Turn public LinkedIn frustration into pipeline.
People write about what occupies them. A post describing a problem you solve, from a profile that fits your ICP, is a signal — the rest is noise.
Why it reveals buying intent
Someone who publicly describes a pain has already framed it as a problem worth solving. If the author owns the budget, you are reading the brief before it is written.
Where it shows up
- LinkedIn posts
- Comment threads
- Company pages
- Event activity
Detection
How to detect this signal manually.
Search on problem vocabulary, not solution vocabulary — buyers describe symptoms (“our onboarding is chaos”), not product categories.
Watch questions: “how do you handle…?” posts are the most explicit intent format on LinkedIn.
Read the comments — intent often sits in the replies, from people other than the author.
Score the author before the post: role, company size, sector.
Prefer first-person experience posts over reshared thought-leadership.
Qualification
Checklist before reaching out.
- The post describes a problem in your category, in the author's own words.
- The author or their company fits your ICP and owns or influences the budget.
- The post is recent — engagement fades within days.
- It is a personal problem or a question, not commentary on industry news.
- You can reply or reach out with something specific to what was written.
False positives
What looks like a signal but isn't.
- Engagement bait and hot takes — problem-shaped content written for reach, not from experience.
- Consultants describing their clients' problems: they are selling, not buying.
- Reshares and congratulation posts with no first-person substance.
- Old posts resurfaced by the algorithm — always check the date.
Concrete example
Turn the signal into a reason to reach out.
Signal detected
A COO posts: “Third time this month our reporting numbers don't match between tools.”
Why it's interesting
A first-person, repeated operational pain, from a budget owner — the exact profile of a pre-purchase frustration.
Possible approach angle
Saw your post on reporting mismatches — that is usually a symptom of the stack, not the team. We have mapped the three common causes; happy to share.
Automation
How Braisely watches this signal for you.
Cost in credits · 2 credits per qualified signal
Braisely monitors LinkedIn on your problem keywords, uses AI analysis to separate first-person problems and questions from noise, scores the intent, and delivers the post, the author profile and a suggested reply angle.
- Problem-keyword watch
- AI intent analysis
- Context-type filtering
- Author profile & angle
Receive this signal, scored and contextualized.
Braisely prepares the context, the reason to contact, and an approach angle. You validate opportunities before any action.