Why AI Alone Can't Find Real Pain (But This Combo Can)

Being relevant starts with finding the real problems people feel. I share a practical problem-hunting method that combines social listening, customer conversations, AI research, and RFM prioritization.

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Most advice about being relevant on social media skips the hardest question: relevant to what? If you lead with expertise, your content becomes relevant when it meets a problem people actually feel. That sounds obvious, but it is easy to spend a week reacting to trends that have nothing to do with the pressure your audience is carrying.

Ken Freire and I call the better habit becoming a problem hunter. The job is to listen for the real pain underneath the polished question, the generic complaint, or the popular hack. A clever shortcut may address a surface problem while leaving the root problem untouched. Thought leadership gets stronger when you can name the thing people are trying to solve in their own words.

Start where people are already talking

Social media is a useful place to build an inventory of problems. Every rant is a complaint worth noticing. Questions, unusual comments, and the way people describe failed attempts can reveal more than a polished authority post. Instead of scrolling passively, spend part of that time looking through comments and noting what people are asking, struggling with, or trying to explain.

That research does not require a complicated scoring system. I sometimes copy problems into a notes document and look for patterns. General categories can help, but the specific version matters. “Businesses need leads” is broad. The useful question is whether the problem is getting qualified people to show up, helping them say yes, or turning interest into a real conversation.

The deeper source of insight is direct conversation. Talk with customers and prospects, listen to sales calls, or invite people to a podcast and ask the same five or six questions repeatedly. After ten or fifteen conversations, look for different people describing the same pressure in different words. When someone responds, “That is exactly what I have been feeling,” you have moved closer to the problem behind the problem.

Use AI to accelerate the first pass

AI deep research can help before you schedule all those conversations. A prompt can ask ChatGPT, Gemini, or another tool to search forums, social posts, Reddit, Quora, and customer reviews for pain points in a product category. Ask for categories, frequencies, direct quotes, and links back to the original sources. A second prompt can search for questions rather than complaints.

That report is a starting point. AI can make things up, so the links and quotes are receipts to check, not permission to skip research. The report shows the surface-level language people use. Conversations reveal the nuance, consequences, and emotion underneath it. That distinction is what helps a post sound like a useful answer instead of another assumption.

Prioritize the pain that is active now

Once you have a list of possible problems, they are not all equally timely. I use an adaptation of the old direct-marketing RFM framework: recency, frequency, and monetary value. For pain points, recency comes first. The problem people experienced yesterday or are discussing today is more likely to earn attention than a serious issue that has been quiet for a year.

Frequency matters next. A recurring problem deserves more attention than an isolated frustration. The monetary dimension becomes a proxy for consequence or importance, but it comes after the pain is current and repeated. A recent change to a platform algorithm, for example, may be more relevant to marketers today than a larger but distant concern.

The combination is simple: watch what people say, talk to them directly, use AI to widen the search, verify the evidence, and prioritize the pain that is fresh and recurring. The goal is not to manufacture urgency. It is to earn relevance by doing the work required to understand what people are already trying to solve.