Unlocking hidden revenue streams with market models
Each day, an airline transports tens of thousands of passengers on hundreds of flights. Often these are not straightforward point-to-point routes, with passengers requiring multiple connections. The airline can consider potentially hundreds of variables to price each of these journeys: demand, season, time of day, current events, global markets, and competitor airline activity to name…
Each day, airlines transport thousands of passengers across numerous flights, often involving multiple connections. The airline must consider hundreds of variables, such as demand, season, time of day, current events, global markets, and competitor activity, to price each journey. This intricate process must adapt quickly to the world's developments.
Generative AI-powered market models are emerging as a solution for managing such complex tasks in real-time. These deep learning models are trained on detailed numerical data and are capable of analyzing, simulating, and predicting intricate financial dynamics. Instead of depending on historical trends or fixed rules, the market model functions as an AI "brain," integrating various data sources to simulate different market conditions and make dynamic business decisions like pricing, inventory, or revenue management.
Dominic Kennedy, Virgin Atlantic's senior vice president of revenue management, sales, and e-commerce, explains that the model allows them to make more precise commercial decisions in real-time. It evaluates a wide range of inputs, including demand, capacity, and bookings, and assesses their positioning relative to competitors, market conditions, and other factors that significantly influence demand.
Written by urgent.news from MIT Technology Review's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
