Introduction

In the hyper-competitive travel industry, user acquisition and retention heavily rely on pricing accuracy, competitive offerings, and real-time hotel inventory. A US-based OTA startup approached Travel Scrape to develop a robust data acquisition strategy using Expedia and Priceline hotel listing data. The goal? To increase direct hotel bookings, optimize margins, and stay competitive on price.

Objectives

• Monitor real-time hotel prices and promotions across top OTAs.
• Compare listings and availability to identify gaps and exclusive deals.
• Enable dynamic pricing and personalized offers on the client app.
• Improve SEO and conversion rates by using enriched hotel metadata and user review trends.

Why Expedia & Priceline?

Expedia and Priceline are two of the largest OTAs globally, hosting millions of listings. Their platforms offer diverse hotel options with varying rates based on location, time, and user profile.

Target Use Case:

• Scrape data across 50 US cities including New York, Miami, Chicago, and Los Angeles.
• Focus on mid-range to premium hotels with high booking intent during holidays.
• Enable filtering by price drops, free cancellations, user ratings, and last-minute deals.

Travel Scrape’s Data Strategy

1. Web Scraping Architecture

Travel Scrape deployed a custom scraping pipeline with rotating proxies, CAPTCHA solvers, and intelligent scheduling to:

• Avoid detection on dynamic pages
• Monitor thousands of listings daily
• Collect structured data using XPath and JSON parsing

2. Key Fields Extracted

3. Sample JSON Snippet

Implementation

Phase 1: Data Collection & Cleansing

• Collected over 500,000+ listings across Expedia and Priceline in 3 months.
• Normalized hotel data to remove duplicates and match similar listings.
• Tagged listings with geo-coordinates to map against app location filters.

Phase 2: Price Trend Analysis

• Mapped daily fluctuations in prices during peak season.
• Identified time slots when last-minute bookings dropped prices by 20-30%.
• Used Python-based data models to flag competitive pricing opportunities.

Phase 3: App Integration

• Integrated scraped data via internal APIs to feed the mobile app search results.
• Introduced “Best Deal Now” tags on listings matching Expedia discounts.
• Highlighted free cancellation deals to drive higher confidence bookings.

Results

Visual Insights

Price Distribution Comparison – Expedia vs. Priceline

• Expedia: Wider discount range (10–35%), skewed toward weekend bookings
• Priceline: More bundled offers, especially for airport hotels

Heatmap: High-Conversion Hotel Zones (Top 5 Cities)

• New York – Midtown & Downtown
• Miami – South Beach
• Chicago – Magnificent Mile
• San Francisco – Union Square
• Las Vegas – The Strip

Challenges Solved

Client Feedback

Conclusion

With Travel Scrape’s hotel data scraping services, the client achieved significant gains in booking conversion rates, user engagement, and price competitiveness. Leveraging real-time hotel data from Expedia and Priceline enabled smarter offers, improved trust, and higher margins.

If you're an OTA, metasearch engine, or hotel chain seeking similar growth, Travel Scrape can help power your platform with precise, real-time travel data intelligence.