# Customer Research — Source Guides

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

---

## 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)

---

## 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:

| Category | Notes |
|----------|-------|
| Job to be done | Why do they use the product? |
| Top praise | What do they love (and might be hard for you to match)? |
| Top complaint | What frustrates them? |
| Switching context | Did they mention switching from something else? |
| Unmet need | "I wish it could…" or "It would be better if…" |

---

## 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

---

## 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

---

## 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]"`

---

## 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

---

## 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)

---

## 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.

---

## 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

---

## 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

| Data Type | What It Tells You | Use It For |
|-----------|------------------|------------|
| Top websites visited | Where your audience reads | Content partnerships, guest posting targets |
| Top podcasts | What they listen to | Podcast guesting, sponsorship decisions |
| Top YouTube channels | What they watch | Video content strategy, ad placements |
| Top subreddits | Where they discuss | Community participation, Reddit ad targeting |
| Search keywords | What they Google | SEO and content topic planning |
| AI prompt topics | What they ask AI tools | Emerging content opportunities |
| Social accounts followed | Who influences them | Influencer partnerships, co-marketing |
| Demographics | Who they are | Persona building, ad targeting |

### 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.

---

## Organizing Your Research

Use a simple tagging system across all sources:

| Tag | Meaning |
|-----|---------|
| `#pain` | A problem or frustration |
| `#trigger` | An event that prompted the search |
| `#outcome` | What success looks like |
| `#language` | Exact phrases worth using in copy |
| `#alternative` | Another solution they considered or use |
| `#objection` | Reason to hesitate or not buy |
| `#competitor` | Anything about a competing product |

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.

---

## Source Reliability and Confidence Scoring

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

### Source Weighting

| Source | Signal Strength | Bias to Note |
|--------|----------------|--------------|
| Customer interviews (unprompted) | Very high | Small sample; selection bias toward engaged customers |
| Win/loss interviews | High | Recent memory only; rationalization common |
| App store / G2 reviews | High | Skews toward strong opinions (love or hate) |
| Reddit / community posts | Medium-high | Skews technical, skeptical, vocal minorities |
| Support tickets | Medium | Skews toward problems; silent majority not represented |
| Survey (open-ended) | Medium | Primed by question framing |
| Survey (multiple choice) | Low-medium | Artifacts of the options you provided |
| NPS verbatims | Medium | Correlates with score; prompted by the survey moment |
| YouTube/TikTok comments | Medium | Skews toward engaged viewers; social performance |
| SparkToro audience data | Medium-high | Aggregated behavioral data; strong for "what" but not "why" |
| Job postings | Low-medium | Aspirational, not necessarily reflective of current pain |

### 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.
