How I Found 47 B2B Customers on Reddit Using Python and Zero Ad Spend
Three months ago, I was burning $2,400/month on Google Ads.
Got 127 clicks. Three signups. One was my mom testing the product.
The math wasn't working. So I tried something different: Reddit + automation + zero ad spend.
Result: 47 genuine conversations. 12 demo calls. 4 paying customers.
Here's the technical breakdown.
The Problem with Traditional Marketing
Google Ads works if you have budget. But for bootstrapped founders, the unit economics don't make sense:
Cost per click: $18.90
Conversion rate: 2.4%
Cost per customer: $787.50
LTV: $119.88 (12 months at $9.99/month)
Yeah. That's a problem.
Why Reddit?
Reddit has 430 million monthly active users. Your customers are already there, asking questions and describing their pain points.
The challenge: finding these conversations before they get buried.
The Manual Approach (And Why It Doesn't Scale)
I started manually:
- Search Reddit for keywords
- Filter by "New"
- Read through posts
- Respond to relevant ones
- Repeat 3 hours a day
This worked for about two weeks. Then I burned out.
Problems with manual search:
- Time-consuming (3+ hours daily)
- Easy to miss conversations
- No way to track what you've already seen
- Inconsistent results
The Automated Approach
I built a Python script to automate the search part. Here's the logic:
Step 1: Define Target Subreddits
target_subreddits = [
'SaaS',
'startups',
'Entrepreneur',
'smallbusiness',
'marketing'
]
Step 2: Define Search Queries
Don't search for your product. Search for the problem:
search_queries = [
'reddit lead generation manual',
'finding customers reddit time consuming',
'alternative to [competitor]',
'frustrated with [pain point]'
]
Step 3: Filter by Engagement
Not all posts are equal. Filter by:
filters = {
'min_comments': 5,
'max_age_days': 7,
'min_upvotes': 3,
'exclude_deleted': True
}
Step 4: Export to CSV
Export results with:
- Post title
- URL
- Author
- Subreddit
- Comment count
- Upvotes
- Post age
This lets you prioritize which conversations to join.
The Response Strategy
Finding conversations is half the battle. Responding correctly is the other half.
Bad Response (Gets Downvoted)
"Check out my tool! It solves this problem.
Here's the link: [url]"
Good Response (Gets Upvoted)
"I had this exact problem last month. Tried doing
it manually for two weeks - burned out fast. Also
tested [competitor] but it was too expensive.
Ended up building something simple that filters
by engagement and exports to CSV. Not perfect but
saves me about 8 hours a week. Happy to share if
it helps."
Key differences:
- Shows you understand the problem
- Mentions trying other solutions
- Admits limitations
- Offers help instead of selling
The Tool I Built
After proving the concept with Python scripts, I built a Reddit marketing tool - a desktop app that does all this automatically.
Features:
- Multi-subreddit search
- Engagement filtering
- CSV export
- Keyword tracking
- Duplicate detection
The UI isn't pretty but it works. 3-day trial, then $9.99/month. Saves me about 8 hours a week, which pays for itself.
The Numbers
After 90 Days
| Metric | Result |
|---|---|
| Conversations | 47 |
| Demo calls | 12 |
| Paying customers | 4 |
| Trial users | 8 |
| Ad spend | $0 |
| Time investment | 10 hrs/week |
Compare to Google Ads
| Metric | Result |
|---|---|
| Ad spend | $2,400 |
| Clicks | 127 |
| Signups | 3 |
| Paying customers | 0 |
| Stress level | High |
The Reddit approach takes more time upfront but the quality of leads is incomparably better.
Technical Challenges
Challenge 1: Rate Limiting
Reddit's API has rate limits. Solution: implement exponential backoff.
import time
def search_with_backoff(query, max_retries=3):
for i in range(max_retries):
try:
return reddit.search(query)
except RateLimitError:
wait_time = 2 ** i
time.sleep(wait_time)
return None
Challenge 2: Duplicate Detection
Same posts appear in multiple searches. Solution: track post IDs.
seen_posts = set()
def is_duplicate(post_id):
if post_id in seen_posts:
return True
seen_posts.add(post_id)
return False
Challenge 3: Shadowban Detection
Reddit can shadowban you without notice. Solution: periodically check if your posts are visible.
def check_shadowban(username):
user_posts = reddit.user(username).posts
for post in user_posts:
if post.removed_by_category:
return True
return False
Mistakes I Made
1. Using a Brand New Account
Accounts under 30 days old get filtered automatically. Had to wait and build karma first.
2. Posting the Same Comment Everywhere
Reddit's spam filters caught this instantly. Got shadowbanned for a week. Now I customize every response.
3. Being Too Promotional
If more than 10% of your comments mention your product, you're doing it wrong.
4. Ignoring Subreddit Rules
Got banned from r/entrepreneur for not reading the rules. Each community has different rules. Read them.
Advanced Tactics
Monitor Competitor Mentions
Set up alerts for competitor names. When someone complains, offer a genuine alternative.
competitor_keywords = [
'competitor_name',
'alternative to competitor',
'competitor too expensive'
]
Track Keywords Over Time
Reddit conversations have a lifecycle. Getting in early means your comment stays near the top.
I check every 4-6 hours. Takes 5 minutes. The ROI is ridiculous.
Build Credibility First
I spent a month just helping people in r/SaaS. Answered questions. Shared what worked. Didn't mention my product once.
Built up karma and credibility. Then when I did share my tool, people actually listened.
The Subreddits That Convert
Not all subreddits are equal:
| Subreddit | Members | Engagement | Conversion |
|---|---|---|---|
| r/SaaS | 100K | High | Best |
| r/startups | 1.5M | Medium | Good |
| r/Entrepreneur | 3.2M | Low | Poor |
| Industry-specific | 10K-50K | High | Excellent |
Smaller, focused communities beat massive generic ones every time.
Is This Sustainable?
Honest answer: I don't know yet.
Three months in, it's working. But I'm spending 10 hours a week on this. That's not scalable long-term.
My plan:
- Automate search (done)
- Build response templates (in progress)
- Hire help for monitoring (considering)
The goal isn't to spam Reddit at scale. It's to have genuine conversations at scale.
Should You Try This?
Yes if:
- You're bootstrapped with more time than money
- You can commit 2-3 months to building credibility
- You're willing to be genuinely helpful
No if:
- You need fast results
- You can't resist being promotional
- You're looking for a growth hack
Getting Started
- Pick 3 subreddits where your customers hang out
- Spend 30 minutes reading top posts from this month
- Search for one pain point your product solves
- Find 3-5 recent posts
- Write genuine, helpful responses
- Don't mention your product yet
Do this for two weeks. Build karma. Build credibility. Then start naturally mentioning your solution when relevant.
It's not a growth hack. It's not a shortcut. It's showing up, being helpful, and building trust.
But if you do it right, it's one of the highest ROI channels available in 2025.
Just don't burn $2,400 on Google Ads first like I did.
Tech Stack
For those interested:
- Python 3.9+
- PRAW (Python Reddit API Wrapper)
- Pandas for data processing
- SQLite for tracking seen posts
- PyQt5 for desktop UI
The code isn't open source (yet) but the approach is straightforward. Start with PRAW and build from there.
Found this helpful? I share more bootstrapping tactics on Wappkit. No spam, just real experiences.
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