My user research wasn't a survey. It was a Python script.
Before writing a single line of Swift for FlipperHelper (an iOS app for resellers to track purchases, expenses, and profits), I scraped roughly 2,000 top posts from reseller subreddits — r/Flipping, r/FlippingUK, r/ThriftStoreHauls, and about 20 others. The goal: find out what people who buy and sell second-hand goods actually complain about, ask for, and obsess over.
I'm a senior Python developer by day. My wife Valentina resells clothes and household items — around £1,300-1,900/month in turnover. I watched her track everything in Notes app entries and mental math. I wanted to build something better, but "something better" is a dangerously vague starting point.
So instead of guessing features, I let the data decide.
Why Reddit and not surveys?
Surveys have a problem: people tell you what they think you want to hear. Interviews are better but expensive and slow when you have zero audience and zero budget.
Reddit is different. People on reseller subs aren't performing for a researcher. They're venting, celebrating wins, asking genuine questions, and arguing about what works. A post titled "entry fees are killing my margins" tells you more than a survey checkbox ever could.
The total dataset ended up at around 36,000 posts from 24+ subreddits. The most useful subset was about 2,000 top-scoring posts from the most active reseller communities. High-scoring posts surface topics that resonate with the most people — a built-in signal filter.
The scraping setup
Straightforward Python with PRAW (Python Reddit API Wrapper). Pull posts, filter by top of all time and top of the year, save to CSV and JSON.
import praw
reddit = praw.Reddit(
client_id="YOUR_CLIENT_ID",
client_secret="YOUR_CLIENT_SECRET",
user_agent="research-scraper"
)
subreddits = [
"Flipping", "FlippingUK", "ThriftStoreHauls",
"Depop", "Vinted", "EtsySellers", "Ebay"
]
for sub_name in subreddits:
subreddit = reddit.subreddit(sub_name)
for post in subreddit.top(time_filter="all", limit=500):
save_post({
"subreddit": sub_name,
"title": post.title,
"body": post.selftext,
"score": post.score,
"comments": post.num_comments,
"created": post.created_utc
})
Nothing fancy. The value wasn't in the scraping — it was in reading the output and finding patterns.
What the data revealed: 4 features I wouldn't have prioritised otherwise
1. Multi-currency support
UK resellers on r/FlippingUK regularly talked about buying at French vide-greniers and European flea markets. They pay in euros, sell in pounds. Some buy from US wholesale lots priced in dollars. Not an edge case — a regular part of how many resellers operate. My wife does exactly this: buys at French markets in EUR, sells on Vinted in GBP.
2. Expense tracking (entry fees + transport)
The phrase "entry fees" came up constantly. Car boot sales in the UK charge £1-5 for buyers, more for early bird access. People wanted to know: is this market actually profitable after the entry fee and petrol? FlipperHelper tracks entry fees per market and transport costs so you see real profit per trip.
3. Days to sell
Reseller communities obsess over velocity. "How long did that take to sell?" appears in almost every haul post. Slow-moving stock ties up money and space. FlipperHelper calculates days-to-sell automatically from purchase date to sale date.
4. Multi-platform tracking (16+ services)
Nobody sells on just one platform. The same person lists on eBay, Vinted, Depop, Facebook Marketplace, Etsy, and Grailed simultaneously. Posts about "where should I list this?" were everywhere. FlipperHelper lets you tag items with any combination of platforms and see which one generates the most sales for your inventory type.
Validation vs. discovery
Some features I would have built anyway — expense tracking felt obvious. But multi-currency in v1? I wouldn't have prioritised that without the data. Days-to-sell as a prominent metric? That came entirely from the frequency of "how long did it take to sell" in post comments.
The scraping didn't just validate assumptions. It corrected some and added features I'd never have considered.
What I'd do differently
Scrape comments, not just posts. Comments are where the real detail lives — the "yes, but I also need X" replies that surface secondary features. I only scraped post data initially and had to read comments manually for promising threads.
Tag by sentiment earlier. Knowing "entry fees" appears often is useful. Knowing it appears in a negative context is much more useful — it tells you this is a pain point, not just a topic.
Cluster by user type. A full-time eBay seller and a weekend car boot reseller have different needs. Segmenting posts by user context would have helped prioritise features for the right audience.
The result
FlipperHelper launched in February 2026. Built solo in 43 days. As of mid-April: around 50 App Store downloads, 25 TestFlight users, 3 people who gave detailed feature feedback. Organic downloads started around 12 April without paid promotion.
Small numbers, but every feature traces back to something real people said in real conversations. No guessing, no focus groups. Just a parser and a lot of reading.
I'm the developer, so the obvious plug — but the method stands regardless of whether you use my app or build your own. Your target users are already talking in public. You just need to listen systematically.
FlipperHelper — App Store | GitHub Pages
