Scoot Airlines Enhances Passenger Experience with IoT
Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Robotic Process Automation (RPA)
Applicable Industries
- Aerospace
- Equipment & Machinery
Use Cases
- Chatbots
- Traffic Monitoring
Services
- Data Science Services
- System Integration
About The Customer
Scoot Airlines is the low-cost arm of the Singapore Airlines Group. It has carried over fifty million guests to 63 destinations across 17 countries. Scoot was voted 2015, 2016 and 2017 Best Low Cost Airline (Asia/ Pacific) by AirlineRatings.com and ranked in the Top 10 of the World’s Best Low-Cost Airlines in 2015 by Skytrax. Scoot uses Navitaire’s hosted passenger service system, an end-to-end passenger experience that starts with buying a ticket to getting a seat change, a boarding pass, all the way to tracking who’s sitting in each seat on the actual flight.
The Challenge
Scoot Airlines, a low-cost arm of the Singapore Airlines Group, was facing a significant challenge with bad bots abusing their booking engine. Unauthorized OTAs, competitors, and meta search sites were using sophisticated web scraping bots to exploit the business logic of Scoot’s booking engine. This led to skewed look-to-book ratios and site slowdowns. The bot traffic was also depriving legitimate customers of the opportunity to book air travel on Scoot’s website. Furthermore, Scoot was dealing with a high volume of traffic due to novice software development practices at its travel partners. The bot traffic was causing slowdowns across passenger-facing systems, including flight check-ins, which could trigger delays in departure times. The bot incidents were also impacting staff resources across multiple departments.
The Solution
Scoot Airlines turned to Imperva Bot Management to address the bot traffic issue. Imperva was chosen over other solutions like Akamai Bot Manager due to its proactive approach to stopping bots and its experienced analyst team. The deployment of Imperva on AWS took less than two weeks, including matching it to the company’s AWS footprint. Imperva’s machine learning capabilities were used to fight the bots in a way that Scoot’s team could not keep up with. The next phase of the solution involved working with Imperva to block bots from scraping their partners’ sites via their API and block bots using their mobile app emulating mobile devices. As Scoot introduced its frequent flyer loyalty program, they used Imperva to take precautions against bad actors trying to hack their login page.
Operational Impact
Quantitative Benefit
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