Customer Research

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.

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: 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:

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 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.

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:

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

When there are no reviews yet

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

1. Your own differentiator — what the product does differently defines who feels that difference most; write the hypothesis down as a hypothesis
2. Direct competitors' reviews — their customers describe the problem space in their words (note what's praised and what's missing)
3. Comparable products on marketplaces — Amazon/app-store reviews for adjacent solutions to the same job
4. Adjacent brands sharing the audience — what else this buyer buys; their reviews reveal the buyer's broader language and values

Personas built this way are provisional: tag each with its proxy source, and replace proxy evidence with first-party evidence as real reviews arrive.

Personas should be built from research, not invented. Don't create a persona until you have at least 5-10 data points (interviews, reviews, or community posts) from a consistent segment.

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 possible]
2. [Pain]
3. [Pain]

**Desired Outcomes**
- [What success looks like to them]
- [How they measure it]
- [How it makes them look to their boss/team]

**Objections and Fears**
- [What makes them hesitate to buy or switch]

**Alternatives They Consider**
- [Competitor, DIY, do nothing, hire someone]

**Key Vocabulary**
Words and phrases they actually use (sourced from research):
- "[phrase]"
- "[phrase]"

**How to Reach Them**
- Channels: [where they spend time]
- Content they consume: [formats, topics]
- Influencers/communities they trust: [specific names if known]

Persona Anti-Patterns

• Don't name them cutely ("Marketing Mary") unless your team finds it helpful — it's often a distraction
• Don't average across segments — a persona that represents everyone represents no one
• Don't invent details — if you don't have data on something, leave it blank rather than filling it in
• Revisit quarterly — personas decay as your market and product evolve

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Deliverable Formats

Depending on what the user needs, offer:

1. Research synthesis report — themes, quotes, patterns, and implications
2. VOC quote bank — organized verbatim quotes by theme, for use in copy
3. Persona document — 1-3 personas built from the research
4. Jobs-to-be-done map — functional, emotional, and social jobs by segment
5. Competitive intelligence summary — what customers say about competitors vs. you
6. Research gap analysis — what you still don't know and how to find it

Ask the user which deliverable(s) they need before generating output.

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Questions to Ask Before Proceeding

If context is unclear:

1. What's the goal? Improve messaging? Build personas? Find product gaps? Understand churn?
2. What do you already have? (transcripts, surveys, tickets, G2 reviews, nothing)
3. Who is the target segment? (all customers, a specific tier, churned users, prospects who didn't buy)
4. What's your product? (if not in the product marketing context file)
5. What do you want delivered? (synthesis report, persona, quote bank, competitive intel)

Don't ask all five at once — lead with #1 and #2, then follow up as needed.

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Related Skills

---

Supporting file: evals/evals.json

{
  "skill_name": "customer-research",
  "evals": [
    {
      "id": 1,
      "prompt": "I have 20 customer interview transcripts. Help me analyze them.",
      "expected_output": "Should check for product-marketing.md first. Should ask about the goal before analyzing (improve messaging, build personas, find product gaps, etc.). Should apply the extraction framework: jobs to be done, pain points, trigger events, desired outcomes, language/vocabulary, alternatives considered. Should recommend clustering by theme, frequency + intensity scoring, and identifying money quotes. Should ask which deliverable is needed.",
      "assertions": [
        "Checks for product-marketing.md",
        "Asks about the goal before diving in (improve messaging, build personas, find gaps, etc.)",
        "Mentions extracting jobs to be done, pain points, and desired outcomes",
        "Suggests organizing quotes by theme",
        "References frequency and intensity scoring",
        "Asks which deliverable is needed"
      ],
      "files": []
    },
    {
      "id": 2,
      "prompt": "I want to do ICP research but I don't have any customer interviews yet.",
      "expected_output": "Should check for product-marketing.md first. Should recommend digital watering hole research as a starting point. Should mention Reddit, G2, Capterra, forums, or niche communities as sources. Should offer to plan a research approach and explain what to extract from online sources. Should note this is Mode 2 and ask what product/category to research.",
      "assertions": [
        "Checks for product-marketing.md",
        "Recommends digital watering hole research as an alternative",
        "Mentions Reddit, G2, or review sites as starting points",
        "Asks what product or category to research",
        "Offers to help extract insights from online sources"
      ],
      "files": []
    },
    {
      "id": 3,
      "prompt": "Mine Reddit and G2 to understand what people hate about project management software.",
      "expected_output": "Should check for product-marketing.md first. Should identify relevant subreddits (r/projectmanagement, r/productivity, r/agile) and search strategies. Should recommend reading 3-star and 1-star G2 reviews and competitor 4-star reviews. Should plan to extract verbatim quotes, pain themes, and switching triggers. Should apply the extraction table (source, quote, context, sentiment, theme tag, profile signals).",
      "assertions": [
        "Checks for product-marketing.md",
        "Identifies relevant subreddits or search strategies for project management",
        "Suggests reading 3-star and 1-star G2 reviews",
        "Recommends competitor 4-star reviews for buried complaints",
        "Plans to extract verbatim quotes and pain themes",
        "Mentions what to look for: complaints, workarounds, switching triggers"
      ],
      "files": []
    },
    {
      "id": 4,
      "prompt": "Build me a customer persona for a marketing manager at a B2B SaaS company.",
      "expected_output": "Should check for product-marketing.md first. Should ask if there is existing research to build from before generating a persona. Should warn against inventing details without data. Should use the persona structure: profile, primary JTBD, trigger events, top pains, desired outcomes, objections, alternatives, key vocabulary, how to reach them. Should note that personas should be built from at least 5-10 data points.",
      "assertions": [
        "Checks for product-marketing.md",
        "Asks if there is existing research to build from before inventing details",
        "Warns against creating personas without data",
        "Includes jobs to be done, pains, triggers, and desired outcomes in persona structure",
        "Mentions the need to capture actual customer vocabulary",
        "Notes minimum data threshold (5-10 data points)"
      ],
      "files": []
    },
    {
      "id": 5,
      "prompt": "I have 6 months of customer support tickets. What insights can I pull from them?",
      "expected_output": "Should check for product-marketing.md first. Should recommend categorizing tickets before analyzing (bugs vs. confusion vs. feature requests vs. expectation mismatches). Should warn against treating all tickets as equal signal. Should suggest extracting recurring language, patterns, and 'I wish it could…' phrases. Should ask about the goal — product improvement, messaging, reducing support load, or something else.",
      "assertions": [
        "Checks for product-marketing.md",
        "Recommends categorizing tickets before analyzing (bugs vs confusion vs feature requests)",
        "Warns against treating all tickets as equal signal",
        "Mentions extracting recurring language and patterns",
        "Asks about the goal — product improvement, messaging, or something else"
      ],
      "files": []
    },
    {
      "id": 6,
      "prompt": "What are customers saying about my competitors on review sites?",
      "expected_output": "Should check for product-marketing.md first. Should ask which competitors to research. Should recommend G2 and Capterra as primary sources. Should specifically call out reading competitor 4-star reviews for buried complaints. Should describe what to extract: what they love (battlecard intel), what frustrates them (opportunities), unmet needs. Should use the review mining template.",
      "assertions": [
        "Checks for product-marketing.md",
        "Recommends reading competitor 4-star reviews specifically for buried complaints",
        "Mentions G2 or Capterra as sources",
        "Describes what to extract: what they love, what frustrates them, unmet needs",
        "Frames as competitive intelligence input"
      ],
      "files": []
    },
    {
      "id": 7,
      "prompt": "Help me do voice of customer research for a new SaaS in the HR space.",
      "expected_output": "Should check for product-marketing.md first. Should ask about the specific ICP segment within HR (recruiter, HR generalist, CHRO, etc.). Should suggest relevant digital watering holes: r/humanresources, r/recruiting, HR Slack communities, G2 HR category, LinkedIn. Should plan to extract verbatim language for copy use. Should offer to produce a VOC quote bank as a deliverable.",
      "assertions": [
        "Checks for product-marketing.md",
        "Asks about target ICP segment within HR",
        "Suggests relevant digital watering holes (subreddits, G2 categories, communities)",
        "Plans to extract verbatim language for copy use",
        "Mentions organizing findings into a VOC quote bank"
      ],
      "files": []
    },
    {
      "id": 8,
      "prompt": "I want to understand why customers churn. I have exit survey results.",
      "expected_output": "Should check for product-marketing.md first. Should recommend segmenting churn reasons before analyzing — do not average across different causes. Should suggest pairing open-ended responses with quantitative data. Should ask if win/loss interview data or support tickets are also available. Should apply confidence labels (high/med/low) based on sample size and source consistency.",
      "assertions": [
        "Checks for product-marketing.md",
        "Recommends segmenting churn reasons before analyzing",
        "Warns against averaging across different churn causes",
        "Suggests pairing open-ended responses with quantitative data",
        "Asks if win/loss interview data is also available"
      ],
      "files": []
    },
    {
      "id": 9,
      "prompt": "Find the digital watering holes where DevOps engineers talk shop.",
      "expected_output": "Should check for product-marketing.md first. Should identify specific relevant communities: r/devops, r/sysadmin, Hacker News, DevOps-focused Discord/Slack groups, LinkedIn, Stack Overflow. Should suggest what to search for in those communities. Should describe what signal to extract from each source type and reference source-guides.md for detailed playbooks.",
      "assertions": [
        "Checks for product-marketing.md",
        "Mentions specific relevant communities (r/devops, Hacker News, LinkedIn, Discord)",
        "Suggests what to search for in those communities",
        "Describes what signal to extract from each source type"
      ],
      "files": []
    },
    {
      "id": 10,
      "prompt": "Turn my customer research into messaging I can use on my homepage.",
      "expected_output": "Should check for product-marketing.md first. Should extract VOC language and top themes before moving to copy. Should identify the highest-signal quotes and language patterns. Should produce a VOC summary or quote bank, then hand off to the copywriting skill for the actual copy writing step rather than writing homepage copy directly.",
      "assertions": [
        "Checks for product-marketing.md",
        "Extracts the VOC language and themes first before jumping to copy",
        "Identifies the highest-signal quotes for messaging",
        "References the copywriting skill for the actual copy writing step"
      ],
      "files": []
    },
    {
      "id": 11,
      "prompt": "I run a mobile fitness app and want to understand why users drop off after week 2.",
      "expected_output": "Should check for product-marketing.md first. Should recognize this as a B2C research scenario. Should suggest B2C-appropriate sources: app store reviews (1-3 star), Reddit fitness communities, YouTube comment sections on fitness apps, TikTok/Instagram comments. Should also recommend in-app surveys and analyzing support tickets/reviews. Should frame around activation and habit formation research.",
      "assertions": [
        "Checks for product-marketing.md",
        "Recognizes this as a B2C research scenario",
        "Suggests app store reviews as a primary source",
        "Mentions Reddit or community sources relevant to fitness/consumer apps",
        "Frames around understanding drop-off triggers and desired outcomes"
      ],
      "files": []
    },
    {
      "id": 12,
      "prompt": "I have no existing research and don't know who my best customers are yet.",
      "expected_output": "Should check for product-marketing.md first. Should treat this as a bootstrap research scenario. Should recommend starting with hypothesis formation before gathering data. Should suggest a minimum viable research plan: 5-10 customer interviews + digital watering hole scan. Should provide interview recruiting tips and what questions to ask. Should warn against building personas before collecting any data.",
      "assertions": [
        "Checks for product-marketing.md",
        "Recognizes this as a zero-research bootstrap scenario",
        "Recommends forming hypotheses before gathering data",
        "Suggests a minimum viable research plan (interviews + online sources)",
        "Warns against building personas without any data"
      ],
      "files": []
    }
  ]
}

---

Supporting file: references/source-guides.md

Customer Research — Source Guides

Detailed, source-by-source playbooks for gathering customer intelligence from online watering holes.

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Reddit Research

Finding the Right Subreddits

Start by identifying where your ICP spends time, not where your product is discussed.

Discovery methods:

• Search site:reddit.com "[job title] tools" or site:reddit.com "[problem category] software"
• Use subreddit search tools (https://www.reddit.com/subreddits/search) with problem-space keywords
• Look at what subreddits show up in Google results when you search ICP problems
• Check what subreddits competitors' customers mention in reviews

Common high-value subreddits by category:

• B2B SaaS: r/sales, r/marketing, r/entrepreneur, r/startups, r/smallbusiness
• Dev tools: r/programming, r/devops, r/webdev, r/cscareerquestions
• Analytics/data: r/analytics, r/dataengineering, r/BusinessIntelligence
• Marketing: r/PPC, r/SEO, r/emailmarketing, r/content_marketing
• HR/recruiting: r/recruiting, r/humanresources, r/jobs
• Finance/ops: r/accounting, r/financialplanning, r/projectmanagement

Search Operators

site:reddit.com/r/[subreddit] "[keyword]"
site:reddit.com "[problem]" "recommend" OR "suggestion" OR "alternative"
site:reddit.com "[competitor name]" "vs" OR "alternative" OR "switched"

What to Look For

High-signal post types:

• "What tools do you use for X?" → reveals alternatives and vocab
• "Frustrated with [competitor], looking for alternatives" → reveals pain and switching triggers
• "How do you handle X?" → reveals workflow and workarounds
• "Is [your category] worth it?" → reveals objections and evaluation criteria
• Complaint threads about competitors → reveals gaps you might fill

What to extract:

• The exact problem described in the post
• Top-voted solutions (what do practitioners actually recommend?)
• Complaints about existing solutions in comments
• The language used — note specific words and phrases
• Upvote patterns — consensus vs. controversy

Tools

• Reddit's native search (limited but fast)
• Google: site:reddit.com [query] (better results)
• Pullpush.io — search archived Reddit posts (good for older threads)

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G2 and Review Site Mining

Your Own Product Reviews

Read in this order for maximum signal:

1. 3-star reviews — these are the most honest. Customer liked it enough to stay but felt something was missing.
2. 1-star reviews — understand the failure modes. Separate product issues from support/onboarding issues.
3. 5-star reviews — extract the "what they love" language. These are your proof points.
4. 4-star reviews — often contain "the only thing I wish…" buried in praise.

What to extract:

• What they say they use it for (the job to be done)
• What they say is hardest or most frustrating
• What they compare it to ("coming from [X]", "better than [Y]")
• Industry and role signals in reviewer profiles

Competitor Reviews on G2

The 4-star competitor reviews are gold — customers who like the product but still have complaints.

G2 structure to exploit:

• "What do you like best?" → their strengths (your battlecard intel)
• "What do you dislike?" → their weaknesses (your opportunities)
• "What problems are you solving?" → the job to be done

Capterra has similar structure. Trustpilot skews B2C. AppSumo reviews are useful for SMB/prosumer SaaS.

Review Mining Template

For each competitor's 4-star reviews, extract:

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Indie Hackers and Product Hunt

Indie Hackers

Strong signal for founder/builder/SMB ICP.

Where to look:

• "Ask IH" posts: questions about problems your product solves
• Milestone posts: when founders describe their stack, they reveal tool preferences and pain
• Comment threads on product launches in your category

Search: site:indiehackers.com "[problem]" or use IH's native search.

Product Hunt

Discussion tabs on competing products are a research goldmine:

• Questions asked = pre-sales concerns = objections
• Comments = early adopter reactions = leading indicators of reception
• "Alternatives to X" collections reveal the competitive landscape as users see it

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Hacker News

Strong signal for technical/developer ICP. Skews toward builders and skeptics.

High-value searches:

• site:news.ycombinator.com "[competitor or category]"
• HN "Ask HN: best tools for X" threads
• "Show HN" posts for competitors — read the skeptical comments

What's different about HN:

• Users are more likely to critique underlying architecture and business model
• Strong opinions about pricing models (especially anything subscription-based)
• First principles objections you might not hear elsewhere

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LinkedIn Research

Posts and Comments

Search for posts by practitioners describing their workflows:

• "[Role] at [company size]" + problem keyword
• "We used to [old way] but now we [new way]" stories
• Posts asking for tool recommendations get comments from active buyers

Job Postings

A job posting is a company's admission of a pain point.

What to look for:

• What tools are listed as "nice to have" vs. "required"? (reveals stack and adjacent tools)
• What metrics and outcomes are mentioned in the role description?
• What does the role spend most of its time doing? (reveals the job to be done)

Search: site:linkedin.com/jobs "[role title]" "[relevant tool or category]"

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YouTube Comments

Finding High-Signal Videos

• Tutorial videos for problems your product solves
• "Best tools for X in [year]" roundup videos
• Competitor product demos and walkthroughs

What to look for in comments:

• "Does this work for [specific use case]?" → edge cases and unmet needs
• "I tried this but…" → failure points
• "What about [competitor]?" → active evaluation
• Timestamps with questions → confusion points in the workflow

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Twitter / X Research

Search Operators

"[competitor]" -filter:replies min_faves:10
"[problem keyword]" "anyone know" OR "recommend" OR "alternative"
"[category] is broken" OR "frustrated with [category]"

What to Find

• Real-time complaints about competitors
• Practitioners discussing their stack
• Influencers/thought leaders your ICP follows (useful for distribution)

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Blog Post and Forum Research

Comparison Content

Google: "[competitor 1] vs [competitor 2]" or "best [category] software [year]"

Read the comments on these posts — people who find comparison content are actively evaluating. Their comments are questions your sales process should answer.

Niche Communities

• Slack communities: Many industries have public or semi-public Slack groups. Search "[industry] Slack community".
• Discord servers: Growing for developer and creator communities.
• Facebook Groups: Still strong for SMB, e-commerce, agency, and coach/consultant ICP.
• Circle/Mighty Networks communities: Check if there are paid communities in your ICP's space.

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B2C and Consumer App Research

B2C research requires different sources than B2B SaaS. Consumer buyers don't congregate on LinkedIn or G2 — they leave traces in app stores, social media, and communities built around the activity your product serves.

App Store Reviews (iOS App Store / Google Play)

One of the richest unfiltered sources for mobile/consumer products.

Read in this order:

1. 1-2 star reviews — failure modes, unmet expectations, frustration peaks
2. 3-star reviews — honest tradeoffs and "it's good but…" feedback
3. 5-star reviews — what they love in their own words (proof points and positioning)

What to extract:

• What job they hired the app to do ("I use this to…")
• The moment it stopped working for them
• What they compared it to or switched from
• Emotional language — "I love how…", "I'm so frustrated that…"

Search tip: Sort by "Most Recent" to get fresh signal, then "Most Critical" for pain themes.

Amazon Reviews (for physical products or software with Amazon presence)

Same priority order as app stores: 3-star reviews first.

G2 analog for consumer SaaS: Trustpilot, Sitejabber, and product-specific review aggregators.

Reddit Consumer Communities

B2C Reddit is highly vertical — go to the hobby/lifestyle subreddit, not the general ones.

Examples by product type:

• Fitness apps: r/running, r/loseit, r/fitness, r/MyFitnessPal
• Personal finance: r/personalfinance, r/financialindependence, r/ynab
• Productivity/notes: r/productivity, r/Notion, r/ObsidianMD
• Travel: r/travel, r/solotravel, r/digitalnomad
• Parenting: r/Parenting, r/beyondthebump, r/daddit

Search pattern: site:reddit.com/r/[community] "[app name OR problem]"

TikTok and Instagram Comments

High-signal for consumer products with visual/lifestyle appeal.

How to find signal:

• Search TikTok for "[product name] review" or "is [product] worth it"
• Watch the top 5-10 videos; read ALL comments — not just likes
• On Instagram, check tagged posts from real users (not brand posts)

What to extract:

• Questions in comments = unmet needs or unclear positioning
• "Does this work for…?" = jobs they want to hire it for
• "I switched from X" comments = switching triggers
• Complaints about price, missing features, or broken promises

YouTube Comments (Consumer)

Same approach as B2B but different video types:

• "X app honest review" or "X app after 6 months"
• "Best [category] apps [year]" comparison videos
• Unboxing or "setup" videos for hardware/physical products

Comments on review videos are especially valuable — these are people actively in the consideration phase.

Consumer Community Platforms

• Facebook Groups: Still dominant for many consumer verticals (parenting, fitness, local services, hobbies)
• Discord servers: Growing for gaming, creator tools, productivity, crypto, lifestyle communities
• Nextdoor: Useful for local service businesses
• Quora: Long-form questions reveal decision anxiety and evaluation criteria

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SparkToro (Audience Intelligence)

SparkToro is a behavioral audience research tool. Instead of mining individual posts and comments, it aggregates clickstream, search, and social data to show what your audience does at scale — what they read, watch, listen to, follow, and search for.

When to Use SparkToro vs. Manual Research

• SparkToro first when you need to understand where your ICP spends time, what content they consume, and which influencers they follow — it answers these questions in seconds with aggregated data
• Manual research first (Reddit, G2, communities) when you need raw language, exact quotes, emotional context, and the "why" behind behavior
• Best together: Use SparkToro to identify which podcasts, subreddits, and websites matter, then go mine those sources manually for voice-of-customer language

Key Queries to Run

By competitor:

• "People who follow @competitor" — reveals shared audience affinities
• "People who visit competitor.com" — shows what else they consume

By audience description:

• "People who frequently talk about [topic]" — finds audience behaviors
• "People whose bio contains [job title]" — profiles a role-based segment

By your own audience:

• "People who visit yourdomain.com" — understand your actual audience
• Compare against competitor audience profiles to find gaps

What to Extract

Source Weighting

SparkToro data is aggregated and anonymized — it shows patterns, not individual opinions. Treat it as:

• High confidence for behavioral data (what they visit, follow, search for)
• Medium confidence for demographic data (self-reported, may be incomplete)
• Not a substitute for qualitative research (doesn't capture language, emotions, or the "why")

Limitations

• Free tier: 5 reports/month, shallow results (top 5–10)
• No public API — all research done through web interface
• Skews English-language, US-centric
• Shows what audiences do, not why — pair with qualitative sources

See tools/integrations/sparktoro.md (../../../tools/integrations/sparktoro.md) for full tool details and pricing.

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Organizing Your Research

Use a simple tagging system across all sources:

Keep a running doc with columns: Source | Date | Quote | Tags | Notes

After 20-30 entries, patterns will emerge. Look for quotes that appear in multiple unrelated sources — those are your highest-confidence insights.

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Source Reliability and Confidence Scoring

Not all sources carry equal weight. Use this guide when assigning confidence labels.

Source Weighting

Confidence Labels in Practice

When presenting insights, lead with confidence:

[HIGH CONFIDENCE] Customers feel overwhelmed by manual reporting — appears in 12 of 20 interviews,
4 Reddit threads, and is the #1 complaint in 3-star G2 reviews. Consistent across SMB and mid-market.

[MEDIUM CONFIDENCE] Customers compare us to spreadsheets more than to direct competitors —
mentioned in 6 interviews and 3 Reddit threads, but not yet seen in review data.

[LOW CONFIDENCE] Enterprise buyers may have procurement concerns — mentioned by 2 interviewees
from companies 500+. Needs more signal before acting on it.

Recency Window

• Use as primary source: Data from the last 12 months
• Use with caution: 12-24 months (product and market may have shifted)
• Use only for baseline context: 2+ years old

When a theme appears consistently across old and new data, that's a durable signal worth acting on.