Data science application plays an essential role in the aviation industry. It is vital to understand traveller demand for specific city pairs and pricing flights as they are among the main problems for which airlines have to make a solution to survive. It is essential to consider thousands of factors while analyzing data. While analysts still can use traditional statistical approaches to find insights from data.
Data science allows for more ways to accomplish demand analysis. IATA suggests that airlines can use traveller behavioural data, searches on the online travel agents, and searching sites or social media chatter can help define needed demand.
Data from professional networking sites and recruitment and procurement activities may signal emerging business travel destinations. In 2017 Skyscanner used machine learning-based clustering to generate insights into the group of about 50,000 origins and destinations by similarities. They considered about 30 parameters like the month of travel, reservation cancellation, and how long people stay at the destination.
Some events like festivals, occurrences, or expos, drive short-term spikes in demand. So, revenue teams can rely on event data to raise fares for specific routes and dates to benefit from the rising demand for tickets. Aviation ranked uses ranking algorithms that match historical flight bookings with event data to reveal how much a given event may affect traveller demand.
Insights into Airlines Industry
The technology of data science application provides excellent insights into the Airlines Industry. The aeroplanes flying in the skies generate data on engine systems, fuel utility, weather, passenger information, and many more aspects. Aircraft will generate more data with more advanced aircraft fitted with sensors and other data collecting tools adopted in the industry. This data can broadly open new opportunities for the industry.
Implementation has more scope for adapting to the data science industry.
Some of the applications are-
Ticket Pricing
Airline pricing depends on demand and supply. Many factors, such as weekends, holidays, routes, etc., influence pricing. It also relies on the timing of flights. Evening & early morning flights have different pricing than afternoon and late-night flights. However, the pricing must always be competitive to attract customers. The analytics-driven charge can help airlines automate the pricing mechanism and help them boost their revenues with optimal capacity utilization.
Personalized Selling
Airlines also sell many comfort services like lounge services, extra baggage, seat upgrades, food, etc. It can recommend personalized services based on the customer’s profile.
Customer Feedback
In the digital world, customer feedback comes from multiple sources, tweeters, pictures, calls, videos, etc. Data Science application has both structured and unstructured data rehearsed to help the customer support team listen to the customers & quickly respond to their needs.
Fleet Maintenance
Every cancellation affects the revenue and the brand image. Unplanned maintenance causes delays. For airlines trying to increase revenues through optimal fleet optimization, predictive maintenance can help keep their fleet up. Collecting & analyzing aircraft data in real-time can support the maintenance staff be proactive in avoiding technical glitches and planning their maintenance schedule uncertainty.
Crew Management
Crew management consists of various things. Working hours, days off, language skills, and Data Science can help automate crew schedules and bring insights to solve problems in personnel management, crew fitness, & regulatory compliances.
Fuel Efficiency
The airline industry estimated the airline industry’s fuel bill to be $180 billion (accounting for around 23.5% of the only operating expenses) in 2018. Data science technologies like AI & machine learning can help airlines get fuel-burn, weather, navigation, and operations data to deliver insights to optimize fuel utilization and reduce operational costs.
These are some of the airline industry’s standards for the data science application. A technology-driven & customer-facing industry like airlines need to optimally leverage data to innovate. That’s what will give a competitive era in the future.
It’s on the value of the product that the customers need. So the amount they are ready to pay depends upon that particular thing. When the flight delays or cancellations, the airline industry has to bear a considerable cost, including maintenance and compensation for the passengers stuck at airports. The solution for this is doing predictive analytics. Artificial Intelligence systems can quickly turn a bad journey of poor experience into a great experience. The response speed of acknowledging the customers’ questions matters more than the necessary steps taken to solve the issue within the time. Besides maintaining schedules for the aeroplane, training needs to focus on pairing junior crews with senior crew members and following the government regulations that the airlines should follow. The airlines spend 12% percent of their fuel on no-frills. Airlines use an Artificial Intelligence system built on Machine Learning models to gather data on every flight route, altitude, aircraft type, weight, capacity, and weather condition.
In simple words:
- Cost Management
- Flight safety and maintenance
- Customer Feedback
- Automated messages
- Air Crew management
- The efficiency of fuel detection
- Sales and food supply
- Fraud detection
- Self-service in flights
- Flight management services
DOWNFALL OF ARTIFICIAL INTELLIGENCE IN AVIATION
While AI has a promising future in the aviation industry, it also has some drawbacks.
It is expensive as all the airlines might not afford or invest in this costly technology of data science application.
Implementing things will take time for the aviation industry worldwide.
Even though there is an auto-pilot feature, we also need human intervention, as we cannot take the risk over the life of passengers.
