Overview
The Canadian startup’s vision was simple: help users find travel deals without searching. Their mobile app would monitor OTA platforms in real time and alert users instantly when prices dropped for flights or hotels. Travel Scrape helped make this a reality by building a high-frequency scraping infrastructure and pricing intelligence layer.
The outcome? A successful beta launch with 35K+ users in 3 months and a 30% conversion rate on push alerts.
Project Goals
- Scrape real-time flight fares and hotel prices from global OTAs
- Monitor 500+ popular origin-destination city pairs and hotel hotspots
- Trigger personalized deal alerts for users based on watchlist
- Enable flexible search: “Any weekend in July under $200 from Toronto”
- Generate visual deal summaries and travel insights
Routes & Markets Monitored
| Origin Cities |
Top Destinations Tracked |
| Toronto, Vancouver, Calgary |
New York, Cancun, London, Paris, Tokyo |
| Montreal, Ottawa |
Lisbon, Miami, San Francisco, Dubai |
Hotel Cities: Toronto, NYC, Montreal, Vancouver, Los Angeles, Rome, Bangkok
Why Real-Time Scraping?
APIs from most OTAs are limited, delayed, or expensive. Scraping allows:
- Instant fare and rate monitoring
- Access to OTA-exclusive promotions
- Flexibility in defining custom alert rules
- Better UX through fresh pricing content
Travel Scrape's Technical Strategy
Data Fields Extracted
Flights (via Google Flights, Skyscanner):
| Field |
Example |
| Route |
Toronto → Miami |
| Airline |
Air Canada |
| Fare |
CA$198 |
| Stops |
Non-stop |
| Departure Date |
2025-07-12 |
| Booking Link |
Skyscanner URL |
| Price Trend |
Dropped 18% in 48 hrs |
Hotels (via Booking.com, Agoda):
| Field |
Example |
| Hotel Name |
Holiday Inn Downtown Toronto |
| Nightly Rate |
CA$134 |
| Rating |
8.2 / 10 |
| Amenities |
Free WiFi, Parking |
| Cancellation |
Free cancellation |
| Rooms Left |
Only 3 rooms available |
2. Scraping Stack
- Tools Used: Puppeteer + Playwright (for dynamic content), Python, BeautifulSoup
- Frequency: Every 1–2 hours per route and hotel city
- Data Storage: MongoDB (pricing logs), AWS S3 (snapshots), Redis (push triggers)
- Load Balancing: Cloudflare proxy rotation, geo-IP-based targeting
Sample JSON (Flight)
Key App Features Powered by Scraped Data
| Feature |
Description |
| Price Watchlist |
Users follow routes and destinations |
| Deal Feed |
Curated fare and hotel drops posted in feed |
| Smart Weekend Search |
Auto-match deals within weekend dates & budget |
| Price Prediction Badge |
“Now is a good time to book” or “Wait – may drop” |
| Push Notifications |
Sent within 10 minutes of price threshold match |
Launch Results (First 90 Days)
| Metric |
Value |
| App Downloads |
35,000+ |
| Watchlist Subscriptions |
120,000+ routes |
| Avg. Push Alert CTR |
42.6% |
| Booking Redirect Conversion Rate |
29.7% |
| OTA Partner Commissions Earned |
$18,000+ |
Deal Examples Powered by Scraped Data
Toronto → Cancun, Return (July 2025)
- Price dropped from CA$432 → CA$298
- Alert triggered for 8,200 users
- 2,100+ clicks in 1 hour
Montreal – Downtown 3-star Hotel
- Booking.com listed CA$102 (regular: CA$149)
- Alert sent with “Free breakfast + cancellation” tag
- 680+ bookings via app affiliate link
User Engagement Boost
| Action Triggered |
Pre-Scraping Era |
With Travel Scrape |
| Deal Alert Conversion |
11.5% |
29.7% |
| Avg. Session Time |
1 min 14 sec |
2 min 42 sec |
| Repeat Logins (weekly) |
3,200 |
11,600+ |
Client Testimonial
"Before Travel Scrape, our alerts were either stale or inaccurate. Now, our users see deals before most OTAs even promote them. It’s the core of our app’s success."
- Founder & CEO, Canada-based Travel Deals App
Tech Stack Summary
| Layer |
Tools Used |
| Scraping |
Puppeteer, Playwright, Scrapy |
| Storage & Queueing |
MongoDB, Redis, AWS S3 |
| Backend Services |
FastAPI, Node.js |
| Frontend (Client Team) |
React Native |
| Alert Delivery |
Firebase Cloud Messaging (FCM) |
| Analytics |
Mixpanel, Amplitude |
Challenges & Solutions
| Challenge |
Travel Scrape’s Solution |
| Frequent CAPTCHA on OTAs |
Human behavior mimicry + proxy rotation |
| Rapid price fluctuations |
Hourly scraping frequency per route |
| Location-sensitive prices |
Geo-targeted scraping IPs |
| Alert overload |
Dynamic scoring to avoid alert fatigue |
Use Cases Unlocked
- Leisure Travelers: Get push alerts for low-cost vacations
- Frequent Flyers: Monitor flexible business trip pricing
- Affiliate Marketers: Earn commission from travel deal redirects
- Travel Agencies: Plug deal alerts into their customer portals
Conclusion
With Travel Scrape’s real-time OTA scraping feeds, the Canada-based startup was able to deliver one of the fastest and most accurate deal alert apps in the market. The app’s core value—spotting flight and hotel drops before users miss them—was fully powered by scraping automation, not third-party APIs.
In a world where deals vanish within minutes, scraping data from OTAs is not just useful—it’s mission-critical.