07 Oct 2022

Application Of Data Science In Airline Industry

Ekeeda Moderator
Works at Ekeeda

The civil aviation industry of India has emerged as one of the fastest-growing industries across the globe. India currently has the 7th largest civil aviation market in the world & it’s expected to become the 3rd largest civil aviation market within the next 10 years. India’s passenger* traffic stood at 188.89 million in FY22 & 76.47 million in the first quarter of FY 2022-23. In FY22, airports in India pegged the domestic passenger traffic to ₹166.8 million, a 58.5% YoY increase, and international passenger traffic to be ₹22.1 million, a 118% YoY increase, as compared to FY 2020-21.

Market Share Of Indian Airlines (2019-20)

With the civil market mushrooming, security and reliability should be at the forefront of priorities for the Airline industry. Transportation is just like blood vessels in our body, and the unexpected failures in its implication will create an expensive and large ripple effect in the market. For these and other reasons, the IT and technology landscape consists of applications built on legacy technologies that will be robust and reliable. But there are designed to easily facilitate technological advancement needs. This technology debt accumulating is now making a paradigm shift – counter the security and reliability solutions. Another factor that contributes to technical debt is the presence of GDS (Global Distribution Systems that take the products from airline carriers and distribute them to corporate & private customers.

But we are now catching up with the technology – things that were just concepts are now into the implementation phase. From eating to shopping, industries across the board provide personalization and convenience. The customer is getting solutions at the click of a button and expects tailor-made solutions for them, which fit their needs without having to instruct in detail, or visit shops, banks, offices, and places to sort things or explain in detail.

Data Analytics In Airline Industry

Today, technology has changed the dynamics of business; especially the way they connect with their customers, take business decisions, and design workflows. No doubt, the world of aviation has been taken away by its storm; data is transforming the already reeling airline industry worldwide from pre-flight to post-flight operations, including ticket buying, seat selection, boarding, ground transportation, etc. everything is handled efficiently due to data science techniques. Thus, data required for hundreds of elements is captured along with various components of a passenger’s journey. 

Although we book flights via phone; there is a catch to it. Guess what? We get access to real-time access to data and help book flights as per flexibility and flying needs. Also, it allows companies to take informed steps to gain operational efficiency and enhanced customer experience. The airline industry is highly competitive, generating billions of dollars every year with a cumulative profit margin of 1-2%.

Data plays a quintessential role to uplift the face of the Airline industry. The ultimate benefits of big data and data science include - timely response to current and future market trends, better planning & strategy with critical decision-making, and monitoring of all major drivers in the airline industry.

Right from customers flying till they come back from the trip, there are applications of Data Science that will monitor and give unbeatable end-to-end solutions. When you couple the historical data of passengers available in the PSS system with social media & information sources, customers could be presented with discounts and offers that they’re likely to avail of and come back for their next trip. Possibilities open up for relevant suggestions, for services like the right time to book a cab, lodging, proactive handle challenging situations like congestion at airports, flight delays due to emergency landings, bad weather conditions, etc.

Some instances where data science techniques will help gain operational efficiency:

  • Baggage Check: A strategy could be to offer a minimum price at booking and increase it once the customer moves along the travel lifecycle.

  • Pre-Assign Seats: Create various seating zones and highlight their features of it.

  • Bundling & pack of services: We can use pure bundling vs mixed bundling such as on holiday, business class, etc.). When can use airlines data analytics to capture what the customer needs and thereby bundle it at the best price to maximize revenue.

  • Upgrade: Use the bidding process and upgrade as rewards

  • Priority Boarding: It could be sold separately or given as a reward or loyalty. 

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Data Science In Airline Industry

Data Science technology offers great opportunities to the airline industry. Optimal decisions about inventory, fares & routes will help to derive valuable insights from predictive models. The airlines flying high in the skies will generate tons of data on the engine systems, fuel utilization, weather, boarding information, etc. With more advanced aircraft, installed with sensors and other data collection tools being adopted in the industry, there will be gigabytes of data generated every day in the sector. The data if leveraged properly will open new avenues for the industry. Opportunities in process optimization, people management, and innovation. Still, it’s in the infancy stage, but there has been increased adoption of data science technologies in the aviation world.

Apart from this, things like boarding delay to accommodate a connecting flight or re-accommodate the incoming passenger is based on real-time statistical data instead of human conversation. Some data points include passenger lifetime value, frequent flyer status, time taken to reach the departure gate & downstream ripple effect. Aircraft turnaround will be another key activity that could be more efficient. Data analysis and pattern recognition will provide key inputs to make resource allocation decisions.

Let’s take a look at some applications of Data Science in airlines industry:

  • Ticket Price - Airline price is driven by demand & supply needs. There are a lot of things that will influence pricing – weekends, holidays, routes, etc. It also depends on flight timings – evenings, and early morning flights will have different pricing compared to afternoon and late-night flights. Destinations, country, demographics, and geographical locations are other factors to decide the ticket price. The pricing has to be competitive so to attract customers. Data Analytics will help airlines automate the pricing mechanism and let them help boost revenues with optimal capacity utilization.

  • Personalize Sell Out - Airlines sell a lot of comfort services such as longer, extra baggage, seat upgrades & cafeteria foods, etc. A data-driven recommendation engine will help the industry analyze customer’s past history & suggest subsidized services during the time of ticket booking. It will recommend personalized services based on the customer’s economic profile.

  • Customer Feedback - In this digital world, customer feedback will come from various sources such as tweeter, images, calls, videos, etc. Data science has the potential to process both – structured and unstructured data in real-time and help the customer support team address customer concerns and quickly deliver solutions to them.

  • Fleet Maintenance - Every cancellation leads to an impact on revenue and also on the brand image to some extent – why was the customer wasn’t retained? Some unplanned maintenance or technical errors might cause flight delays. Airlines are trying to boost revenues through optimal fleet optimization, thus predictive maintenance will help airlines keep their fleet up and running. Collection and analyzing aircraft data in real-time will help the maintenance staff be proactive in avoiding technical glitches and plan maintenance in advance. 

  • Crew Management - There were a lot of things in crew management – working hours, shift timings, roasters, weekly offs, weekends, unplanned leaves, member licensing, language skills, etc. Data Science will help in automating crew schedules and bring a lot of insights to solve challenges in personnel management, crew fitness, and regulatory compliances.

Enhance Fuel Efficiency - The global airline industry's fuel bill is estimated to reach a total of $192 billion in 2022 so far. Data Science technologies such as machine learning and AI will help airlines gain valuable insights on fuel-burn, weather, navigation, and operations data to deliver valuable thereby helping optimize fuel utilization & reduce operational costs.

These are some of the common areas where data science techniques are applicable in the aviation industry. As the scope widens, we will see a deeper application of data in the industry. Airlines are technology-driven and customer-oriented which needs to optimally leverage the data to achieve operational efficiency and book higher profits for the companies. This is how companies will be able to beat the competition and gain an edge in the future.

Get more in-depth knowledge on the use of data science in aviation industry. Watch now

Sources: IATAtv

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On A Concluding Note –

Data science plays a key role in the aviation industry. Through predictive analytics, sentiment analytics, and travel journey analytics, the airline industry can keep customers updated in real time, and promote special offers based on their requirements, habits, and unique experiences. Data collection and crunching will help airlines understand passengers’ tastes and behaviour, offering them transportation options they prefer and are willing to spend money on. Industry can gain operational efficiency and beat the competition to data-driven and informed decisions.

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