- 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.
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")
