# 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.