Airline revenue management – disruption by ai

Airline revenue management – disruption by ai

Airline revenue management – disruption by ai

Being a foreign student at one of the universities in the UAE, you tend to have frequent visits back to your home country.  As you check the deals for different flights, you see that non-stop flights have a higher price whereas the flights that travel a long distance and have multiple stopovers have a much lower fare.  As you wade through the deals, you realize that the first deal you saw was probably the best and try to revisit your first choice. Voila! The price has gone up. There are a number of obstacles you must pull through to get the right ticket at a justified price and at your convenience

Welcome to the world of increasingly data-driven Airline Revenue Management (ARM) in which dynamic ticket pricing is just one aspect. The science of adjusting fares dynamically is not a simple case of supply and demand. ARM is a complex pipeline with interdependent components like flight distance, competition, purchase date, seasonality, oil price, airline reputation, right down to the individual preferences of their passengers.

Why Price Optimization is critical for Airline Revenue Management?

If an airline wants to serve a new route, cater to a special event, or grow an existing market route, the questions faced are:Where does the analysis start?
How are the fares determined?
How are the existing air-fares performing?

Market price prediction – traditionally performed using a rules-based pricing tool by airlines, is an area that offers vast potential to improve revenue and remain competitive in today’s crowded market place.

How AI can help in Price Optimization with Market price prediction?

Airlines have always had ample volumes of data spanning multiple sectors-Customer, Operations, Airfares. Deep neural network based AI models can help build sophisticated price-optimization system by deriving:

– Flight routes in demand: Insights from the historical data

– Willingness to pay: Understand customer preferences and buying behavior ( e.g. when the customer is likely to pay the maximum price to buy the air ticket)

– Competitive airfare: Apache spark framework helps in performing real-time analysis on competitive market price.

Apart from Market price prediction, machine learning models can be applied for:Ancillary Price Optimization: Leveraging customer data, historical ancillary purchases, competitive pricing are few of the many factors involved. Hybrid recommendation systems developed based on multi-class classification models are good at recommending ancillary products for customers.
Customer Segmentation: Airlines can discover hidden patterns and design their advertisement & communication very precisely and in a personalized manner. Unsupervised learning algorithms are the best tools for this purpose.
Predictive Maintenance: Lack of timely maintenance of aircraft, can lead to incidents which can ground planes for extended periods. It also leads to reputation damage which affects customer satisfaction. A single-day of grounding aircraft costs millions of dollars. Hence, the industry is always on the lookout for better maintenance methods. Historical data along with the massive amount of data generated in real-time by the multiple sensors equipped in next-generation of aircraft engines can be used to develop algorithms which can help to make proactive decisions on maintenance schedules and ensure better aircraft Supervised learning algorithms are appropriate to handle this scenario.

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