B2B audience research shouldn’t be a luxury item. If insights require a massive budget, most teams will never get them.
You don’t need a six-figure research sprint to learn what people want. The conversations you need are already public, sitting on Reddit, LinkedIn, review sites, and forums, waiting to be mined. The only thing stopping most marketers is habit and a misguided belief that market research needs an agency badge to be legitimate.
In this post, we’ll cover how to build a high-level Reddit market research engine that turns scraped insights into conversion lifts, all in an afternoon.
For the setup, exact prompts, and troubleshooting to get it running smoothly, Lucy Woolfenden (Founder/CEO of The Scale Up Collective) and growth strategist, Ronan O’Duffy unpack their Reddit scraper workflow in CXL’s live course on AI for audience research and social listening.
Table of contents
Why most B2B audience research is too slow, too fake, and too expensive
Most businesses still assume they know what customers want without talking to them. And even when customers do talk, they aren’t listening closely enough.
Research is usually treated like a big, occasional project—something you do “ when budget appears,” which means insights only land after the market has already moved.
The predictable result is that boardroom opinions end up winning arguments that real market data could settle in five minutes.
Meanwhile, people get hit with roughly 5,000 messages a day, paid media keeps getting pricier, and most B2B buyers aren’t in market at any given time. So if your message doesn’t match their lived reality right now, it just slides right off them.
The fix isn’t to run more surveys; it’s continuous listening to the conversations already happening, every week, so your messaging stays current without the heavyweight price tag.
The new rule: Insights are already public, so go take them
Public communities, like Reddit, hold objections, anxieties, desired outcomes, and competitor comparisons in plain text. And with AI, you can read more in an hour than a human team reads in a month.
AI does one main job here: scale.
It shows patterns and themes across thousands of posts. But it doesn’t replace talking to customers. It just makes those conversations smarter and cheaper because you show up with hypotheses already grounded in reality.
How to build your Reddit scraper with AI (step-by-step)
The system is refreshingly simple, and you can run it on repeat. All you need is two tools and (believe it or not) there’s zero coding background required.
Step 1. Choose an LLM that can hold your dataset
Not all models can handle large text dumps. Context window size matters because it determines how much text the model can reliably analyze at once. If the model can’t hold the data, it will fake the gaps.
Ronan recommends using leaderboards to pick a model that’s strong at language and agentic coding, then checking context limits. If your scrape is bigger than the window, split it into batches.
Step 2. Find communities worth scraping
Start by asking AI to suggest where your audience hangs out, then do a manual check to confirm and select the right ones.
Forum selection rules:
- Pick active communities: High member count and steady posting;
- Avoid spam forums: Some may look legit until you join;
- Niche can be better than larger, general subs: Smaller, focused communities are easier to analyze because the signal is cleaner.
Step 3. Prompt the LLM to write the scraper
You don’t need to write code, but you do need to describe what you want clearly.
Your prompt should include:
- Goal: What insight you’re trying to get;
- Source: Which subreddits or forums;
- Constraints: Date range, volume, keyword filters;
- Output: CSV with title and post body.
You’ll also need Reddit API credentials to authenticate the scraper. The live course walks through that setup and gives a starter prompt pack so you don’t have to invent it from scratch.
Step 4. Run it in Google Colab and iterate errors
Google Colab is free and beginner-friendly. Paste the code, run it, and if it throws errors, copy the error back into the LLM and ask it to fix. Repeat until it works. That iteration loop is the real “skill,” not the code itself. This is how non-coders ship Reddit scrapers today.
“You’re no longer held back by the coding. You’re just held back by your ideas and how fast you can execute those ideas.”
— Lucy Woolfenden
Step 5. Clean the data so AI doesn’t get confused
Raw Reddit exports contain a lot of junk you don’t need. Remove columns you don’t plan to analyze (scores, ratios, comment counts, etc.) and keep just the text.
Then upload clean CSVs into a project space in your AI tool and explain the structure once: column one is title, column two is content. Now you don’t need to re-explain every chat.
When to DIY vs pay for tools
DIY scraping is free but not always the fastest.
- Use Apify when speed matters: It has pre-built Reddit scrapers as well as for Trustpilot, Facebook groups, and more.;
- Use ParseHub when sites fight back: If cookies, popups, or login walls break your DIY script, ParseHub can simulate clicks;
- DIY when you want flexibility: Building your own means you are not blocked by tool limits.
Private groups are different. If the community is closed, you need to join. There’s no shortcut worth trusting on this one.
What happens when you listen at scale
AI research changes outcomes up and down the funnel. Wolfenden shared some real-world case study insights in the live course:
Childcare supply problem: Customers wanted older nannies. Scraped conversations and interviews showed older nannies felt nervous about tech and wanted to feel valued, not paid more. Message shift led to a 281% lift in nanny signups and fixed supply.
Credit score app onboarding: Scraping negative reviews and forum posts showed users didn’t understand credit scores and felt blocked from life goals. Rebuilt onboarding around that job and anxiety, then tripled subscriptions in a few weeks.
Energy efficiency app message pivot: Listening continuously caught a job shift from “be sustainable” to “save money on bills” during the energy crisis. Messaging evolved with customer needs instead of lagging behind it.
Clear takeaways: Do this next week
“You can reduce how many people you have to speak to by doing…quantitative research at the start.”
— Lucy Woolfenden
Here’s the simplest way to implement without overthinking:
- Start weekly: Talk to two customers or audience members every week;
- Scrape monthly: Pull public posts and reviews, then run analysis to spot new patterns;
- Validate quarterly: Use focused surveys or groups to pressure-test the patterns and refine messaging;
- Never trust AI blindly: Ask for word-for-word quotes, percentages behind claims, and spot-checks.
Scraping gets you raw data, but you still need a framework to turn it into insight. This is where you need to be disciplined about how you use AI.
“The less you ask AI to do,…the better it’s going to execute…”
— Lucy Woolfenden
Tip: Split your analysis into more focused, narrower tasks instead of one bloated mega-prompt. Cleaner inputs means fewer mistakes.
Scraping is not the end. It’s the beginning.
Forums will get noisier as AI sludge grows. The only long-term defense is better systems plus real human conversation.
So here’s the challenge:
- Stop treating research like a once-a-year event.
- Stop paying for questions your audience already answered in public.
- Build the scraper. Run the analysis. Talk to customers. Ship better tests.
If you want to level this up, CXL’s live course on Audience Research, Experimentation, and Messaging gives you the frameworks, exact setup, starter assets, and analysis templates to make your Reddit research engine scale week after week.
