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Just scraped 413 Nepal hotels from
Google Maps! 🏨🇳🇵
413 records including:
→ Phone numbers
→ Full addresses
→ Ratings & websites
→ Opening hours
→ Direct Maps URLs
Perfect for tourism agencies, travel
companies and hotel booking platforms!
🎉 50% launch offer — message me!
Normal $30 → NOW $15
📌 Order or message me:
🔗 Fiverr: fiverr.com/bibhuti981
🌐 Portfolio: bibhutiportfolio.vercel.app
🐙 GitHub: github.com/bibhuti-adhikari
💼 LinkedIn: linkedin.com/in/bibhuti-adhikari
📧 Email: bibhutiadhikari788@gmail.com
#webscraping #python #googlemaps
#nepal #leadgeneration #data #Nepal #kathmandu
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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Everyone wants more leads.
Very few businesses have a system for following up with them.
One thing I've learned while working with startups is that growth isn't just about getting more traffic.
It's about what happens after someone shows interest.
A missed follow-up.
An unanswered email.
A lead that never gets a second touch.
Those small gaps add up.
That's why I'm excited to be helping build Warmo.ai, a platform focused on making outreach and follow-up more organized, efficient, and scalable.
Technology is important.
But the real goal is helping people build better relationships, not just send more messages.
What's the biggest challenge you face with follow-up today?
#Startup #Sales #AI #LeadGeneration #BusinessGrowth #Automation #SaaS #Founder
The point about growth breaking after someone shows interest is spot on. For a startup with no follow-up system yet, which first automation usually recovers the most missed opportunities?
Great design! I like that little rounded square photo on the top!
I built a small data-cleaning workflow around one simple rule: bad rows should never silently disappear.
Synthetic demo results:
• 20 raw order records
• 16 clean rows
• 2 duplicates removed
• 2 invalid rows quarantined
• $3,690 paid revenue reconciled
The output includes a clean CSV, a rejected-row file, an audit log, and an Excel dashboard. Dates, names, emails, regions, products, and payment statuses are standardized.
The key design choice is quarantine + reasons, not just dropping bad rows and exporting. That lets a reviewer trace exactly what changed and decide whether rejected records should be repaired.
For recurring exports, what needs the most manual review in your workflow: duplicates, inconsistent labels, or invalid amounts and dates?
