How to extract real voice of customer data from Reddit (without ever doing an interview)

You've seen the promises: AI will “revolutionize” your customer research, generating deep insights in seconds. But when you dive into the results, disappointment hits. The insights feel thin, lifeless—like something's missing.

It's not just you. Recently, Matt Lerner from SYSTM talked about this exact problem. In his experiment (Can Chat GPT replace customer interviews?), ChatGPT could generate some useful themes, but it couldn't capture real emotional nuance. It just didn't get the deeper human struggles, fears, and frustrations hiding behind customer behaviors.

Sound familiar? If your insights have ever felt a little soulless, you're not alone. The truth is, it can't give us the full picture—at least, not without the right fuel.

But here's the surprising thing... that fuel is out there, waiting for you. It's hiding in the raw, unfiltered conversations your customers are having every day—on Reddit.

Reddit is where customers bare their souls (and I'm taking notes)

While ChatGPT and other AI aren’t great at generating emotional depth, they’re fantastic at organizing it—if you give them the right raw material. And Reddit just happens to be the perfect place to find those honest, vulnerable conversations.

Think about it: on Reddit, people open up in ways they rarely do elsewhere. Anonymity lets them share their frustrations, admit failures, and be blunt about why things aren’t working. They talk about how they feel when tools fall short, when businesses fail to deliver, and when self-doubt creeps in during their journey.

If we can capture these stories as they’re said, AI can help us take those raw emotions and find real patterns.

AI works best when you feed it the customer’s exact words

Here’s the mistake most people make: they summarize the customer’s pain. They paraphrase what they’re hearing, stripping away the emotional truth behind it. The simplifications, and neat summaries sanitize the rawness—the heart—of what people are saying.

If you want AI to do its best work organizing customer data, you need to capture exactly what they’re saying, verbatim. That means the emotional language, the pain-filled context, and the specific frustrations that give the story weight. When you feed it real stories, it can recognize and organize real emotional patterns that you'd miss otherwise.

Your job? Forget neat data points. Start collecting messy emotions.

Let’s run through an example to show you what I’m talking about.

Imagine you’re browsing Reddit and come across this thread from an entrepreneur on r/entrepreneur:

“I started this business with high hopes, thinking it would solve my financial problems, but here I am, months later, and I feel like I’m barely holding it together. Every time I try a new scaling tactic or invest in another SaaS platform, I end up having to explain to my wife why we’re short on rent... again. The stress is killing me, and I’m starting to wonder if I’m just not cut out for this.”

Don’t reduce this problem to “customer struggles with scaling.” That’s not their real pain.

Here’s how you extract the verbatim emotional insights:

• Pain point: “I end up having to explain to my wife why we’re short on rent… again.”
• Desired outcome: “I just want to scale and stop stressing about how to pay the bills.”
• Worldview: “I’m starting to wonder if I’m just not cut out for this.”
• Alternatives tried: “I’ve tried scaling tactics and invested in several SaaS platforms.”

Notice how this isn’t just about “scaling a business.” It’s about dealing with failure, self-doubt, and the emotional burden of letting down family—all of which AI can organize... if you give it this full emotional story.

Build each story out: don’t rush to aggregate data

At this point, you should have multiple raw, real stories built around exact quotes from real people. But don’t skip straight to finding patterns across it.

Instead, start by generating individual reports based on each Reddit user’s journey. Be sure to collect threads that are similar in nature. For example, my company is interested in how sales people follow-up. Dropping the "follow-up" keyword in the r/sales subreddit gives me deep, emotionally rich conversations.

Additionally, capturing personal journeys one-by-one makes sure you respect each customer’s story—rather than lumping emotions together too quickly.

For each report, capture:

• The task they were trying to accomplish
• The emotional context (their life situation or trigger moments)
• What pain points they were struggling with
• Their desired outcome—not just what they want, but what emotional relief they’re after
• Their worldview or beliefs about why things should work a certain way
• Which alternatives they’ve tried (and how those failed them, what were the consequences?)

Now, let AI spot the patterns hiding in those emotional stories

With your individual reports ready, it's time to put AI to work and aggregate.

TypingMind's (Or Claude’s) projects feature is perfect for this. Create a new project, then add each customer story as a separate document. In your system prompt ask it to always reference the research data provided in its answers.

This lets you feed emotionally rich, genuine customer data into Claude, taking full advantage of its ability to organize and synthesize insights at scale.

Because AI now has the customer’s literal words—unfiltered and detailed—it can properly identify emotional pain points and the common threads that connect the examples you’ve provided.

Don’t let it wander: give it clear rules to follow

Of course, AI isn’t magic. To get meaningful insights out of this process, you need to give your analysis structure. Be specific in your instructions and guide the AI as it processes the stories.

For example, tell the AI:

• Group customers based on similar emotional pain, not just tasks or superficial goals
• Identify recurring emotional frustrations about failed alternatives
• Pinpoint common themes, especially related to the pain points they feel.

When you have exact customer words leading the way, AI becomes effective at recognizing these patterns—and the insights become incredibly valuable.

Ground your insights in customer motivations, not just emotions

Once it has helped you organize the larger emotional patterns, go deeper by grounding those insights in customer motivations. This is where Matt Lerner’s demand-side prompts come in.

Think of these as the final step to turning emotional data into practical, actionable insights. Matt’s prompts force you to investigate what triggered a customer decision, what anxieties were at play, and which emotional failures drove them to abandon a product or service.

It helps you think through demand rather than your product/service (supply).

For instance, once AI recognizes customers repeatedly expressing family-related pressures, you could apply Matt’s question: "What are some situations or triggers that might have prompted them to start looking for ways to [achieve outcome]?"

By layering these kinds of questions over your AI-generated patterns, you ground the emotional data in a direction that leads to meaningful action.

With a little bit of work, you can set up an engine for customer understanding.

Stop waiting for AI to ‘get’ your customers. Find their raw stories yourself.

If you’ve ever felt let down by the emotional thinness of AI insights, here’s why: it can’t invent emotional depth. But it can organize it—if you give it the right raw material.

Start with real human stories—the ones where customers aren’t holding back, messy and emotional. When you capture their feelings as they happen, AI becomes the best tool for organizing those emotions into actionable insights. Don’t ask AI to generate empathy. Feed it what people are already saying.

What’s next: Capture, organize, and act

Here’s how you can turn this process into your next powerful insight-gathering technique:

1. Search relevant subreddits for raw emotional stories from your audience.
2. Extract verbatim emotional quotes—don’t summarize.
3. Build individual reports to preserve each person’s unique story.
4. Load those reports into Claude (via TypingMind or your preferred tool), and let AI start laying out the emotional patterns.
5. Finally, apply Matt Lerner’s prompts to ground your insights in actionable outcomes and customer decisions.

The key to making AI useful? Give it real human truth.

AI can’t pull human emotions out of thin air. But when you dive into corners of the internet like Reddit, where people are being honest about their struggles, you can build a source of emotional truth that AI can organize and make sense of.

This means feeding it raw emotional stories—not clean, convenient summaries—and then asking the AI tool to connect those stories into bigger patterns. When you do that, you’re not just gathering data anymore. You’re collecting understanding—empathy-driven insights that will help you connect deeply with your customers.