skills/customer-research/references/source-guides.md
Detailed, source-by-source playbooks for gathering customer intelligence from online watering holes.
Start by identifying where your ICP spends time, not where your product is discussed.
Discovery methods:
site:reddit.com "[job title] tools" or site:reddit.com "[problem category] software"Common high-value subreddits by category:
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"
High-signal post types:
What to extract:
site:reddit.com [query] (better results)Read in this order for maximum signal:
What to extract:
The 4-star competitor reviews are gold — customers who like the product but still have complaints.
G2 structure to exploit:
Capterra has similar structure. Trustpilot skews B2C. AppSumo reviews are useful for SMB/prosumer SaaS.
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…" |
Strong signal for founder/builder/SMB ICP.
Where to look:
Search: site:indiehackers.com "[problem]" or use IH's native search.
Discussion tabs on competing products are a research goldmine:
Strong signal for technical/developer ICP. Skews toward builders and skeptics.
High-value searches:
site:news.ycombinator.com "[competitor or category]"What's different about HN:
Search for posts by practitioners describing their workflows:
A job posting is a company's admission of a pain point.
What to look for:
Search: site:linkedin.com/jobs "[role title]" "[relevant tool or category]"
What to look for in comments:
"[competitor]" -filter:replies min_faves:10
"[problem keyword]" "anyone know" OR "recommend" OR "alternative"
"[category] is broken" OR "frustrated with [category]"
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.
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.
One of the richest unfiltered sources for mobile/consumer products.
Read in this order:
What to extract:
Search tip: Sort by "Most Recent" to get fresh signal, then "Most Critical" for pain themes.
Same priority order as app stores: 3-star reviews first.
G2 analog for consumer SaaS: Trustpilot, Sitejabber, and product-specific review aggregators.
B2C Reddit is highly vertical — go to the hobby/lifestyle subreddit, not the general ones.
Examples by product type:
Search pattern: site:reddit.com/r/[community] "[app name OR problem]"
High-signal for consumer products with visual/lifestyle appeal.
How to find signal:
What to extract:
Same approach as B2B but different video types:
Comments on review videos are especially valuable — these are people actively in the consideration phase.
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.
By competitor:
By audience description:
By your own audience:
| 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 |
SparkToro data is aggregated and anonymized — it shows patterns, not individual opinions. Treat it as:
See tools/integrations/sparktoro.md for full tool details and pricing.
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.
Not all sources carry equal weight. Use this guide when assigning confidence labels.
| 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 |
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.
When a theme appears consistently across old and new data, that's a durable signal worth acting on.