research Synthesis Template

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After gathering from multiple sources, synthesize into:

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

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When there are no reviews yet

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Early-stage products (or new categories) lack first-party review data. Don't invent personas — walk outward through proxy sources, in order:

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    \n
  1. Your own differentiator — what the product does differently defines who feels that difference most; write the hypothesis down as a hypothesis
    2. \n
  2. Direct competitors' reviews — their customers describe the problem space in their words (note what's praised and what's missing)
    3. \n
  3. Comparable products on marketplaces — Amazon/app-store reviews for adjacent solutions to the same job
    4. \n
  4. Adjacent brands sharing the audience — what else this buyer buys; their reviews reveal the buyer's broader language and values
    5. \n
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Personas built this way are provisional: tag each with its proxy source, and replace proxy evidence with first-party evidence as real reviews arrive. The minimum viable sample above applies to proxy evidence too.

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Persona Structure

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Read references/persona-templates.md before writing the first persona of a run — it holds the full fill-in structure (profile, primary job, triggers, pains, desired outcomes, objections, alternatives, vocabulary, how to reach them). Personas written from memory drift field by field and stop being comparable.

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Persona Anti-Patterns

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Provenance Gate

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Run this over the finished deliverable, before it goes out. Boundaries below\nforbids fabricated quotes, themes, sample sizes and frequency counts; this is\nwhat makes that checkable rather than aspirational.

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Deliverable Formats

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Default deliverable: a research synthesis report (themes, quotes, patterns,\nimplications) plus a VOC quote bank organized by theme. Produce those unless\nthe user asked for something else.

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Offer these instead or in addition when the goal calls for it: a persona\ndocument (1-3 personas), a jobs-to-be-done map (functional, emotional,\nsocial jobs by segment), a competitive intelligence summary (what customers\nsay about competitors vs. you), or a research gap analysis (what you still\ndon't know and how to find it).

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Questions to Ask Before Proceeding

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If context is unclear:

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    \n
  1. What's the goal? Improve messaging? Build personas? Find product gaps? Understand churn?
    2. \n
  2. What do you already have? (transcripts, surveys, tickets, G2 reviews, nothing)
    3. \n
  3. Who is the target segment? (all customers, a specific tier, churned users, prospects who didn't buy)
    4. \n
  4. What's your product? (if not in the product marketing context file)
    5. \n
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Don't ask all four at once — lead with #1 and #2, then follow up as needed.

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Boundaries

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Routing

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