The airline industry strives to understand better the demands of travelers about particular pairs of destinations and flight ticket prices. Air carriers need to consider many factors when analyzing the data to accomplish this. Analysts also have the option to use conventional statistical tools.
Experts can get their hands on more sophisticated methods to analyze demand more efficiently using data science. Airlines can effectively utilize the behavioral data of passengers and discard searches done on travel agents’ websites. They can use the data of metasearch sites and discussions done on social media to get an idea of the demand.
Professional networking websites and procurement projects can provide data that can help airlines identify which business travel spots will be the most popular in the future. Many events like expos, conferences, and festivals lead to a short-term increase in demand. Revenue teams can use this opportunity and utilize the event data to increase the fares for particular routes in response to the increased demand.
Several airlines use ranking algorithms that compare earlier flight reservations with the data of the events to predict the impact that a specific event can have on ticket demand. Let us look at some other ways the airlines use data science.
Customized selling
Apart from travel tickets, airlines also sell various comfort services such as additional baggage, lounge services, seat upgrades, and refreshments. Analytical tools can generate data-backed recommendations to track previous services opted by the passenger and use them to recommend additional services. By analyzing the traveler’s economic profile, the airline platform can also suggest customized services.
In-flight food catering
Though many travelers do not pre-order meals while booking a flight ticket, they might order food after takeoff. So the supply management experts of the airline need to figure out the number of eatables and beverages they need to keep with them to fulfill the demand of passengers and not waste any food either.
For instance, the supply team knew that the demand for food on a 6:00 AM flight to Washington differs from the demand for the same on a Friday night flight to San Francisco. But the food kept for both these flights was the same, which led to a lot of food going to the bin.
The wastage also cost the airliner millions of dollars which could have been used somewhere else where the return on investment would be better. Data scientists came up with a solution and developed an algorithm to predict demand. The insights generated can assist airlines in cutting costs and also become more environment-friendly.
Demand prediction can be made in a better way using data science. If you want to learn the art of demand prediction, you should consider doing the best data science course online from Great Learning, and it will make you skilled and ready for the future.
Feedback from customers
Since the world has started moving towards digitalization, there are many ways through which airliners can record customer feedback. Some of these are calls, tweets, Facebook and Instagram posts, videos, Google forms, and much more.
Using data science, you get the power to capture both structured and unstructured data. The best part is that you get to process this data in real-time. This can let the customer support team explain what the customers have to say and immediately respond to them to fulfill their needs.
Consumption of fuel and its optimization
Airplane producers and airlines are searching for methods to enhance the overall fuel efficiency. On average, airlines spend a quarter of their expenses just on jet fuel.
If an airline wishes to enhance their fuel efficiency, they need to predict its fuel requirements and the amount of fuel required for each trip. If the fuel estimates are there with the airliner, they can supply the airplane with the same amount, and Analytical tools help estimate these amounts.
Southwest Airlines took up a project to find a solution for tackling excessive fuel consumption. The airline company formed a team, and they came up with eight predictive models, which consisted of neural networks and time series algorithms. The system they developed could throw up 9600 fuel consumption predictions every month.
The airline creates forecasts for a total window of 12 months. It considers several factors like the total number of trips, the current fuel price, and the time taken to make the predictions as accurate as possible. With the rising number of data science applications in this industry, airlines are looking to hire professionals who can do the job well. You can enroll yourself in a Great Learning’s data science and engineering course to enhance your skills in data science.
Maintenance of fleet
If an airline cancels a scheduled flight, it negatively impacts the brand image and leads to a decline in revenue. If sudden maintenance has to be done, delays are bound to happen. This is why airlines are opting for tools that support predictive maintenance, and this ensures that the entire fleet does not suffer unexpected issues and is constantly running.
To achieve a smoother maintenance experience, airliners need to record and analyze the airplane data in real-time. Doing this will help the maintenance staff to stay alert and on their feet for any potential fixes and repairs. They can also develop their maintenance schedule accordingly.
Conclusion
The above applications are not exhaustive, and the potential uses of data science in the airline industry and other sectors are limitless. Other applications are being discussed and implemented as you read this article.
Data science is also valuable for other industries, apart from the airline industry, and it is one of the hottest skills in the market. Great Learning has some excellent courses on data science that can help one become more knowledgeable. Make sure to check it out.