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 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

B0

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

B1

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 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:

B2

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.