source-guides
Detailed, source-by-source playbooks for gathering customer intelligence from online watering holes.
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 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:
- 3-star reviews — these are the most honest. Customer liked it enough to stay but felt something was missing.
- 1-star reviews — understand the failure modes. Separate product issues from support/onboarding issues.
- 5-star reviews — extract the "what they love" language. These are your proof points.
- 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:
- 1-2 star reviews — failure modes, unmet expectations, frustration peaks
- 3-star reviews — honest tradeoffs and "it's good but…" feedback
- 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 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.