How a Singapore OTA Increased Conversions with Google Flights Scraping Insights
Overview
With increasing competition from global OTAs and travel aggregators, the Singapore-based OTA wanted to gain a competitive edge using real-time flight pricing intelligence. Travel Scrape partnered with them to implement Google Flights data scraping, enabling pricing transparency, fare trend analysis, and smarter airfare alerts across Southeast Asia routes.
This case study explores how scraping Google Flights data helped the OTA increase user retention and drive a 34% uplift in conversions within just 60 days of integration.
Objectives
- Scrape flight fares, airlines, routes, and date-based price changes from Google Flights.
- Map dynamic fare trends for 50+ high-traffic international and domestic routes.
- Provide deal alerts and in-app nudges based on live fare insights.
- Enable AI-based fare predictions by aggregating historical scraped data.
- Increase conversion rates and repeat booking behavior by offering better timing advice.
Why Google Flights?
Google Flights aggregates real-time flight data from hundreds of airline websites and OTAs, including:
- Fare calendar and date-based price drops
- Airline comparison with baggage, layover filters
- Predictive trends (e.g., “prices are likely to increase soon”)
By tapping into this structured information, OTAs can:
- Price match or undercut competition
- Trigger alerts before fare hikes
- Recommend alternative dates with lower fares
Data Fields Extracted from Google Flights
| Field | Sample Value |
|---|---|
| Departure City | Singapore (SIN) |
| Arrival City | Tokyo (NRT) |
| Airline | Singapore Airlines |
| Price (One-way) | SGD 421 |
| Return Price (Round-trip) | SGD 780 |
| Flight Date | 2025-07-10 |
| Stops | Nonstop |
| Departure Time | 9:30 AM |
| Fare Trend Alert | “Prices expected to rise in 2 days” |
| Booking Source URL | Google Flights Link |
Sample JSON Extract
Travel Scrape’s Execution Plan
Step 1: Target Route Identification
- 100+ route combinations between Singapore ↔ key cities:
- Asia-Pacific: Tokyo, Bangkok, Bali, Hong Kong, Kuala Lumpur
- Europe: London, Paris, Zurich
- Middle East: Dubai, Doha
- Domestic (Chartered/Regional): Tioman, Langkawi
Step 2: Intelligent Scraping Infrastructure
- Utilized headless browsers (Puppeteer) to load dynamic pricing calendars
- Rotating proxies + human-like delays to avoid Google CAPTCHA triggers
- Extracted structured fare data across 30-day and 60-day calendar views
Step 3: Historical Data Warehousing
- Stored daily scraped data in a PostgreSQL database
- Tagged prices with timestamp and fare movement direction (↑, ↓, ↔)
- Developed AI models to estimate fare volatility and best booking windows
Step 4: Integration with OTA App
- Plugged insights into the client’s backend via REST API
- Front-end updated with:
- “Smart Dates” – alternate low-fare suggestions
- “Watch This Route” alerts for price drops
- “Book Now” urgency prompts when trend predicted a fare hike
Results & Metrics
| KPI | Before Travel Scrape | After Google Flights Integration |
|---|---|---|
| Conversion Rate | 3.2% | 5.3% |
| Avg. Booking Value (ABV) | SGD 420 | SGD 476 |
| Repeat Users on App | 15% | 23% |
| Alert-Triggered Conversions | – | 2,800+ in first 45 days |
| Support Tickets on Fare Issues | High (230/month) | Low (92/month) |
Visuals & Data Insights
Price Volatility Graph (Singapore to Tokyo – July 2025)
- Fare range: SGD 610 – SGD 980
- Lowest prices seen when booking 17–24 days in advance
Top 5 Price-Sensitive Routes (Q3 2025)
| Route | Avg. Volatility | Peak Booking Days |
|---|---|---|
| Singapore – Tokyo | High | Sundays & Tuesdays |
| Singapore – Bangkok | Medium | Fridays |
| Singapore – Zurich | High | Mondays |
| Singapore – Dubai | Medium | Wednesdays |
| Singapore – Bali | Low | Weekends |
User Interface Enhancements Powered by Scraped Data
- Fare Prediction Badge: "Likely to increase soon" or "Good deal now"
- Date Alternatives Modal: "Cheaper flights available 2 days earlier - Save SGD 84"
- Low Fare Alert Notification: Triggered if price drops >10% on watched routes
- Travel Calendar Tool: Populated with scraped prices from Google Flights
Client Feedback
"Travel Scrape’s Google Flights data integration gave us exactly what we needed—price intelligence that converts browsers into buyers. Our users feel more in control and trust our recommendations."
Challenges Overcome
| Challenge | Travel Scrape’s Approach |
|---|---|
| Dynamic Content (JS Loaded) | Used Puppeteer for JavaScript-heavy pages |
| Anti-scraping Protections (CAPTCHA) | Human-like user behavior, random delays |
| Date-Range Pricing Extraction | Calendar parsing for next 30-60 days |
| Fare Prediction | Trained models on historical scraped data |
| Conversion Attribution | Tracked alert-triggered vs regular bookings |
Tech Stack Used
- Scraping Framework: Puppeteer, Python (Scrapy for static fallback)
- Backend Storage: PostgreSQL + AWS S3 for backups
- AI/ML Models: XGBoost for fare prediction
- API Layer: FastAPI for data feeds
- Dashboard & Reporting: Metabase for trend visuals
Conclusion
By leveraging Google Flights data scraping, Travel Scrape enabled the Singapore OTA to:
- Understand market-level pricing dynamics
- Automate flight fare alerts and suggestions
- Improve customer confidence and booking urgency
- Reduce manual fare tracking efforts by over 80%
As competition heats up in Southeast Asia’s travel ecosystem, real-time airfare intelligence is no longer optional—it’s a strategic necessity. Travel Scrape continues to support OTAs and airlines across the region in building smarter, data-led experiences.