Methodology
How we rank demand
Thynkk is not a black box. Here is exactly what a scan does, what the numbers mean, and how to make decisions you can actually trust from the output.
What a scan actually does
No magic. Here is the full picture of what happens when you hit Scan.
50–100
Posts analysed
top posts, last 30 days
1–3
Communities
subreddits per scan
~10%
Filter pass rate
posts reach the AI
5–8
Themes returned
clustered by AI
30 days
Data window
top posts only
30–90s
Scan time
live fetch + AI
Limitations to know
— Top posts skew toward dramatic, viral complaints. Quiet frustrations that don't get upvoted won't appear.
— Comments are not currently fetched — the richest signal lives in comment threads.
— AI clustering can produce themes that sound plausible but overfit to a handful of posts. Always read the source quotes.
— Demand score is a proxy — upvotes and mentions, not credit cards. People complain about things they'd never pay to fix.
How to rely on this data
Thynkk is a strong first signal. Here is exactly when to trust it and when not to.
Reliable for
Directional validation
"Is this a real problem space?" — Yes, reliably. If Thynkk surfaces 5 themes with strong signals and real quotes, the pain exists.
Fast idea elimination
Fastest way to kill a bad idea before you write a line of code. If nobody's complaining about it, that's signal too.
Quote mining
The actual quotes are real posts from real people. Read them. They're worth more than the score.
Niche discovery
Finding that a community exists and is active around a problem — the Trend Radar is especially strong here.
Conversation starters
Use the themes to know exactly what to ask in customer interviews. The themes tell you what to probe.
Not reliable for
Market size
Thynkk can't tell you TAM. Loud Reddit complainers ≠ addressable market.
Build/no-build decisions alone
Treat it as a first conversation, not a final verdict. Combine with 5–10 customer interviews before committing.
Representative sampling
Top posts skew toward dramatic, viral complaints. Quiet frustrations that don't get upvoted won't appear.
Purchase intent
Demand score is a proxy — upvotes and mentions, not credit cards. People complain about things they'd never pay to fix.
The right mental model
Use Thynkk as your first conversation with a market — not your last. A strong scan result tells you a problem space is worth 5–10 customer interviews. A weak result tells you to move on before you waste a week. That alone is worth it.
The pipeline
Every Pain Point Scanner scan follows the same five steps:
Harvest
Fetch 50–100 top posts from the last 30 days across 1–3 relevant communities. Top posts only — sorted by engagement, not recency.
Filter
Match posts against pain-point language before any AI call — cutting noise by ~90%. Only posts with frustration, request, or willingness-to-pay signals make it through.
Cluster
AI groups filtered posts into 5–8 distinct themes with representative quotes. Each theme gets a name, summary, opportunity assessment, and next step.
Score
Demand score, severity, verdict, and willingness-to-pay are calculated and themes are ranked highest to lowest.
Present
Ranked report with source links — every quote is traceable back to the original post.
We filter before we call AI. That keeps cost sane and stops generic posts from diluting your report.
Demand score
Demand is a comparative ranking within a scan — not an absolute market size. A score of 94 means this theme ranked highest in this report, not that 94% of the internet wants it.
mention_count
How many posts in the scan cluster into this theme. Assigned during AI clustering — themes with more recurring language score higher.
log(1 + upvotes + comments)
Engagement on source posts linked in the theme's quotes. Log-scaled so one viral post does not dominate the ranking.
recency_weight
Recent pain matters more. Weight decays linearly over 12 months: a post from today scores 1.0; a post from a year ago scores ~0.0. Older posts floor at 0.1.
Other fields
Severity (1–10)
AI-assessed based on emotional intensity, how often the problem appears, and how much it disrupts the person's work or life. Higher = louder, more urgent pain.
Verdict
Strong signal — recurring frustration, real build opportunity. Weak signal — people vent but unlikely to pay. Already crowded — pain is real but incumbents exist and satisfaction is adequate.
Willingness to pay
High, Medium, or Low — based on money language in posts: workarounds people already pay for, time wasted, or phrases like 'I'd pay for' and 'shut up and take my money.'
Competition
Whether an obvious existing tool is named in the posts, and what gap remains. 'No obvious tool' is a signal; 'FreshBooks handles this' is a warning.
Next step
One concrete validation action for this week — a post to write, people to interview, or a landing page to test. Not generic advice.
Trend Radar
Mode 2 works differently. No keyword input — Thynkk pulls recent post titles across tracked subreddits (r/entrepreneur, r/indiehackers, r/SaaS, and others), then clusters emerging topics by momentum.
HOT
Highest growth velocity this week. Topic volume and engagement are spiking relative to the 7-day window.
RISING
Steady upward momentum — not yet peaked, but gaining posts and attention.
NEW
First appearance in our radar window. Early signal — higher novelty, less historical data.
Trend Radar niches are sorted by tag priority (HOT → RISING → NEW), then by growth percentage within each tier. Run a Pain Point Scanner on the subreddit to go deeper on any niche.
What we do not publish
Transparency does not mean giving away the recipe. We keep private:
- Exact pain-phrase filter lists (tuned constantly)
- Full AI prompts and clustering instructions
- Data source and infrastructure details
The formula and field definitions above are stable. The tuning behind them improves over time as we run more scans and refine quality.
See it in action
Example scan outputs with demand scores, verdicts, and source quotes.