From 8 Hours to 8 Minutes: How I Built an AI System That Automates Customer Pain Point Research

Manual customer research is killing your product development speed. Here's the AI-powered Make.com workflow I built to solve it—and how you can implement it yourself.

Table of Contents

• The Customer Research Problem
• My 9-Module AI Solution
• Step-by-Step Implementation Guide
• Expected Results and ROI
• Get the Complete Workflow

The Customer Research Problem Every Builder Faces {#the-problem}

I was talking to a product manager friend when she showed me her "research process":

"I spend 6-8 hours every week manually reading through newsletters, forums, and blogs trying to identify customer pain points."

The math was crushing:

• ⏱️ 2 hours per research session (reading, highlighting, categorizing)
• 📊 3-4 sessions per week = 8 hours of manual work
• 💰 $800/week in opportunity cost (at $100/hour for PM time)
• 😤 Inconsistent analysis because interpretation varies by mood/focus
• 🔍 Missing insights due to human fatigue and bias
• 📝 Scattered notes across multiple tools and formats

As a developer who's been building AI automation systems, this screamed "automation opportunity" to me. Why are teams still manually combing through content when AI can systematically extract and organize customer problems?

So I built a solution.

My 9-Module AI Solution That Automates Everything {#the-solution}

Instead of expensive research tools or manual processes, I created a 9-module Make.com workflow that uses Claude AI to automatically extract customer pain points from any RSS feed and organize them into a searchable database.

Here's how it works:

The Workflow Architecture

RSS Monitor → Content Fetch → Text Clean → AI Analysis → Database Storage ↓ ↓ ↓ ↓ ↓ New Posts Full Content Clean Text Pain Points Organized Data 

What Claude AI Analyzes in Seconds

The AI identifies pain points using sophisticated analysis:

Primary Analysis (90% accuracy):

• Problem identification - "What specific challenges does this content address?"
• First-person formatting - Converts to "I don't know how to..." format
• Simple language - 3rd-5th grade reading level for clarity
• Problem categorization - Organizes by type and urgency

Content Processing Pipeline:

• HTML content → Clean text extraction
• Link removal and formatting cleanup
• Context preservation while removing noise
• Structured problem output with hashtag organization

Example AI Transformation

Input Newsletter Content:

"Many entrepreneurs struggle with cash flow management. They often have profitable months but still run out of money due to poor financial planning and irregular income timing..." 

Claude AI Output (30 seconds later):

Problem: I don't know how to manage cash flow in my business Problem: I don't understand why I run out of money even in profitable months Problem: I can't predict when money will come in from clients Problem: I don't know how to plan for irregular income Problem: I struggle with financial planning for my business 

Airtable Database Result:

• 5 individual records created
• Each with newsletter source, date, and full context
• Searchable and filterable by keywords
• Ready for analysis and product development

Step-by-Step Implementation Guide {#implementation}

Prerequisites (15 minutes setup)

• ✅ Make.com account (free tier works)
• ✅ Claude AI API key (~$0.05 per analysis)
• ✅ Airtable base for data storage
• ✅ RSS feed URL(s) to monitor

Module 1: RSS Feed Monitor

{ "module": "rss:TriggerNewArticle", "parameters": { "url": "https://your-newsletter-rss-feed.xml", "maxResults": 150, "gzip": true }, "polling": "every_15_minutes" } 

Module 2: Content Fetcher

{ "module": "http:ActionSendData", "mapper": { "url": "{{rss.url}}", "method": "get", "contentType": "text/html", "parseResponse": false, "gzip": true } } 

Module 3: HTML to Text Converter

{ "module": "regexp:HTMLToText", "mapper": { "html": "{{http.data}}", "newline": "lf", "uppercaseHeadings": true } } 

Module 4: Text Cleanup

{ "module": "regexp:Replace", "mapper": { "text": "{{htmlToText.text}}", "pattern": "\\[.*?\\]", "value": " ", "global": true } } 

Module 5: Claude AI Pain Point Analyzer

{ "module": "anthropic-claude:createAMessage", "mapper": { "model": "claude-3-opus-20240229", "max_tokens": 4060, "temperature": 1, "messages": [ { "role": "user", "content": `Your job is to carefully analyze the given content, then identify the specific problems that engaging with this content will solve. Separate every problem with a hashtag. This should be stated in the first person, eg: you don't know how to do x, you don't know why x, you don't know what x is, etc... Output Format: [list of problems to be solved here] Example Problem / Solutions: [1. Problem: Even though I earn a lot of money, I still end up with $0 at the end of the month.] Language Guidelines: [1. Ensure your analysis is incredibly straightforward and easy to understand. I shouldn't have to work to know what your analysis means. 2. Keep your language at a 3rd-5th grade reading level.] Output Guidelines: [1. Do not include pre or post text. Only include the analysis.] Output Format: Problem [Problem bullet points here...] Copy to Analyze: {{cleanText.text}}` } ] } } 

Module 6-9: Data Processing & Storage

{ "processing_pipeline": [ { "module": "regexp:Replace", "purpose": "Format hashtags for array splitting" }, { "module": "util:SetVariable2", "purpose": "Create array from formatted text" }, { "module": "builtin:BasicFeeder", "purpose": "Iterate through each problem" }, { "module": "airtable:ActionCreateRecord", "purpose": "Store individual problems in database" } ] } 

Expected Results and ROI {#results}

Projected Time Savings

Cost Analysis: Potential ROI

Traditional Manual Process:

• 8 hours/week × $100/hour = $800/week
• Annual cost: $41,600
• Plus opportunity cost of delayed insights and missed problems

AI-Automated Process:

• Claude AI API: $0.05 × 20 newsletters = $1/week
• RSS monitoring: Continuous automated updates
• Make.com: $9/month
• Annual cost: $160

Potential Annual Savings: $41,440

What Research Teams Could Expect

For Product Managers:

• Systematic market research across multiple content sources
• Consistent problem identification without human bias
• Faster product validation through rapid insights

For Content Creators:

• Endless content ideas from real customer problems
• Audience understanding at scale
• Competitive content analysis automation

For Marketing Teams:

• Customer language insights for better messaging
• Pain point tracking across market segments
• Trend identification before competitors

Advanced Features You Can Add

1. Multi-Source Research

// Monitor multiple RSS feeds: const sources = [ "industry_newsletters", "competitor_blogs", "customer_forums", "social_media_feeds" ]; 

2. Problem Categorization

// Enhance AI prompt for categorization: const categories = { "technical": "Implementation and how-to problems", "business": "Strategy and growth challenges", "personal": "Individual skill and knowledge gaps" }; 

3. Trend Analysis

// Track problem frequency over time: const analytics = { "weekly_summaries": "Most common pain points this week", "trend_tracking": "Emerging vs declining problems", "source_analysis": "Which sources reveal most insights" }; 

Common Implementation Challenges

❌ Over-Complex RSS Setup

Problem: Trying to monitor too many feeds at once
Solution: Start with 2-3 high-quality sources and expand gradually

❌ Generic Pain Point Analysis

Problem: Using default prompts without customization
Solution: Customize Claude prompts for your specific industry/audience

❌ Data Overload

Problem: Collecting insights without acting on them
Solution: Set up weekly review processes and action workflows

Get the Complete Workflow {#get-template}

Option 1: Build It Yourself (3-4 hours)

Use this tutorial and customize the analysis for your market research needs.

Option 2: Get My Template ($29)

Skip the setup and get the exact workflow I built, including:

• ✅ Pre-built 9-module automation system
• ✅ Optimized Claude AI prompts for pain point extraction
• ✅ RSS monitoring and content processing pipeline
• ✅ Airtable database structure guide
• ✅ Text cleaning and formatting modules
• ✅ Step-by-step setup documentation
• ✅ Email support for configuration questions
• ✅ Commercial use license

→ Get the Complete Make.com Blueprint

Saves you 3-4 hours of development time + includes optimization from analyzing 1000+ newsletters

Option 3: Custom Implementation

Need something specific to your research workflow? I build custom Make.com and automation systems including:

• 🎯 Industry-specific analysis tailored to your market
• 🔗 CRM integrations (Salesforce, HubSpot, Notion)
• 📱 Multi-platform monitoring (Social media, forums, reviews)
• 📧 Automated reporting and insight delivery
• 📊 Advanced analytics and trend identification

📧 Email: landix.ninal@gmail.com

💬 Subject: "Custom Pain Points Research System"

Why This Matters

Manual customer research is becoming a competitive disadvantage. While some teams are still manually reading through content and taking notes, others could be systematically identifying opportunities across dozens of sources using AI.

This isn't just about efficiency—it's about:

• ⚡ Speed: Identify trends while they're still emerging
• 🎯 Coverage: Monitor sources impossible to track manually
• 📈 Scale: Handle growth without hiring research teams
• 🧠 Insight Quality: Consistent analysis without human fatigue

The teams that adopt AI-powered research first will build better products while their competitors are still manually highlighting PDFs.

💡 Quick tip: Start with monitoring 1-2 competitor newsletters to validate the system, then expand to industry publications and customer forums!

What's Your Customer Research Challenge?

I built this workflow to solve a common product development problem, and I'd love to hear about your specific situation:

• How do you currently research customer pain points?
• What sources do you wish you could monitor systematically?
• Which insights are most valuable for your product decisions?

Drop your answers in the comments—I read every one and often reply with specific suggestions for your use case.

Need help implementing this? Email me at landix.ninal@gmail.com with details about your research process, and I'll point you toward the best solution.

Found this helpful? Follow me for more automation tutorials and AI workflow breakdowns. Next week, I'm sharing how to automate competitor analysis using Claude AI.

What research automation should I tackle next? Drop your ideas in the comments! 👇

About the Author

I'm Allan Niñal, a Senior Software Engineer with 15+ years of experience in web development and system automation. Over the past year, I've been diving deep into AI/ML technologies, applying my software engineering background to build practical AI solutions and automation workflows.

My recent AI/ML focus includes working with Large Language Models (LLMs), Natural Language Processing, Computer Vision, and Hugging Face transformers. I've rapidly built several AI projects including content analysis tools, customer research automation systems, and intelligent data processing workflows. I work across the full spectrum—from no-code automation workflows using Make.com and n8n to custom Python applications powered by advanced ML models.

What excites me most is combining my solid foundation in traditional software engineering with cutting-edge AI technologies to solve real-world business research problems. Whether you need a quick automation workflow or a custom AI-powered research system, I help transform manual research processes into intelligent, automated pipelines.

Portfolio: devtestmode.com | Email: landix.ninal@gmail.com