Great direct response marketing starts with a problem people already feel. Not a problem we guess they might have. Not a clever angle we made up in a brainstorming session. We want the real complaints, frustrations, and money-draining issues people are talking about right now.
That is where an expensive and painful problem, or EPP, comes in. When we identify the problems that cost our market time, money, leads, customers, or peace of mind, we can write marketing that speaks directly to what matters.
AI makes this research much faster. Instead of trying to manually search through hundreds of posts, comments, support threads, and forums, we can use AI to gather the conversations, pull out the pain points, and rank them by intensity.
Table of Contents
What Is an Expensive and Painful Problem?
An EPP is a problem that is both costly and frustrating. The cost may be direct, such as wasted ad spend, missed jobs, lost customers, or expensive repairs. It can also be indirect, such as stress, wasted time, poor reviews, or the fear of falling behind competitors.
The more painful the problem feels, the more likely people are to look for a solution. That gives us a much stronger starting point for direct response copy.
For example, a tree service company might serve property owners who need hazardous trees removed. A dead tree hanging over a roof is not a minor concern. The property owner may worry about damage, insurance issues, family safety, and the cost of emergency work. That is a high-intensity problem.
On the B2B side, an agency owner may complain that they are spending hours building links with little to show for it. They may be losing clients, missing deadlines, or struggling to deliver results at a profit. Again, that is more than a small annoyance. It is a business problem with a real cost.
Our job is to find the language people use when they describe these problems. Then we can build offers and campaigns around solutions that make sense.
Mine Complaints From the Right Places
We can use the same basic method for both business-to-consumer and business-to-business marketing. The main difference is where we look and who we research.
B2C: Research Consumer Problems
If we are targeting consumers, we want to find public conversations from the people who may buy from our clients. This can include:
- Social media posts and comments
- Reddit discussions
- Quora questions
- Local community groups
- Review sites and complaint threads
- Industry forums where consumers ask for help
For a home service contractor, we might search for conversations about emergency repairs, bad contractor experiences, confusing estimates, long wait times, damage concerns, or problems people could not fix on their own.
The point is not to collect random keywords. We want to find the words people use when they are upset, worried, stuck, or spending money on a problem.
B2B: Research Business Owner Problems
For B2B campaigns, we research business owners, agencies, consultants, and other professionals. Their pain points may show up in industry groups, software support forums, Facebook groups, Reddit communities, and product discussions.
Contractors, for example, may discuss problems in communities tied to tools such as ServiceTitan, Housecall Pro, or Jobber. They may complain about scheduling issues, no-show leads, rising software costs, poor close rates, hiring problems, or not knowing which marketing is working.
These conversations are gold because they reveal what the market already cares about. We do not have to invent a problem. We simply need to listen, organize the data, and connect our offer to the pain.
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Use Real-Time Conversations With Grok and X
One of the strongest parts of this approach is using Grok and X for research. Because Grok can access real-time conversations on X, it can help us find discussions as they happen.
That matters because pain points change. A complaint that was common six months ago may not be the biggest issue today. Real-time research helps us spot current concerns, fresh wording, and active conversations.
We use a custom process called X Multiplier to research complaints and identify EPPs. For contractor campaigns, we also refer to this as a Demand Interception System. The idea is simple: find demand that already exists and create messaging that meets people where their problem is happening.
We begin by telling the AI model what market we want to research. It could be a local service, a type of business owner, a company name, or a broad topic. The AI can then gather relevant conversations and help us spot the repeated complaints.
For example, we could research “AI integration for home service contractors.” The output may show repeated concerns around missed calls, slow response times, lead follow-up, staff workload, and uncertainty about how to use AI without creating more work.
Once we know the recurring pain, we have a better foundation for ads, landing pages, emails, sales scripts, and offers.
A Simple Starting Point: The Last 30 Days Skill
If we do not have a custom research system, we can still get started with a GitHub skill called last-30-days-skill. A quick search for “last 30 days skill GitHub” should bring it up.
This skill is designed to search for online conversations around a topic from the last 30 days. Depending on how it is set up, it can use several data sources. Some options may require API keys, while other research can be done without them.
The workflow is simple:
- Install the skill in the AI environment we use.
- Trigger the skill by typing the command for it.
- Enter the topic, company, market, or search query we want to research.
- Review the detailed output file it creates.
- Send that output back through an AI model for pain-point analysis.
We might enter a company name to see what people have said about that company recently. Or we could enter a service and location, such as tree service in a specific city. We can also use a broader topic that relates to a market we want to target.
The output can be long, but that is fine. We are collecting raw market language. The next step is to have AI clean it up and make it useful for marketing.
Turn Raw Research Into Ranked Pain Points
A research skill can collect a lot of data, but raw data alone is not a campaign. We still need to extract the pain points and decide which ones deserve our attention.
We can attach the output file to an AI model and ask it to identify the main complaints. Then we can ask it to group similar complaints and score each one by intensity.
A simple scoring approach can look at questions such as:
- Does the problem cost the person money?
- Does it waste a lot of time?
- Does it create stress, fear, anger, or urgency?
- Does it cause lost leads, jobs, clients, or revenue?
- Are people using strong emotional language when they describe it?
- Does the problem appear again and again across many conversations?
Strong language is often a useful signal. When people sound angry, frustrated, desperate, or worried, the issue is likely more intense than a casual preference or small inconvenience.
We do not need to rely on one perfect prompt. We can use AI in steps. First, gather the data. Next, pull out the complaints. Then group them, score them, and ask the model to show the exact phrases and examples that support each finding.
This process gives us material for copy that feels specific. Instead of saying, “Get more leads,” we can speak to the deeper issue behind the complaint, such as missed calls, poor-quality leads, slow follow-up, or marketing spend that does not produce booked jobs.
Install AI Skills Safely
AI skills can make research much easier, but we should always be careful before installing anything from a public repository.
Before we install a skill, ask the AI tool to conduct a security review of the repository. This should become a normal habit.
The AI can clone or import the repository into a sandbox environment, inspect the files, and look for suspicious code, prompt injections, or other problems. Even when a skill appears safe, it is smart to review it first.
Once it passes the review, we can provide the repository URL and ask the AI environment to install it as a skill. In many cases, the tool will handle the setup for us. We may need to restart the application or add environment keys when a skill uses outside services.
That is one of the best parts of current AI tools. What used to require a lot of technical work is becoming much simpler. We can spend less time fighting with setup and more time learning what our market actually wants.
Use the Market’s Words, Not Our Assumptions
The goal is not to have AI write generic marketing for us. The goal is to use AI to uncover real market intelligence.
When we find expensive and painful problems, we can create campaigns that are more direct and more relevant. We can build offers around issues people already want solved. And we can write copy using the words they already use to explain their frustration.
That is how we move from guessing to informed direct response marketing. Find the conversations. Extract the pain. Rank the intensity. Then create messaging that clearly connects the problem to the solution.