Original data - updated June 2026
The State of SaaS Gaps
PainMiner is tracking 135 validated software opportunities across 11 categories, mined from real user complaints. Average commercial intent is 76/100, and Security / Compliance leads on commercial intent (avg 86). 84% show an explicit willingness-to-pay signal and 37% are trending up.
Validated gaps
Avg commercial intent
Willing to pay
Trending up
Category leaderboard
Ranked by average commercial intent.
| Category | Gaps | Avg CI | Avg conf | Trending |
|---|---|---|---|---|
| Security / Compliance | 5 | 86 | 83 | 100% |
| Real Estate / Local Operations | 5 | 83 | 79 | 100% |
| Marketing Operations | 15 | 78 | 83 | 33% |
| Data / Analytics | 13 | 77 | 83 | 38% |
| HR / Recruiting | 7 | 77 | 82 | 71% |
| E-commerce | 13 | 76 | 80 | 38% |
| Dev Tools / SaaS Infrastructure | 18 | 75 | 83 | 28% |
| No-Code / Automation | 29 | 74 | 82 | 17% |
| Finance / Accounting | 15 | 73 | 79 | 33% |
| Creator Operations | 10 | 72 | 82 | 50% |
| Sales / CRM | 5 | 70 | 83 | 0% |
Highest-intent gaps right now
- Bulletproof checkout and SEO for BigCommerce storesE-commerceCI 100
- Self-serve SOC 2 that skips the $50k consultantSecurity / ComplianceCI 96
- An applicant tracking system recruiters actually likeHR / RecruitingCI 95
- AI podcast editor that cuts edit time 4xCreator OperationsCI 94
- Trustworthy Meta Ads attribution after iOS 14Marketing OperationsCI 93
- E-commerceCI 92
- Data / AnalyticsCI 92
- Real Estate / Local OperationsCI 91
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Where demand concentrates
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Frequently asked questions
- How many validated SaaS gaps does PainMiner track?
- PainMiner tracks a growing corpus of validated software opportunities mined from real user complaints, each scored for commercial intent and confidence and grouped into categories. The figures on this page reflect the current approved set and refresh as the weekly pipeline runs.
- Which categories have the highest commercial intent?
- The category leaderboard on this page ranks every category by average commercial-intent score, so you can see where people are most clearly willing to pay for a fix.
- Where does this data come from?
- From public, attributable complaints (Reddit, Hacker News, app store reviews, public forums), classified by a two-pass LLM pipeline and editorially approved. See the methodology page for the full process.