Airline Data Mining from OTAs

Introduction

The airline sector is changing at a fast pace. The airline industry has changed dramatically in recent years as a result of new technologies, a push toward centralized planning, and the introduction of new market participants. Furthermore, air travel has grown so common that it is difficult to imagine living without it. By cutting travel time and changing the perception of distance, the airline industry has also changed how people live and conduct business.

As the airline industry has matured, the number of players has increased, each attempting to entice customers through value and innovation. Customers, too, have high expectations for each flight they take. Companies in the airline industry are hitting their stride by utilizing advanced airline data mining tools and attempting to not only meet but also exceed customer expectations.

iWeb Scraping assisted a large American airline company in increasing customer satisfaction by 2X by improving their pricing approach, resulting in a 17 percent increase in earnings.

Business Challenge

Working in the airline sector, which is extremely dynamic and data-intensive, means there is a large risk of human mistakes at any given time. The client wanted to create a data-driven dynamic pricing system that could assist them in properly pricing aircraft tickets while simultaneously focusing on margin improvement. The goal was to gain access to structured flights such as arrivals and departures dates, gate details, prices, flight numbers, etc. from various airline websites or online travel agents throughout the web to study market dynamics and uncover opportunities for margin improvement. The client also had to deal with the following issues:

Market share losses – The client wanted to develop their competitive price elasticity analytical framework to prevent market share losses to competitors.

Dynamic pricing — Using airline price data, the client intended to use dynamic pricing tactics to increase profits while maintaining a positive brand image.

The in-house airline data scraping tools were not precise enough to assist them to drive profits, even though sophisticated choices on aircraft ticket pricing were made using both real-time and historical data sets.

Solution

Trip IDs/names, trip day, arrival airport, departure airport, Plane name, location code, flight prices, number of stops, arrival time, departure time, check-in, check-out, sight-seeing, and flight price tracking were among the primary information that we scraped. Airlines such as American Airlines, Aeroflot, Air Canada, Air France, Air India, Air Mauritius, Alaska Airlines, British Airways, Cathay Pacific, Continental Airlines, Emirates, Gulf Air, Indian Airlines, Jet Airways, Kenya Airways, Kuwait Airways, Lufthansa, Malaysia Airlines, Qantas Airways, Qatar Airways, Singapore Airlines, and others were among the source websites the client wanted scraped.

We built up the entire data pipeline so that data is sent to the client automatically using our API. iWeb Scraping formed an internal team dedicated solely to this project to produce high-quality data. Our team meets with the client on a regular basis to review KPIs, new initiatives, and barriers, ensuring that their web scraping solution is precisely aligned with their business objectives.

Conclusion

Our LinkedIn Post Data Analysis offered proof of how analytics-based data can revolutionize B2B content strategy. The shift toward structured performance data and less guesswork allowed the client to enhance the level of engagement, attract the right people, and produce more qualified leads. This case study demonstrates the knowledge of iWeb Scraping, which has the expertise of developing custom data extraction and analytics solutions on individual platforms. By being in 20+ industries, we assist businesses to maximize all the potential of the online presence with precise and useful real-time data insights.

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