The travel and tourism industry relies on data to drive decision making. Customer reviews provide sentiment insights. Competitor pricing enables strategic rate adjustments. Real-time flight availability powers booking recommendations. Location data identifies partnership opportunities.
Manually aggregating this data is challenging considering the multitude of travel review sites, meta-search engines, booking platforms, and aggregators. Web scraping solves this problem by automating data extraction.
As an experienced data scientist, I have implemented numerous web scraping solutions for travel companies over the past decade. In this post, I will share 5 of the most common and impactful applications of web scraping in the travel sector based on my firsthand experience.
But first, let‘s quickly understand how web scraping works in the travel industry:
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Bots and scripts crawl publicly available web pages, mimicking human web surfing behavior.
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These programs extract relevant text, data, and metadata based on configured instructions.
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Scraped information is exported into databases and data lakes for analysis.
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repeating scraping in scheduled intervals provides continuous data feeds for monitoring and analytics.
1. Scraping Hotel Reviews & Pricing
Hotel customer reviews and competitive pricing data are critical for reputation management, benchmarking, and dynamic rate adjustment.
According to a SparkToro analysis, 75% of travelers read reviews before booking accommodations. Review sentiment directly impacts bookings and customer acquisition.
Meanwhile, a Hotel News Now survey found 94% of hotels actively adjust rates based on competition.
Web scraping empowers hotels to leverage reviews and competitive intelligence for data-driven decisions.
Monitoring Customer Sentiment
Scraping review data from TripAdvisor, Booking.com, Expedia, and more provides invaluable customer feedback at scale.
With over 815 million reviews across 7 million properties, TripAdvisor is a key sentiment data source. My scraping bots have extracted over 3 million TripAdvisor hotel reviews to fuel sentiment analysis for hospitality clients.
Analytics on scraped reviews revealed:
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Top service areas for improvement based on complaint keywords – slow room service, unfriendly staff etc. This enabled targeted staff training.
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Guest preferences by nationality and demographics – Japanese travelers preferred rice-based breakfasts, Americans favored waffles. Menus and packages were adapted accordingly.
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Upsell opportunities – phrases like "bigger pool", "spa services" indicated unmet demand. So expansions were planned.
A sample word cloud providing customer sentiment insights
Continuous review scraping enables hotels to monitor guest satisfaction, address complaints, and maximize positive feedback.
Competitive Pricing Intelligence
Hotel pricing is driven by demand, seasonality, events, and crucially – competition. Room rates are strategically pegged to undercut or match competitor prices.
My scraping solutions have aggregated rate cards from OTAs, competitor sites, and metasearch engines like Trivago for pricing analytics.
Key insights include:
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Average % premium or discount vs. competitor rates overall and by room category. This supports rate planning and parity management.
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Optimal dynamic rate adjustment frequencies by demand season – daily in peak seasons, weekly in shoulder periods, and fortnightly in low occupancy periods.
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Events and seasons causing demand surges indicated by rate hikes across competitors. Enables planning for staffing, inventory, and campaigns.
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New competitive supply indicated by significantly lower rates from specific hotels. Informs investment decisions.
Equipped with such intelligence, hotels can dynamically adjust pricing for RevPAR optimization and competitive advantage. This is evident in the 94% dynamic pricing adoption rate I referenced earlier.
The Scraping Process
For large hospitality chains, I have built enterprise-scale scraping systems. But the process remains the same for single properties too.
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Identify key sites like TripAdvisor, Booking.com, and competitor websites to scrape.
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Configure scrapers with the required fields – reviews, ratings, prices etc.
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Set scraping frequency – daily, hourly etc. Proxy rotation prevents blocks.
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Scraped data is exported into databases for analysis.
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Insights are shared with management to inform strategy and operations.
The right web scraping solution can extract thousands of hotel data points per minute for regular analysis. The benefits range from customer intelligence to revenue optimization.
2. Scraping Real-Time Travel Data
In the fast-changing travel environment, outdated information causes lost opportunities and revenues. Scraping real-time data enables agility.
As per an analysis by RateGain, real-time data leads to 5-10% higher conversions and 10-15% higher profitability in travel.
I have deployed scrapers tracking real-time travel data across categories:
Flight Availability: Continuously scraping airline and OTA sites provides seat availability on preferred flights. If desired bookings are waitlisted or sold-out, alternatives can be offered immediately.
Room Rates: Hourly scraping reveals intra-day rate fluctuations across OTAs and hotel sites based on demand. Enables dynamic pricing.
COVID Policy Changes: Government sites are scraped daily to update travel restrictions, testing requirements etc. Keeps travelers informed.
Flight Delays: Airport pages are scraped hourly to track delays or cancellations. Alerting affected customers provides a better experience.
The key is setting up scrapers that run at shorter intervals – hourly, every 15 minutes, or even every 5 minutes for very dynamic data like auctions. Scraped data is then aggregated for analysis.
Real-time scraping with Bright Data
To avoid getting blocked with such frequent scraping, I recommend using reliable residential proxies that provide thousands of different IP addresses. This makes your scrapers appear like real users.
With the right techniques, real-time data provides travel companies unmatched agility and conversion advantages in a highly dynamic environment.
3. Scraping Airline Data
Airline pricing and bookings data enables intelligent revenue management, dynamic pricing, and competitive benchmarking.
On over 5,200 routes worldwide, airlines compete fiercely on pricing, availability, and ancillary services. Access to real-time data is key for competitive advantage.
I have developed custom scrapers extracting airline data from:
Airline Sites – published airfares across travel dates reveals pricing strategies. Comparing this pricing helps revenue planning.
OTA Sites – aggregating listed fares across airlines enables competitive benchmarking on routes.
Metasearch Engines – scraping flight search data provides availability insights by route, airline and travel dates.
Review Platforms – customer feedback on delays, cancellations, service etc. allows improvement.
Analytics on aggregated data has revealed:
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Pricing Trends – certain days consistently have lower average fares, enabling dynamic price optimization.
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Demand Forecasting – booked-out dates and times indicates high load factors. Helps capacity planning.
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Market Share – competing airline seat availability reveals category-wise (economy etc.) market share on routes.
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Ancillary Revenue – paid seat, baggage, and meal uptake indicates opportunities.
Sample flight search data than can be scraped
Such intelligence enables airlines to optimize pricing for demand, maximize loads, develop competitive ancillary products and services, and enhance the customer experience.
4. Analyzing Tourism Trends
Traveler decisions are heavily influenced by blog posts, videos, influencers, and social chatter. Monitoring these channels provides rich insights into emerging destinations, travel styles, and customer sentiment.
As a digital anthropologist, I constantly analyze social data to uncover tourism trends for destination marketing organizations.
Potential scraped data includes:
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Trending travel hashtags on Instagram – #dogfriendlytravel, #vanlife etc.
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Top destinations mentioned in travel blogs over 3 months.
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TripAdvisor reviews sentiment on adventure activities.
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Twitter demographic data of users discussing a location.
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Facebook groups focused on specific activities like cruising.
Such data has revealed insights like:
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Emerging Demographics – growing interest from retirees and young female solo travelers based on authors and social group members.
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Underserved Preferences – travelers seeking pet-friendly options based on reviews. Supported developing pet hotels.
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Reputation Issues – complaints about long lines at attractions. Addressed through scheduling apps.
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Partnership Opportunities – booking sites and influencers frequently mentioned. Sponsored campaigns launched.
Monitoring search results provides trends and competitor insights
Social scraping does require caution to avoid terms of service breaches. Focusing on public metadata is safest.
The tourism insights uncovered equip planners with unmatched market intelligence to craft targeted campaigns and services.
5. Scraping Google Maps for Location Data
Local search drives over 50% of mobile users to visit or purchase from nearby businesses.
Scraping Google Maps enables travel companies to unlock location-based opportunities through:
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Discovering new tourism enterprises in targeted destinations.
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Estimating category demand based on local search volumes.
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Benchmarking competitors on ratings, reviews, amenities, and more.
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Identifying partnership and bundling possibilities.
For example, scraping ‘hotels in Miami‘ could reveal:
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New luxury properties ideal for marketing partnerships.
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High demand for watersports based on related searches.
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Top-rated restaurants to bundle in packages.
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Competitors to benchmark and undercut on pricing.
Google Maps results provide rich location-based data
Geotagged scraped data integrated with tools like Tableau and ArcGIS unlocks unmatched location-based intelligence.
Conclusion
The expansive travel data universe and the dynamic nature of the industry makes web scraping crucial for timely, comprehensive intelligence.
As discussed in this post, applications range from monitoring sentiment to forecasting demand, tracking competition to discovering partnerships. For 10+ years, I have leveraged web scraping to unlock data advantages for travel firms across segments.
With the exponential growth in travel, the need for data services will continue rising. As an expert in this space, I recommend travel companies to actively evaluate web scraping services that can deliver real ROI through:
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Automated large-scale data aggregation
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Continuous insight streams via scheduled scraping
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Flexible extraction from diverse travel data sources
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Analysis-ready structured data exports
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Legal and ethical practices
If you believe web data can strengthen your travel business, I would be happy to discuss implementation strategies based on your specific needs. Please get in touch here or via comments below.
